style analysis research papers

Literary Focus

Roundtables

—Style Analysis—

When we refer to a writer's style, we mean the way authors or playwrights use various literary techniques to establish tone and convey theme.  To make this process as simple as possible for students, we limit our discussion to what we call the "four pillars" of style analysis:  diction, imagery, language, and syntax.  We'll look at each of these pillars individually in the sections below.

I.  Diction

To understand the significance of diction, or word choice, students first must differentiate between the denotation of a word and its connotation.  By denotation we mean the definition found in the dictionary; by connotation we mean the attitude or feelings associated with the word.  To make this difference clear to students, we provide three pairs of words that have similar denotations but very different connotations:

In the first example, if we refer to a dog as a "mutt," there is an element of disrespect in that designation, implying that the dog is of an inferior type.  In the second example, two groups could be engaged in the exact same behavior that shows they find something funny, but the group described as "giggling" seems young and immature compared to a group that is "laughing."  Boys and girls "giggle," whereas men and women "laugh."  In the final example, if a teacher is described as "strict," it implies that she maintains order and discipline in the classroom.  Even though a "strict" teacher may not be as much fun for students, the adjective carries an element of respect.  If a teacher is described as "rigid," however, she may be so disciplined in her approach that she is seen as closed-minded and inflexible, which are obviously negative attributes.

Once students have been introduced to the concept of diction, we analyze a short passage from a literary work they are reading.  For instance, in the opening description of Billy Pilgrim in Kurt Vonnegut's  Slaughterhouse-Five , the narrator tells us that "Billy has come unstuck in time."  When we ask students if the word "unstuck" has a positive or negative connotation, there is usually a pause.  On one hand, being "unstuck" implies liberation and freedom, words that have positive connotations, but to be "unstuck in time" also implies a helplessness and lack of control, which is reinforced by the word "spastic" in the following excerpt.

Similar to our interpretation of AP prompts, we encourage students to look at words as not necessarily positive  or negative, but as sometimes positive and  negative.  For Billy, becoming "unstuck in time" is a coping mechanism that helps him deal with the traumatic experiences in his life.  By taking a passive, fatalistic approach to life, Billy protects himself from feeling pain in any particular moment.  With that "freedom," however, comes a corresponding lack of control and disconnectedness from the reality of his own experience.  Moments are seen as transitory and impermanent, and the result is that Billy suffers from perpetual anxiety, or "stage fright," since he has no idea "what part of his life he is going to have to act in next."

The purpose of identifying tone is to determine a narrator's attitude towards a subject. So how does the narrator feel about Billy's being "spastic in time"?  As a temporary coping mechanism, it appears to be a successful strategy; as a way to live one's life, however, it appears to be an abject failure.  To be "spastic" is to lack control and agency over one's own life.  While we understand why Billy responds to trauma in this particular way, we should recognize that Vonnegut does not want us emulating Billy.  Our job over the course of the novel is to determine what we can learn from Billy's experience and find a better way to deal with life's difficulties on our own.

II.  Imagery

When we refer to an author or playwright's use of imagery, we mean descriptions that include any of the five senses:  sight, sound, smell,  touch, and taste.  Sensory details not only make the reader experience a scene more vividly, but they also serve to establish the narrator's tone, or attitude, towards the subject. 

In Leslie Marmon Silko's  Ceremony , we are introduced to Tayo, another character who suffers from traumatic experiences in World War II.  Silko uses imagery to immerse us in the opening scene so that we can vicariously see, hear, and feel what is going on inside Tayo's head as he lies sleeplessly on his bed after returning to the Laguna Pueblo reservation:

Visually, we can see the "old iron bed" that Tayo "tossed" restlessly upon, but the more dominant sensory detail is the "coiled springs" underneath the mattress that "kept squeaking even after he lay still again."  In many ways Tayo is like those "coiled springs" under constant tension.  The incessant squeaking triggers "humid dreams of black night," which combines a visual darkness with a tactile humidity that reminds him of the Pacific jungles that still haunt him.  From out of the darkness comes a series of "loud voices" that are figuratively "rollling him over and over again" as if he were "debris caught in a flood."  Silko makes us experience Tayo's hellish dreamscape through her use of vivid imagery—a disorienting combination of sight, sound, and touch that makes us intimately aware of Tayo's inner distress.  Tayo cannot control the shifts that occur inside his mind that combine his present reality with his traumatic past.  We know from Silko's use of imagery in the opening description that Tayo's life is in the balance, for he is currently helpless in confronting the confusion and despair that threaten to engulf him.

III.  Language

When the AP refers to language in its prompts, they mean figurative language.  To simplify this concept for students, we focus only on the three types of figurative language most often used in literature:  metaphors, symbols, and allusions.

When authors or playwrights use metaphoric language, they are comparing one thing in terms of another.  Similes are metaphors using "like" or "as" to make the comparison more explicit; personification is giving a non-human entity human characteristics.  An element in a literary work may be symbolic if it represents something larger than the element itself.  Allusions are indirect references to something outside the literary work for the purpose of comparison.  The four most common types of allusions that we discuss with students are literary, mythological, historical, and biblical.

In the final soliloquy of Macbeth , William Shakespeare uses all three types of figurative language to show Macbeth's hopelessness and despair after receiving word that Lady Macbeth has died,  most likely by suicide.  As Macbeth faces his own imminent death as he prepares to defend Dunsinane against his fellow countrymen who have rebelled against his tyrannous rule, he contemplates the absurdity and meaninglessness of life:

When Macbeth says, "Out, out, brief candle," he seems to welcome the thought of his own death, comparing life to a symbolic candle that burns itself down into nothingness.  Shakespeare has Macbeth follow this line by stating, "Life's but a walking shadow," which seems to be an allusion to Psalms 23:4 in the Old Testament:

Yea, though I walk through the valley of the shadow of death, I will fear no evil: for thou art with me; thy rod and thy staff they comfort me.

The irony of this biblical allusion is that instead of being comforted by the presence of God in the face of death, Macbeth finds himself utterly alone and in despair.  Macbeth's only solace is that perhaps there is no God nor any purpose in life.  Instead of a life of goodness and faith, Macbeth has embraced evil and a belief in "nothing."  The final metaphor that Macbeth uses is one of a "poor player," or actor, who "struts and frets his hour upon the stage / And then is heard no more."  By having Macbeth equate life to acting upon a stage, Shakespeare seems to imply that we all play roles in life, some that may not reflect who we really are.  In this final soliloquy, Shakespeare perhaps wants us to remember that Macbeth was once admired and respected for his courage and virtue.  Shakespeare suggests that Macbeth's life is a tragedy because he was once capable of goodness.  By seeing himself as an actor upon a stage, we imagine that the real Macbeth could have been someone far different from the villain he portrayed.  

IV.  Syntax

For most students, the most daunting and least utilized pillar of style analysis is syntax, which refers to the way that words are structured and organized in a text.  When we refer to syntax, we think of how sentences and paragraphs are built — which includes everything from word order and arrangement to verb tenses and punctuation.

An examination of a writer's syntax requires a subtle and nuanced reading of the text, so we advise students not to put too much pressure on themselves to include a syntactical analysis in their essays.  If they focus only on diction, imagery, and language, they should have plenty to discuss.  That being said, if they can include elements of syntax in their analysis, it will be impressive to the reader because it is so rarely done.  

The first thing we tell students when analyzing the syntax of a passage is to look for any structural elements that seem unusual or interesting.  For instance, in the final chapter of Julie Otsuka's  When the Emperor Was Divine , the narration shifts from the third-person limited perspective of the mother and her children that was used in the previous chapters to the first-person narration of the father in the final chapter. 

From what we know about the mild-mannered father from the previous chapters, we understand that he is in no way guilty of any of the crimes to which he now confesses.  In fact, the seemingly never-ending laundry list of supposed crimes he has committed embodies all the accusations that were made against Japanese-Americans in World War II.  By shifting to first-person narration, Otsuka gives us an opportunity to enter into the psyche of the falsely accused.  The short, terse sentences reveal the pain and bitterness of being considered disloyal simply because of one's racial identity.  Furthermore, by having the first-person narrator address the abstract "you" in this final chapter, Otsuka makes all Americans who failed to intervene on behalf of Japanese-Americans complicit in the crime of their internment.  Otsuka's shift in style also represents a shift in tone.  The readers being addressed as "you" are now put in a defensive position, feeling attacked for things for which we may not feel personally responsible.  Now we know, Otsuka seems to suggest, how it feels to be falsely accused.  The father also reveals his anger and frustration when he sarcastically repeats, "You were right.  You were always right."  Despite his innocence, the father knows that he will always be viewed as suspicious, regardless of what he says or does.

When first introducing the "four pillars" of style analysis to students, we focus on one element at a time so as not to overwhelm them.  Once students are comfortable, however, we have them work in small groups to work on all four elements at once, as they do when analyzing the following passage from our unit on Margaret Atwood's  The Handmaid's Tale :

Once students understand the fundamentals of style analysis and how diction, imagery, language, and syntax help authors establish tone and convey theme, they are ready to write an AP Passage Analysis essay, which is the ultimate assessment of a student's ability to read a passage closely to determine the author's intent.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Academic Writing Style
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
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• Further Readings
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Academic writing refers to a style of expression that researchers use to define the intellectual boundaries of their disciplines and specific areas of expertise. Characteristics of academic writing include a formal tone, use of the third-person rather than first-person perspective (usually), a clear focus on the research problem under investigation, and precise word choice. Like specialist languages adopted in other professions, such as, law or medicine, academic writing is designed to convey agreed meaning about complex ideas or concepts within a community of scholarly experts and practitioners.

Academic Writing. Writing Center. Colorado Technical College; Hartley, James. Academic Writing and Publishing: A Practical Guide . New York: Routledge, 2008; Ezza, El-Sadig Y. and Touria Drid. T eaching Academic Writing as a Discipline-Specific Skill in Higher Education . Hershey, PA: IGI Global, 2020.

Importance of Good Academic Writing

The accepted form of academic writing in the social sciences can vary considerable depending on the methodological framework and the intended audience. However, most college-level research papers require careful attention to the following stylistic elements:

I.  The Big Picture Unlike creative or journalistic writing, the overall structure of academic writing is formal and logical. It must be cohesive and possess a logically organized flow of ideas; this means that the various parts are connected to form a unified whole. There should be narrative links between sentences and paragraphs so that the reader is able to follow your argument. The introduction should include a description of how the rest of the paper is organized and all sources are properly cited throughout the paper.

II.  Tone The overall tone refers to the attitude conveyed in a piece of writing. Throughout your paper, it is important that you present the arguments of others fairly and with an appropriate narrative tone. When presenting a position or argument that you disagree with, describe this argument accurately and without loaded or biased language. In academic writing, the author is expected to investigate the research problem from an authoritative point of view. You should, therefore, state the strengths of your arguments confidently, using language that is neutral, not confrontational or dismissive.

III.  Diction Diction refers to the choice of words you use. Awareness of the words you use is important because words that have almost the same denotation [dictionary definition] can have very different connotations [implied meanings]. This is particularly true in academic writing because words and terminology can evolve a nuanced meaning that describes a particular idea, concept, or phenomenon derived from the epistemological culture of that discipline [e.g., the concept of rational choice in political science]. Therefore, use concrete words [not general] that convey a specific meaning. If this cannot be done without confusing the reader, then you need to explain what you mean within the context of how that word or phrase is used within a discipline.

IV.  Language The investigation of research problems in the social sciences is often complex and multi- dimensional . Therefore, it is important that you use unambiguous language. Well-structured paragraphs and clear topic sentences enable a reader to follow your line of thinking without difficulty. Your language should be concise, formal, and express precisely what you want it to mean. Do not use vague expressions that are not specific or precise enough for the reader to derive exact meaning ["they," "we," "people," "the organization," etc.], abbreviations like 'i.e.'  ["in other words"], 'e.g.' ["for example"], or 'a.k.a.' ["also known as"], and the use of unspecific determinate words ["super," "very," "incredible," "huge," etc.].

V.  Punctuation Scholars rely on precise words and language to establish the narrative tone of their work and, therefore, punctuation marks are used very deliberately. For example, exclamation points are rarely used to express a heightened tone because it can come across as unsophisticated or over-excited. Dashes should be limited to the insertion of an explanatory comment in a sentence, while hyphens should be limited to connecting prefixes to words [e.g., multi-disciplinary] or when forming compound phrases [e.g., commander-in-chief]. Finally, understand that semi-colons represent a pause that is longer than a comma, but shorter than a period in a sentence. In general, there are four grammatical uses of semi-colons: when a second clause expands or explains the first clause; to describe a sequence of actions or different aspects of the same topic; placed before clauses which begin with "nevertheless", "therefore", "even so," and "for instance”; and, to mark off a series of phrases or clauses which contain commas. If you are not confident about when to use semi-colons [and most of the time, they are not required for proper punctuation], rewrite using shorter sentences or revise the paragraph.

VI.  Academic Conventions Among the most important rules and principles of academic engagement of a writing is citing sources in the body of your paper and providing a list of references as either footnotes or endnotes. The academic convention of citing sources facilitates processes of intellectual discovery, critical thinking, and applying a deliberate method of navigating through the scholarly landscape by tracking how cited works are propagated by scholars over time . Aside from citing sources, other academic conventions to follow include the appropriate use of headings and subheadings, properly spelling out acronyms when first used in the text, avoiding slang or colloquial language, avoiding emotive language or unsupported declarative statements, avoiding contractions [e.g., isn't], and using first person and second person pronouns only when necessary.

VII.  Evidence-Based Reasoning Assignments often ask you to express your own point of view about the research problem. However, what is valued in academic writing is that statements are based on evidence-based reasoning. This refers to possessing a clear understanding of the pertinent body of knowledge and academic debates that exist within, and often external to, your discipline concerning the topic. You need to support your arguments with evidence from scholarly [i.e., academic or peer-reviewed] sources. It should be an objective stance presented as a logical argument; the quality of the evidence you cite will determine the strength of your argument. The objective is to convince the reader of the validity of your thoughts through a well-documented, coherent, and logically structured piece of writing. This is particularly important when proposing solutions to problems or delineating recommended courses of action.

VIII.  Thesis-Driven Academic writing is “thesis-driven,” meaning that the starting point is a particular perspective, idea, or position applied to the chosen topic of investigation, such as, establishing, proving, or disproving solutions to the questions applied to investigating the research problem. Note that a problem statement without the research questions does not qualify as academic writing because simply identifying the research problem does not establish for the reader how you will contribute to solving the problem, what aspects you believe are most critical, or suggest a method for gathering information or data to better understand the problem.

IX.  Complexity and Higher-Order Thinking Academic writing addresses complex issues that require higher-order thinking skills applied to understanding the research problem [e.g., critical, reflective, logical, and creative thinking as opposed to, for example, descriptive or prescriptive thinking]. Higher-order thinking skills include cognitive processes that are used to comprehend, solve problems, and express concepts or that describe abstract ideas that cannot be easily acted out, pointed to, or shown with images. Think of your writing this way: One of the most important attributes of a good teacher is the ability to explain complexity in a way that is understandable and relatable to the topic being presented during class. This is also one of the main functions of academic writing--examining and explaining the significance of complex ideas as clearly as possible.  As a writer, you must adopt the role of a good teacher by summarizing complex information into a well-organized synthesis of ideas, concepts, and recommendations that contribute to a better understanding of the research problem.

Academic Writing. Writing Center. Colorado Technical College; Hartley, James. Academic Writing and Publishing: A Practical Guide . New York: Routledge, 2008; Murray, Rowena  and Sarah Moore. The Handbook of Academic Writing: A Fresh Approach . New York: Open University Press, 2006; Johnson, Roy. Improve Your Writing Skills . Manchester, UK: Clifton Press, 1995; Nygaard, Lynn P. Writing for Scholars: A Practical Guide to Making Sense and Being Heard . Second edition. Los Angeles, CA: Sage Publications, 2015; Silvia, Paul J. How to Write a Lot: A Practical Guide to Productive Academic Writing . Washington, DC: American Psychological Association, 2007; Style, Diction, Tone, and Voice. Writing Center, Wheaton College; Sword, Helen. Stylish Academic Writing . Cambridge, MA: Harvard University Press, 2012.

Strategies for...

Understanding Academic Writing and Its Jargon

The very definition of research jargon is language specific to a particular community of practitioner-researchers . Therefore, in modern university life, jargon represents the specific language and meaning assigned to words and phrases specific to a discipline or area of study. For example, the idea of being rational may hold the same general meaning in both political science and psychology, but its application to understanding and explaining phenomena within the research domain of a each discipline may have subtle differences based upon how scholars in that discipline apply the concept to the theories and practice of their work.

Given this, it is important that specialist terminology [i.e., jargon] must be used accurately and applied under the appropriate conditions . Subject-specific dictionaries are the best places to confirm the meaning of terms within the context of a specific discipline. These can be found by either searching in the USC Libraries catalog by entering the disciplinary and the word dictionary [e.g., sociology and dictionary] or using a database such as Credo Reference [a curated collection of subject encyclopedias, dictionaries, handbooks, guides from highly regarded publishers] . It is appropriate for you to use specialist language within your field of study, but you should avoid using such language when writing for non-academic or general audiences.

Problems with Opaque Writing

A common criticism of scholars is that they can utilize needlessly complex syntax or overly expansive vocabulary that is impenetrable or not well-defined. When writing, avoid problems associated with opaque writing by keeping in mind the following:

1.   Excessive use of specialized terminology . Yes, it is appropriate for you to use specialist language and a formal style of expression in academic writing, but it does not mean using "big words" just for the sake of doing so. Overuse of complex or obscure words or writing complicated sentence constructions gives readers the impression that your paper is more about style than substance; it leads the reader to question if you really know what you are talking about. Focus on creating clear, concise, and elegant prose that minimizes reliance on specialized terminology.

2.   Inappropriate use of specialized terminology . Because you are dealing with concepts, research, and data within your discipline, you need to use the technical language appropriate to that area of study. However, nothing will undermine the validity of your study quicker than the inappropriate application of a term or concept. Avoid using terms whose meaning you are unsure of--do not just guess or assume! Consult the meaning of terms in specialized, discipline-specific dictionaries by searching the USC Libraries catalog or the Credo Reference database [see above].

Additional Problems to Avoid

In addition to understanding the use of specialized language, there are other aspects of academic writing in the social sciences that you should be aware of. These problems include:

• Personal nouns . Excessive use of personal nouns [e.g., I, me, you, us] may lead the reader to believe the study was overly subjective. These words can be interpreted as being used only to avoid presenting empirical evidence about the research problem. Limit the use of personal nouns to descriptions of things you actually did [e.g., "I interviewed ten teachers about classroom management techniques..."]. Note that personal nouns are generally found in the discussion section of a paper because this is where you as the author/researcher interpret and describe your work.
• Directives . Avoid directives that demand the reader to "do this" or "do that." Directives should be framed as evidence-based recommendations or goals leading to specific outcomes. Note that an exception to this can be found in various forms of action research that involve evidence-based advocacy for social justice or transformative change. Within this area of the social sciences, authors may offer directives for action in a declarative tone of urgency.
• Informal, conversational tone using slang and idioms . Academic writing relies on excellent grammar and precise word structure. Your narrative should not include regional dialects or slang terms because they can be open to interpretation. Your writing should be direct and concise using standard English.
• Wordiness. Focus on being concise, straightforward, and developing a narrative that does not have confusing language . By doing so, you  help eliminate the possibility of the reader misinterpreting the design and purpose of your study.
• Vague expressions (e.g., "they," "we," "people," "the company," "that area," etc.). Being concise in your writing also includes avoiding vague references to persons, places, or things. While proofreading your paper, be sure to look for and edit any vague or imprecise statements that lack context or specificity.
• Numbered lists and bulleted items . The use of bulleted items or lists should be used only if the narrative dictates a need for clarity. For example, it is fine to state, "The four main problems with hedge funds are:" and then list them as 1, 2, 3, 4. However, in academic writing, this must then be followed by detailed explanation and analysis of each item. Given this, the question you should ask yourself while proofreading is: why begin with a list in the first place rather than just starting with systematic analysis of each item arranged in separate paragraphs? Also, be careful using numbers because they can imply a ranked order of priority or importance. If none exists, use bullets and avoid checkmarks or other symbols.
• Descriptive writing . Describing a research problem is an important means of contextualizing a study. In fact, some description or background information may be needed because you can not assume the reader knows the key aspects of the topic. However, the content of your paper should focus on methodology, the analysis and interpretation of findings, and their implications as they apply to the research problem rather than background information and descriptions of tangential issues.
• Personal experience. Drawing upon personal experience [e.g., traveling abroad; caring for someone with Alzheimer's disease] can be an effective way of introducing the research problem or engaging your readers in understanding its significance. Use personal experience only as an example, though, because academic writing relies on evidence-based research. To do otherwise is simply story-telling.

NOTE:   Rules concerning excellent grammar and precise word structure do not apply when quoting someone.  A quote should be inserted in the text of your paper exactly as it was stated. If the quote is especially vague or hard to understand, consider paraphrasing it or using a different quote to convey the same meaning. Consider inserting the term "sic" in brackets after the quoted text to indicate that the quotation has been transcribed exactly as found in the original source, but the source had grammar, spelling, or other errors. The adverb sic informs the reader that the errors are not yours.

Academic Writing. The Writing Lab and The OWL. Purdue University; Academic Writing Style. First-Year Seminar Handbook. Mercer University; Bem, Daryl J. Writing the Empirical Journal Article. Cornell University; College Writing. The Writing Center. University of North Carolina; Murray, Rowena  and Sarah Moore. The Handbook of Academic Writing: A Fresh Approach . New York: Open University Press, 2006; Johnson, Eileen S. “Action Research.” In Oxford Research Encyclopedia of Education . Edited by George W. Noblit and Joseph R. Neikirk. (New York: Oxford University Press, 2020); Oppenheimer, Daniel M. "Consequences of Erudite Vernacular Utilized Irrespective of Necessity: Problems with Using Long Words Needlessly." Applied Cognitive Psychology 20 (2006): 139-156; Ezza, El-Sadig Y. and Touria Drid. T eaching Academic Writing as a Discipline-Specific Skill in Higher Education . Hershey, PA: IGI Global, 2020; Pernawan, Ari. Common Flaws in Students' Research Proposals. English Education Department. Yogyakarta State University; Style. College Writing. The Writing Center. University of North Carolina; Invention: Five Qualities of Good Writing. The Reading/Writing Center. Hunter College; Sword, Helen. Stylish Academic Writing . Cambridge, MA: Harvard University Press, 2012; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College.

Structure and Writing Style

I. Improving Academic Writing

To improve your academic writing skills, you should focus your efforts on three key areas: 1.   Clear Writing . The act of thinking about precedes the process of writing about. Good writers spend sufficient time distilling information and reviewing major points from the literature they have reviewed before creating their work. Writing detailed outlines can help you clearly organize your thoughts. Effective academic writing begins with solid planning, so manage your time carefully. 2.  Excellent Grammar . Needless to say, English grammar can be difficult and complex; even the best scholars take many years before they have a command of the major points of good grammar. Take the time to learn the major and minor points of good grammar. Spend time practicing writing and seek detailed feedback from professors. Take advantage of the Writing Center on campus if you need help. Proper punctuation and good proofreading skills can significantly improve academic writing [see sub-tab for proofreading you paper ].

Refer to these three basic resources to help your grammar and writing skills:

• A good writing reference book, such as, Strunk and White’s book, The Elements of Style or the St. Martin's Handbook ;
• A college-level dictionary, such as, Merriam-Webster's Collegiate Dictionary ;
• The latest edition of Roget's Thesaurus in Dictionary Form .

3.  Consistent Stylistic Approach . Whether your professor expresses a preference to use MLA, APA or the Chicago Manual of Style or not, choose one style manual and stick to it. Each of these style manuals provide rules on how to write out numbers, references, citations, footnotes, and lists. Consistent adherence to a style of writing helps with the narrative flow of your paper and improves its readability. Note that some disciplines require a particular style [e.g., education uses APA] so as you write more papers within your major, your familiarity with it will improve.

II. Evaluating Quality of Writing

A useful approach for evaluating the quality of your academic writing is to consider the following issues from the perspective of the reader. While proofreading your final draft, critically assess the following elements in your writing.

• It is shaped around one clear research problem, and it explains what that problem is from the outset.
• Your paper tells the reader why the problem is important and why people should know about it.
• You have accurately and thoroughly informed the reader what has already been published about this problem or others related to it and noted important gaps in the research.
• You have provided evidence to support your argument that the reader finds convincing.
• The paper includes a description of how and why particular evidence was collected and analyzed, and why specific theoretical arguments or concepts were used.
• The paper is made up of paragraphs, each containing only one controlling idea.
• You indicate how each section of the paper addresses the research problem.
• You have considered counter-arguments or counter-examples where they are relevant.
• Arguments, evidence, and their significance have been presented in the conclusion.
• Limitations of your research have been explained as evidence of the potential need for further study.
• The narrative flows in a clear, accurate, and well-organized way.

Boscoloa, Pietro, Barbara Arféb, and Mara Quarisaa. “Improving the Quality of Students' Academic Writing: An Intervention Study.” Studies in Higher Education 32 (August 2007): 419-438; Academic Writing. The Writing Lab and The OWL. Purdue University; Academic Writing Style. First-Year Seminar Handbook. Mercer University; Bem, Daryl J. Writing the Empirical Journal Article. Cornell University; Candlin, Christopher. Academic Writing Step-By-Step: A Research-based Approach . Bristol, CT: Equinox Publishing Ltd., 2016; College Writing. The Writing Center. University of North Carolina; Style . College Writing. The Writing Center. University of North Carolina; Invention: Five Qualities of Good Writing. The Reading/Writing Center. Hunter College; Sword, Helen. Stylish Academic Writing . Cambridge, MA: Harvard University Press, 2012; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Considering the Passive Voice in Academic Writing

In the English language, we are able to construct sentences in the following way: 1.  "The policies of Congress caused the economic crisis." 2.  "The economic crisis was caused by the policies of Congress."

The decision about which sentence to use is governed by whether you want to focus on “Congress” and what they did, or on “the economic crisis” and what caused it. This choice in focus is achieved with the use of either the active or the passive voice. When you want your readers to focus on the "doer" of an action, you can make the "doer"' the subject of the sentence and use the active form of the verb. When you want readers to focus on the person, place, or thing affected by the action, or the action itself, you can make the effect or the action the subject of the sentence by using the passive form of the verb.

Often in academic writing, scholars don't want to focus on who is doing an action, but on who is receiving or experiencing the consequences of that action. The passive voice is useful in academic writing because it allows writers to highlight the most important participants or events within sentences by placing them at the beginning of the sentence.

Use the passive voice when:

• You want to focus on the person, place, or thing affected by the action, or the action itself;
• It is not important who or what did the action;
• You want to be impersonal or more formal.

Form the passive voice by:

• Turning the object of the active sentence into the subject of the passive sentence.
• Changing the verb to a passive form by adding the appropriate form of the verb "to be" and the past participle of the main verb.

NOTE: Consult with your professor about using the passive voice before submitting your research paper. Some strongly discourage its use!

Active and Passive Voice. The Writing Lab and The OWL. Purdue University; Diefenbach, Paul. Future of Digital Media Syllabus. Drexel University; Passive Voice. The Writing Center. University of North Carolina.  

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13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

• 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

• 📝 Analysis Example
• 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

• Why was the research carried out?
• What gaps does it try to fill, or what controversies to settle?
• How does the study contribute to its field?
• Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

• Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
• Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
• Is the study biased in any way?
• What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

• What are the major findings of the study?
• Did the author present the results clearly and unambiguously?
• Are the findings statistically significant ?
• Does the author provide sufficient information on the validity and reliability of the results?
• Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

• What findings did the authors use to support their conclusions?
• How do the researchers’ conclusions compare to other studies’ findings?
• How does this study contribute to its field?
• What future research directions do the authors suggest?
• What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

• The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
• The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
• Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

• The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
• Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
• The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

• Analyze whether the study’s purpose is significant and relevant.
• Examine whether the chosen methodology allows for answering the research questions.
• Evaluate how the authors presented the results.
• Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

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Survey on sentiment analysis: evolution of research methods and topics

• Published: 06 January 2023
• Volume 56 , pages 8469–8510, ( 2023 )

Cite this article

• Jingfeng Cui   ORCID: orcid.org/0000-0001-8306-0727 1 , 2 ,
• Zhaoxia Wang   ORCID: orcid.org/0000-0001-7674-5488 3 ,
• Seng-Beng Ho   ORCID: orcid.org/0000-0003-4839-1509 1 &
• Erik Cambria   ORCID: orcid.org/0000-0002-3030-1280 4  

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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Avoid common mistakes on your manuscript.

1 Introduction

Web 2.0 has driven the proliferation of user-generated content on the Internet. This content is closely related to the lives, emotions, and opinions of users. Therefore, analysis of this user-generated data is beneficial for monitoring public opinion and assisting in making decisions. Sentiment analysis, as one of the most popular applications of text-based analytics, can be used to mine people’s attitudes, emotions, appraisals, and opinions about issues, entities, topics, events, and products (Cambria et al. 2022a , b , c , d ; Injadat et al. 2016 ; Jiang et al. 2017 ; Liang et al. 2022 ; Oueslati et al. 2020 ; Piryani et al. 2017 ). Sentiment analysis can help us interpret emotions in unstructured texts as positive, negative, or neutral, and even calculate how strong or weak the emotions are. Today, sentiment analysis is widely used in various fields, such as business, finance, politics, education, and services. This analytical technique has gained broad acceptance not only among researchers but also among governments, institutions, and companies (Khatua et al. 2020 ; Liu et al. 2012 ; Sánchez-Rada and Iglesias 2019 ; Wang et al. 2020b ). It helps policy leaders, businessmen, and service people make better decisions.

The majority of user-generated content data is unstructured text, which increases the great difficulty of sentiment analysis. Since 2000, researchers have been exploring techniques and methods to enhance the accuracy of such analysis. The popularity of social media platforms has brought people around the world closer together. With the continuous advancement of technology, the research topics, application fields, and core methods and technologies of sentiment analysis are also constantly changing.

Comparing and analyzing papers from specific disciplines can help researchers gain a comprehensive understanding of the field. There have been many surveys on sentiment analysis (Nair et al. 2019 ; Obiedat et al. 2021 ; Raghuvanshi and Patil 2016 ). However, there is a lack of adequate discussion on the connections between research methods and topics in the field, as well as on their evolution over time. In 1983, Callon et al. proposed co-word analysis (Callon et al. 1983 ). It can effectively reflect the correlation strength of information items in text data. Co-word analysis based on the frequency of co-occurrence of keywords used to describe papers can reveal the core contents of the research in specific fields. An evolutionary analysis of the associations between core contents is helpful for a comprehensive understanding of the research hotspots and frontiers in the field (Deng et al. 2021 ). It can provide guidance for researchers, especially those who are new to the field, and help them determine research directions, avoid repetitive research, and better discover and grasp the research trends in this field (Wang et al. 2012 ). To fill in the gap in existing research, we conduct keyword co-occurrence analysis and evolution analysis with informetric tools to explore the research hotspots and trends of sentiment analysis.

The main contributions of this survey are as follows:

Using keyword co-occurrence analysis and the informetric tools, the paper presents a survey on sentiment analysis, explores and discovers useful information.

A keyword co-occurrence network is constructed by combining the paper title, abstract, and author keywords. Through the keyword co-occurrence network and community detection algorithm, the research methods and topics in the field of sentiment analysis, along with their evolution in the past two decades, are discussed.

The paper summarizes the research hotspots and trends in sentiment analysis. It also highlights practical implications and technical directions.

The remainder of this paper is organized as follows: In Sect.  2 , we summarize and analyze the existing surveys on sentiment analysis and present the research purpose and methodologies of this paper. Section  3 details the survey methodology, including the collection and processing of scientific publications, visualization, and analysis using different methods and tools. In Sect.  4 , we analyze the results obtained from the keyword co-occurrence analysis and evolution analysis, along with the research hotspots and trends in sentiment analysis identified through the analysis results. Finally, in Sect.  5 , we summarize the research conclusions as well as the practical implications and technical directions of sentiment analysis. We also clarify the limitations of this paper and make suggestions for future work.

2 Existing surveys on sentiment analysis

Sentiment analysis is a concept encompassing many tasks, such as sentiment extraction, sentiment classification, opinion summarization, review analysis, sarcasm detection or emotion detection, etc. Since the 2000s, sentiment analysis has become a popular research field in natural language processing (Hussein 2018 ). In the existing surveys, the researchers mainly conducted specific analyses of the tasks, technologies, methods, analysis granularity, and application fields involved in the sentiment analysis process.

2.1 Surveys on contents and topics of sentiment analysis

When research on sentiment analysis was still in its infancy, the contents and topics of surveys mainly focused on sentiment analysis tasks, analysis granularity, and application areas. Kumer et al. reviewed the basic terms, tasks, and levels of granularity related to sentiment analysis (Kumar and Sebastian 2012 ). They also discussed some key feature selection techniques and the applications of sentiment analysis in business, politics, recommender systems and other fields. Nassirtoussi et al. explored the application of sentiment analysis in market prediction (Nassirtoussi et al. 2014 ). Medhat et al. analyzed the improvement of the algorithms proposed in 2010–2013 and their application fields (Medhat et al. 2014 ). Ravi et al. analyzed the papers related to opinion mining and sentiment analysis from 2002 to 2015. Their study mainly discussed the necessary tasks, methods, applications, and unsolved problems in the field of sentiment analysis (Ravi and Ravi 2015 ).

Existing surveys of the applications of sentiment analysis have focused more on the domains of market research, medicine, and social media in recent years. Rambocas et al. examined the application of sentiment analysis in marketing research from three main perspectives, including the unit of analysis, sampling design, and methods used in sentiment detection and statistical analysis (Rambocas and Pacheco 2018 ). Cheng et al. summarized techniques based on semantic, sentiment, and event extraction, as well as hybrid methods employed in stock forecasting (Cheng et al. 2022 ). Yue et al. categorized and compared a large number of techniques and approaches in the social media domain. That study also introduced different types of data and advanced research tools, and discussed their limitations (Yue et al. 2019 ). In the context of the COVID-19 epidemic, Alamoodi et al. reviewed and analyzed articles on the occurrence of different types of infectious diseases in the past 10 years. They reviewed the applications of sentiment analysis from the identified 28 articles, summarizing the adopted techniques such as dictionary-based models, machine learning models, and mixed models (Alamoodi et al. 2021b ); Alamoodi et al. also conducted a review of the applications of sentiment analysis for vaccine hesitancy (Alamoodi et al. 2021a ). Researchers also reviewed the application of sentiment analysis in the fields of election prediction (Brito et al. 2021 ), education (Kastrati et al. 2021 ; Zhou and Ye 2020 ) and service industries (Adak et al. 2022 ).

Quite a number of research works investigated sentiment analysis works in non-English languages. Sentiment analysis in Chinese (Peng et al. 2017 ), Arabic (Al-Ayyoub et al. 2019 ; Boudad et al. 2018 ; Nassif et al. 2021 ; Oueslati et al. 2020 ), Urdu (Khattak et al. 2021 ), Spanish (Angel et al. 2021 ), and Portuguese (Pereira 2021 ) were conducted. They mainly reviewed the classification frameworks of the sentiment analysis process, supported language resources (dictionaries, natural language processing tools, corpora, ontologies, etc.), and deep learning models used (CNN, RNN, and transfer learning) for each of the languages involved.

2.2 Surveys on methods of sentiment analysis

Before machine learning technology became mature, researchers were particularly concerned about feature extraction methods. For example, Feldman summarized methods for extracting preferred entities from indirect opinions and methods for dictionary acquisition (Feldman 2013 ). Asghar et al. reviewed the natural language processing techniques for extracting features based on part of speech and term position; statistical techniques for extracting features based on word frequency and decision tree model; and techniques for combining part of speech tagging, syntactic feature analysis, and dictionaries (Asghar et al. 2014 ). Koto et al. discussed the best features for Twitter sentiment analysis prior to 2014 by comparing 9 feature sets (Koto and Adriani 2015 ). They found that the current best features for sentiment analysis of Twitter texts are AFINN (a list of English terms used for sentiment analysis manually rated by Finn Årup Nielsen) (Nielsen 2011 ) and Senti-Strength (Thelwall et al. 2012 ). Taboada sorted out the characteristics of words, phrases, and sentence patterns in sentiment analysis from the perspective of linguistics (Taboada 2016 ). Besides, Schouten and Frasinar conducted a comprehensive and in-depth critical evaluation of 15 sentiment analysis web tools (Schouten and Frasincar 2015 ). Medhat et al. ( 2014 ) and Ravi et al. (Ravi and Ravi 2015 ) also analyzed the early algorithms for sentiment analysis.

In the study by Schouten et al., the authors focused on aspect-level sentiment analysis, combing the techniques of aspect-level sentiment analysis before 2014, such as frequency-based, syntax-based, supervised machine learning, unsupervised machine learning, and hybrid approaches. They concluded that the latest technology was moving beyond the early stages (Schouten and Frasincar 2015 ). As research into sentiment analysis became more and more popular and there was important progress made in the development of deep learning technologies, researchers started to pay more attention to the techniques and methods of sentiment analysis. Deep learning methods in particular became the focus of discussions among researchers.

Prabha et al. analyzed various deep learning methods used in different applications at the level of sentence and aspect/object sentiment analysis, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) (Prabha and Srikanth 2019 ). They discussed the advantages and disadvantages of these methods and their performance parameters. Ain et al. introduced deep learning techniques such as Deep Neural Network (DNN), CNN and Deep Belief Network (DBN) to solve sentiment analysis tasks like sentiment classification, cross-lingual problems, and product review analysis (Ain et al. 2017 ). Zhang et al. investigated deep learning and machine learning techniques for sentiment analysis in the contexts of aspect extraction and categorization, opinion expression extraction, opinion holder extraction, sarcasm analysis, multimodal data, etc. (Zhang et al. 2018 ). Habimana et al. compared the performance of deep learning methods on specific datasets and proposed that performance could be improved using models including Bidirectional Encoder Representations from Transformers (BERT), sentiment-specific word embedding models, cognitive-based attention models, and commonsense knowledge (Habimana et al. 2020 ). Wang et al. reviewed and discussed existing analytical models for sentiment classification and proposed a computational emotion-sensing model (Wang et al. 2020b ).

Some researchers also discussed web tools (Zucco et al. 2020 ), fuzzy logic algorithms (Serrano-Guerrero et al. 2021 ), transformer models (Acheampong et al. 2021 ), and sequential transfer learning (Chan et al. 2022 ) for sentiment analysis.

2.3 Overall survey methodology

With the increase in the popularity of sentiment analysis research, more related research results began to accumulate. Researchers needed to systematically organize and analyze results from a large number of publications to perform literature reviews. They used different survey methodologies to conduct surveys of a large number of papers.

Content analysis is a powerful approach to characterizing the contents of each study by carefully reading its content and manually identifying, coding, and organizing key information in it. A literature review is formed as a result of the repeated use of this approach (Elo and Kyngäs 2008 ; Stemler 2000 ). Content analysis has been used for different studies and systematic reviews (Qazi et al. 2015 , 2017 ). For example, Birjali et al. have studied the most commonly used classification techniques in sentiment analysis from a large amount of literature and introduced the application areas and sentiment classification processes, including preprocessing and feature selection (Birjali et al. 2021 ). They conducted a comprehensive analysis of the papers, discovering that supervised machine learning algorithms are the most commonly used techniques in the field. A complete review of methods and evaluation for sentiment analysis tasks and their applications was conducted by Wankhade et al. ( 2022 ). They compared the strengths and weaknesses of the methods, and discussed the future challenges of sentiment analysis in terms of both the methods and the forms of the data. Although this method can review the research contents and penetrate into the cores of the papers most systematically, it requires a considerable amount of manpower and time for in-depth literature reading.

The systematic literature review guideline proposed by Kitchenham and Charters has gradually attracted the attention of researchers (Kitchenham 2004 ; Kitchenham and Charters 2007 ; Sarsam et al. 2020 ). This review process is divided into six stages: research question definition, search strategy formulation, inclusion and exclusion criteria definition, quality assessment, data extraction, and data synthesis. Researchers can eliminate a large number of retrieved papers by using this standard process and finally conducting further analysis and research on a small number of papers. Kumar et al. reviewed context-based sentiment analysis in social multimedia between 2006 and 2018. From the 573 papers retrieved in the initial search, they finally selected 37 papers to use in discussing sentiment analysis techniques (Kumar and Garg 2020 ). This approach was also used by Kumar et al. in their research on sentiment analysis on Twitter using soft computing techniques. They selected 60 articles out of 502 for follow-up analysis (Kumar and Jaiswal 2020 ). Zunic et al. selected 86 papers from 299 papers retrieved in the period 2011–2019 to discuss the application of sentiment analysis techniques in the field of health and well-being (Zunic et al. 2020 ); Ligthart et al. followed Kitchenham’s guideline and identified 14 secondary studies. They provided an overview of specific sentiment analysis tasks and of the features and methods required for different tasks (Ligthart et al. 2021 ). Obiedat (Obiedat et al. 2021 ), Angel (Angel et al. 2021 ) and Lin (Lin et al. 2022 ) also all followed this guideline to select literature for further analysis. This method can reduce the amount of literature that requires in-depth reading, but in the case of a large amount of literature, more effort is still required to search and screen the material than in traditional literature review methods (Kitchenham and Charters 2007 ).

There are also a few authors who have used informetric methods to review papers. Piryani et al. conducted an informetric analysis of research on opinion mining and sentiment analysis from 2000 to 2015 (Piryani et al. 2017 ). The authors used social network analysis, literature co-citation analysis, and other methods in the paper. They analyzed publication growth rates; the most productive countries, institutions, journals, and authors; and topic density maps and keyword bursts, among other elements. To a certain extent, they interpreted core authors, core papers, areas of research focus in this field, and the current state of national cooperation. In order to explore the application of sentiment analysis in building smart societies, Verma collected 353 papers published between 2010 and 2021 (Verma 2022 ). Using a topic analysis perspective combined with the Louvain algorithm, the author identified four sub-topics in the research field. Similarly, Mantyla et al. employed LDA techniques and manual classification to explore the topic structures of sentiment analysis articles (Mäntylä et al. 2018 ). The informetric methods use natural language processing technologies to intuitively conduct topic mining and analysis of a large number of papers. Through topic clustering, the literature is organized and analyzed, which reduces the time researchers spend on reading the literature in depth. These methods are suitable for exploring research topics and trends in the field.

2.4 Summary of advantages and disadvantages of the existing surveys

In the following, we discuss the advantages and disadvantages of the existing surveys from a number of different points of view.

2.4.1 From the point of view of the contents and topics of sentiment analysis

As summarized in Table 1 , the researchers organized the literature and conducted depth investigations of the contents and topics of sentiment analysis. They reviewed the tasks of sentiment analysis (e.g., different text granularity, opinion mining, spam review detection, and emotion detection), the application areas of sentiment analysis (e.g. market, medicine, social media, and election prediction), and different languages for sentiment analysis, such as Chinese, Spanish, and Arabic (Adak et al. 2022 ; Al-Ayyoub et al. 2019 ; Alamoodi et al. ( 2021a , b ); Alonso et al. 2021 ; Angel et al. 2021 ; Boudad et al. 2018 ; Brito et al. 2021 ; Cheng et al. 2022 ; Hussain et al. 2019 ; Kastrati et al. 2021 ; Khattak et al. 2021 ; Koto and Adriani 2015 ; Kumar and Sebastian 2012 ; Ligthart et al. 2021 ; Medhat et al. 2014 ; Nassif et al. 2021 ; Nassirtoussi et al. 2014 ; Oueslati et al. 2020 ; Peng et al. 2017 ; Pereira 2021 ; Rambocas and Pacheco 2018 ; Ravi and Ravi 2015 ; Schouten and Frasincar 2015 ; Sharma and Jain 2020 ; Yue et al. 2019 ; Zhou and Ye 2020 ). They summarized the methods and application prospects of sentiment analysis under different contents and topics. As the field has grown, new topics have emerged, and knowledge from other fields has been gradually integrated into it. In recent years, the popularity of social media has aroused increasing interest in sentiment analysis research, and the number of papers published, especially those related to different topics of sentiment analysis, has grown rapidly. However, the existing surveys cover a short time range, and there has not been a survey dedicated to the evolution of research contents or topics of sentiment analysis. There have also been few survey works analyzing the connections between topics and methods, or their evolution (e.g., how the contents and topics of sentiment analysis have changed over time).

2.4.2 From the point of view of the methods of sentiment analysis

Some researchers reviewed different techniques and methods of sentiment analysis in different application areas and tasks. They analyzed and discussed sentiment analysis methods based on lexicons, rules, part of speech, term position, statistical techniques, supervised and unsupervised machine learning methods, as well as deep learning methods like LSTM, CNN, RNN, DNN, DBN, BERT, and other hybrid approaches (Acheampong et al. 2021 ; Ain et al. 2017 ; Alamoodi et al. 2021b ; Asghar et al. 2014 ; Chan et al. 2022 ; Cheng et al. 2022 ; Feldman 2013 ; Habimana et al. 2020 ; Koto and Adriani 2015 ; Kumar, Akshi and Sebastian 2012 ; Medhat et al. 2014 ; Prabha and Srikanth 2019 ; Ravi and Ravi 2015 ; Schouten and Frasincar 2015 ; Serrano-Guerrero et al. 2021 ; Taboada 2016 ; Wang et al. 2020b ; Yue et al. 2019 ; Zhang et al. 2018 ; Zucco et al. 2020 ). These researchers also compared the advantages and disadvantages of each method. As summarized in Table 1 , even though existing surveys analyze the techniques and methods of sentiment analysis, providing good insights, there has not been a survey that analyzes the evolution of research methods over time. There have also been few survey works that focuses on the connections between topics and methods of sentiment analysis, and their evolution over time.

2.4.3 From the point of view of the overall survey methodology

The survey methods used have mainly been the content analysis method, Kitchenham and Charters' guideline, and the informetric methods. As summarized in Table 1 , the content analysis method can effectively analyze the contents of research papers in depth, but it does not address the issue of the evolution of the research methods and topics (Bengtsson 2016 ; Birjali et al. 2021 ; Elo and Kyngäs 2008 ; Krippendorff 2018 ; Qazi et al. 2015 , 2017 ; Wankhade et al. 2022 ). Although the number of papers that need to be read in depth can be reduced by following Kitchenham and Charters' guideline, more effort is needed to search and screen literature than in traditional literature review methods (Angel et al. 2021 ; Kitchenham 2004 ; Kitchenham and Charters 2007 ; Kumar and Garg 2020 ; Ligthart et al. 2021 ; Lin et al. 2022 ; Obiedat et al. 2021 ; Sarsam et al. 2020 ; Zunic et al. 2020 ). The informetric methods are best suited to investigating the research methods and topics of sentiment analysis (Bar-Ilan 2008 ; Mäntylä et al. 2018 ; Piryani et al. 2017 ; Santos et al. 2019 ; Verma 2022 ). There are three surveys using informetric techniques and tools that are well suited for analysis of a large number of papers over many years (Mäntylä et al. 2018 ; Piryani et al. 2017 ; Verma 2022 ). However, the evolution of research methods and topics of sentiment analysis over time has not been studied with informetric methods. There have also been few survey works that leverages keyword co-occurrence analysis and community detection to analyze the connections between research methods and topics, and their evolution over time.

Therefore, to address the gaps in the existing surveys, this study presents a survey on the research methods and topics, and their evolution over time. It combines keyword co-occurrence analysis and informetric analysis tools to reveal the methods and topics of sentiment analysis and their evolution in this field from 2002 to 2022.

The following section, Sect.  3 , describes our proposed survey methodology in detail.

3 The proposed survey methodology

This section describes our proposed survey methodology, including collection of scientific publications, processing of scientific publications, as well as visualization and analysis using different methods and tools. The overall scheme of this survey (Fig.  2 ) is also presented in the end of Sect.  3 to better visualize and summarize the proposed survey methodology in this research.

3.1 Collection of scientific publications

We collected research data from the Web of Science platform. We used keywords such as "sentiment analysis," "sentiment mining," and "sentiment classification" to search for relevant papers as data samples. In examining the retrieved papers, we found that some paper topics, paper types, and publication journals were not related to sentiment analysis, so we excluded them. The papers we included were mainly related to the sentiment analysis of texts. We excluded papers on sentiment analysis related to image processing, video processing, speech processing, biological signal processing, etc. Therefore, the retrieval strategy was as follows:

Topic Search (TS) = ("sentiment analy*" or "sentiment mining" or "sentiment classification") And Abstract (AB) = "sentiment" NOT TS = ("face image*" or "speech recognition" or "speech emotion" or "physiological signal*" or "music emotion*" or "facial feature extraction" or "video emotion" or "electroencephalography " or "biosignal*" or "image process*") NOT Title = ("facial" or "speech" or "sound*" or "face" or "dance" or "temperature" or "image*" or "spoken" or "electroencephalography" or "EEG" or "biosignal*" or "voice*" not AB = "facial."

The results in conferences are given the same relevance as journal papers. We chose four databases in the Web of Science: two conference citation databases (Conference Proceedings Citation Index—Social Sciences & Humanities [CPCI-SSH], and Conference Proceedings Citation Index—Science [CPCI-S]), and two journal citation databases (Science Citation Index Expanded [SCI-Expanded] and Social Sciences Citation Index [SSCI]). Given the various forms of words such as "analyzing" and "analysis," a truncated search technique (marked with an asterisk) was used to prevent the omission of relevant papers. The time frame of the retrieved papers was from January 2002 to January 2022, and the publication types of the papers included "article," "conference paper," "review," and "edited material." A total of 9,714 papers were obtained from the four databases above. These included 3,809 articles, 5,633 proceeding papers, 267 reviews, and 5 pieces of editorial material from 2002 to 2022. Overall, there were 104 papers from January 2022. The number of papers each year from 2002 to 2021 is shown in Fig.  1 .

The number of papers each year from 2002 to 2021

3.2 Processing of scientific publications

In this process, our purpose was to extract the key contents of the papers, which are used to analyze the research methods and topics in the field of sentiment analysis. Due to their limited number, the author keywords in each paper often cannot fully represent the key content of the paper. We found that combining the title and abstract could better reflect the core information. Therefore, we synthesized the title, abstract, and author keywords of each paper to extract keywords that represented the main research method and topic of the paper involved using KeyBERT Footnote 1 . KeyBERT is a keyword extraction technique that uses BERT embedding to create keywords and key phrases that most closely resemble document content (Grootendorst and Warmerdam 2021 ). The specific keyword extraction process was as follows:

First, we used KeyBERT to extract 8 keywords and eliminated keywords with a weight lower than 0.3. We then combined the extracted keywords with the author keywords and removed duplicates. After that, we standardized the whole collection of keywords and merged synonyms. Finally, we counted the number of keywords and removed meaningless terms like "sentiment analysis," "sentiment classification," and "sentiment mining."

After statistical analysis, we obtained 41,827 keywords with a total word frequency of 88,104. As there were 9,714 papers and 41,827 keywords, we found that most of the keywords with word frequency below 10 were not representative of the research contents of sentiment analysis. As a result, a total of 685 representative keywords were reserved for subsequent analysis. These keywords appeared a total of 30,801 times. Table 2 shows the keywords with word frequency in the top 50.

High-frequency keywords generally represent research hotspots. We therefore extracted high-frequency keywords to serve as the basis for the subsequent analysis. We found that most of the keywords with word frequency 18 and lower, such as "ranking," "mask," "experience," "affect," "online forum," and so on, were not relevant to sentiment analysis. Therefore, the keywords with a word frequency higher than 18 were reserved for analysis. These keywords appeared 25,429 times in the collected data, accounting for close to 83% of all the keywords. We obtained 275 keywords, which were used to analyze the main methods and topics of sentiment analysis.

3.3 Visualization and analysis using different methods and tools

3.3.1 analytical methods.

Keywords are the core natural language vocabulary to express the subject, content, ideas, and research methods of the literature (You et al. 2021 ). Keywords represent the topics of the domain, and cluster analysis of these words can reflect the structure and association of topics. Keyword co-occurrence analysis counts the number of occurrences of a set of keywords in the same document. The strength and number of associations between research contents can be obtained through keyword co-occurrence analysis. Dividing research methods and topics into sub-communities helps researchers to analyze hotspots and trends in methods and topics, as well as to obtain sub-fields of sentiment analysis research (Ding et al. 2001 ).

3.3.2 Visualization and analysis tools

BibExcel Footnote 2 is a software tool for analyzing bibliographic data or any text-based data formatted in a similar way (Persson 2017 ). The tool generates structured data files that can be read by Excel for subsequent processing (Persson et al. 2009 ). Our processing steps are as follows. First, we imported the standardized bibliographic data into BibExcel. This tool can help structure the data. Second, we checked and corrected the data and used BibExcel to count the number of co-occurrences of keywords.

We then used Pajek Footnote 3 software to visualize the keyword co-occurrence network and divided the sub-communities. Pajek is a large and complex network analysis tool (Batagelj and Andrej 2022 ; Batagelj and Mrvar 1998 ). It can calculate certain indicators to reveal the state and properties of the network involved. In addition, Pajek’s Louvain community detection algorithm can help divide the keyword co-occurrence network into sub-communities, which represent sub-fields of sentiment analysis (Blondel et al. 2008 ; Leydesdorff et al. 2014 ; Rotta and Noack 2011 ). The Louvain community-detection algorithm unfolds a complete hierarchical community structure for the network. It has an advantage in subdividing different areas of study: multiple knowledge structures and details can be shown in one network (Deng et al. 2021 ).

After that, we applied VOSviewer Footnote 4 to optimize the visualization of sub-communities (Van Eck and Waltman 2010 ; VOSviewer 2021 ; Perianes-Rodriguez et al. 2016 ; Waltman and Van Eck 2013 ; Waltman et al. 2010 ). VOSviewer can help display the core keywords in each sub-community and the correlation between keywords. It can also reflect the closeness of the association between sub-communities. Finally, we used Excel to count the frequency of keywords for each year and to map the evolution of research methods and topics in the field of sentiment analysis.

3.3.3 Graphical representation of the overall scheme of this survey

This paper proposes and conducts a new research survey on sentiment analysis. The graphical representation of the overall scheme of this survey is shown in Fig.  2 . The main scheme includes four modules: Module A, Collection of scientific publications; Module B, Processing of scientific publications; Module C, Visualization and analysis through different methods and tools, and Module D, Result analysis and discussions based on various aspects.

Graphical representation of the overall scheme of this survey. Module A: Collection of scientific publications; Module B: Processing of scientific publications; Module C: Visualization and analysis using different methods and tools; Module D: Result analysis and discussions considering various aspects

In Module A, scientific publications are collected from the Web of Science (WOS) platform, as has been detailed in Sect.  3.1 Collection of scientific publications above. Module B, Processing of scientific publications, has been detailed in Sect.  3.2 above. It performs a data processing procedure to obtain key information, which includes all the representative keywords and high-frequency keywords. The title, abstract and keywords of the papers are used to extract such key information using KeyBERT (Grootendorst and Warmerdam 2021 ). Such key information is analyzed and visualized through different methods, including different visualization tools, as introduced in Sect.  3.3 (Module C), Visualization and analysis using different methods and tools, above.

In Module C, the number of co-occurrences of keywords is obtained using BibExcel (Persson 2017 ), the co-occurrences of keywords are analyzed and visualized using Pajek (Blondel et al. 2008 ; Leydesdorff et al. 2014 ; Rotta and Noack 2011 ) and VOSviewer (Van Eck and Waltman 2010 ; VOSviewer 2021 ; Perianes-Rodriguez et al. 2016 ; Waltman and Van Eck 2013 ; Waltman et al. 2010 ). The keyword community network and the keyword community evolution are analyzed and visualized using these tools, as described in Sect.  3.3 (Module C), Visualization and analysis using different methods and tools. According to the visualization and analysis results obtained in Module C, Module D, Result analysis and discussions, will be detailed in Sect.  4 .

In the following section, Sect.  4 (Module D), results are analyzed and discussed considering various aspects, including the research methods and topics of sentiment analysis in each community, the evolution of research methods and topics along with the research hotspots and trends over time.

4 Results and analysis through various aspects

4.1 research methods and topics of sentiment analysis, 4.1.1 overall characteristic analysis.

The high-frequency keywords were presented in Table 2 . These keywords can be regarded as the main research contents in the field of sentiment analysis. "Twitter" ranks at the top. It is followed by "opinion mining," "natural language processing," "machine learning," and so on. The high-frequency keywords cover the topics of the studies, the contents of the studies, and the techniques and methods used. Based on these keywords, we used Pajek’s Louvain method to construct a keyword co-occurrence network to represent the research methods and topics as shown in Fig.  3 . The keyword co-occurrence network is divided into six communities. The research methods and topics of the six communities include social media platforms (C1), machine learning methods (C2), natural language processing and deep learning methods (C3), opinion mining and text mining (C4), Arabic sentiment analysis (C5), and others, such as domain sentiment analysis and transfer learning, etc. (C6).

Keyword community network

In Fig.  3 , the size of the node represents the number of keywords. The thickness of the line between the nodes represents the number of collaborations between keywords. The top 20 keywords in each community are sorted in descending order, as shown in Table 3 . The keyword co-occurrence network features of the six sub-communities are described in Table 4 . The number of nodes shows the number of keywords in each community, and the number of links shows the correlations between the keywords.

As shown in Table 4 , we can see from the number of links between sub-communities that there is a strong correlation between them, especially the link between C3 and C4, which has 1306 lines. The reason may be that the research methods of C4 focus on "opinion mining" and "text mining," while those of C3 focus on "natural language processing" and "deep learning," and C3 provides more technical support for C4 research. In C5 and C6, the research methods and topics are scattered. Their internal links are also low, but the connections with C3 and C4 are relatively high. The contents of C5 and C6 may include some emerging research methods and topics. We will present a specific analysis on the methods and topics of each sub-community in the next subsection.

4.1.2 Analysis on research methods and topics of sub-communities

4.1.2.1 analysis on research methods and topics of the c1 community.

Figure  4 shows the keyword co-occurrence network of the C1 community. The research methods and topics of the C1 community focus on three areas: "social media," "topic models," and "covid-19." In the context of big data, web 2.0 technology provides users with a way to express reviews and opinions of services, events, and people. Various social media platforms, such as Twitter, YouTube, and Weibo, have a large amount of users’ emotional data (Momtazi 2012 ). Compared to traditional news media, information on social media spreads more quickly, and people are able to express their feelings more freely. It is important to analyze the emotions generated by the information shared and published on social media (Abdullah and Zolkepli 2017 ; Wang et al. 2014 ). Researchers have been extracting text data from social media platforms for years to detect unexpected events (Bai and Yu 2016 ; Preethi et al. 2015 ), improve the quality of products (Abrahams et al. 2012 ; Isah et al. 2014 ; Myslin et al. 2013 ), understand the direction of public opinion (Fink et al. 2013 ; Groshek and Al-Rawi 2013 ), and so on.

The keyword co-occurrence network for the C1 community

Users’ sentiments are often associated with the topics, and the accuracy of sentiment analysis can be improved through the introduction of topic models (Li et al. 2010 ). Among them, the Latent Dirichlet Allocation (LDA) method is cited most frequently. Previous studies found that the LDA method can be effective in subdividing topics and identifying the sentiments of the contents. This method is quite general, and there are also many improved models based on this one that can be applied to any type of web text, helping to enhance the accuracy of sentiment polarity calculation (Chen et al. 2019 ; Liu et al. 2020 ).

As the COVID-19 pandemic has unfolded, a large number of individuals, media and governments have been publishing news and opinions about the COVID-19 crisis on social media platforms. This has resulted in a lot of sentiment analysis studies focusing on COVID-19-related texts exploring the impact of the epidemic on people’s lives (Sari and Ruldeviyani 2020 ; Wang, T. et al. 2020a ), physical health (Berkovic et al. 2020 ; Binkheder et al. 2021 ) and mental health (Yin et al. 2020 ), and so on. Therefore, we can see many related keywords, such as "infodemiology," "healthcare," and "mental health."

4.1.2.2 Analysis on research methods and topics of the C2 community

The contents of the C2 community mainly focus on "machine learning," "text classification," "feature extraction," and "stock market" (see Fig.  5 ). Most keywords are related to the research methods of sentiment analysis. Machine learning approaches have expanded from topic recognition to more challenging tasks such as sentiment classification. It is very important to explore and compare machine learning methods applied to sentiment classification (Li and Sun 2007 ). Methods like Support Vector Machine (SVM) and Naive Bayes models are widely used (Altrabsheh et al. 2013 ; Dereli et al. 2021 ; Shofiya and Abidi 2021 ; Tan et al. 2009 ; Wang and Lin 2020 ) and are used as benchmarks for the comparisons of models proposed by many researchers (Kumar et al. 2021 ; Sadamitsu et al. 2008 ; Waila et al. 2012 ; Zhang et al. 2019 ). Many algorithms, such as random forest (Al Amrani et al. 2018 ; Fitri et al. 2019 ; Sutoyo et al. 2022 ), tf-idf (Arafin Mahtab et al. 2018 ; Awan et al. 2021 ; Dey et al. 2017 ), logistic regression (Prabhat and Khullar 2017 ; Qasem et al. 2015 ; Sutoyo et al. 2022 ), and n-gram (Ikram and Afzal 2019 ; Singh and Kumari 2016 ; Xiong et al. 2021 ) are used to enhance the accuracy of machine learning, as shown in Fig.  5 .

The keyword co-occurrence network for the C2 community

The trading volume and asset prices of financial commodities or financial instruments are influenced by a variety of factors in the online environment. Machine learning and sentiment analysis are powerful tools that can help gather vast amounts of useful information to predict financial risk effectively (Li et al. 2009 ). Research on the relationship between public sentiment and stock prices has always been the focus of many scholars (Smailović et al. 2014 ; Xing et al. 2018 ). They have used machine learning methods to explore the influence of sentiments on stock prices through sentiment analysis of news articles, and then predicted the trend changes in the stock market (Ahuja et al. 2015 ; Januário et al. 2022 ; Maqsood et al. 2020 ; Picasso et al. 2019 ).

4.1.2.3 Analysis on research methods and topics of the C3 community

The contents of the C3 community also mainly focus on the methods for sentiment analysis, like "natural language processing", "deep learning," "aspect-based sentiment analysis," and "task analysis" (Fig.  6 ). Sentiment analysis is a sub-field of natural language processing (Nicholls and Song 2010 ), and natural language processing techniques have been widely used in sentiment analysis. Using natural language processing technology can help to better parse text features, such as part-of-speech tagging, word sense disambiguation, keyword extraction, inter-word dependency recognition, semantic parsing, and dictionary construction (Abbasi et al. 2011 ; Syed et al. 2010 ; Trilla and Alías 2009 ). With the rise of deep learning technology, researchers began to introduce it to sentiment analysis. Neural network models like LSTM (Al-Dabet et al. 2021 ; Al-Smadi et al. 2019 ; Li and Qian 2016 ; Schuller et al. 2015 ; Tai et al. 2015 ), CNN (Cai and Xia 2015 ; Jia and Wang 2022 ; Ouyang et al. 2015 ), RNN (Hassan and Mahmood 2017 ; Tembhurne and Diwan 2021 ; You et al. 2016 ), and some combination of these, as well as other models (An and Moon 2022 ; Li et al. 2022 ; Liu et al. 2020a ; Salur and Aydin 2020 ; Zhao et al. 2021 ), have received significant attention.

The keyword co-occurrence network for the C3 community

Sentiment analysis granularity is subdivided into document level, sentence level, and aspect level. Document-level sentiment analysis takes the entire document as a unit, but the premise is that the document needs to have a clear attitude orientation—that is, the point of view needs to be clear (Shirsat et al. 2018 ; Wang and Wan 2011 ). Sentence-level sentiment analysis is intended to perform sentiment analysis of the sentences in the document alone (Arulmurugan et al. 2019 ; Liu et al. 2009 ; Nejat et al. 2017 ). Aspect-based analysis is a fundamental and significant task in sentiment analysis. The aim of aspect-level sentiment analysis is to separately summarize positive and negative views about different aspects of a product or entity, although overall sentiment toward a product or entity may tend to be positive or negative (Rao et al. 2021 ; Thet et al. 2010 ). Aspect-level sentiment analysis facilitates a more finely-grained analysis of sentiment than either document or sentence-level analysis (Liang et al. 2022 ; Wang et al. 2020c ). The traditional levels of analysis, such as sentence-level analysis can only calculate the comprehensive sentiment polarity of paragraphs or sentences (Wang et al. 2016 ; Zhang et al. 2021 ). In recent years, the aspect level has become more and more popular, and with the application of deep learning technology, it has become better at capturing the semantic relationship between aspect terms and words in a more quantifiable way (Huang et al. 2018 ). The process of sentiment analysis involves the coordination of multiple tasks, and the subtasks include feature extraction (Bouktif et al. 2020 ; Lin et al. 2020 ), context analysis (Yu et al. 2019 ; Zuo et al. 2020 ), and the application of some analytical models (Tan et al. 2020 ).

4.1.2.4 Analysis on research methods and topics of the C4 community

The C4 community mainly shows keywords related to the research methods and topics of "opinion mining" and "user review," which is the largest of the six sub-communities (Fig.  7 ). With the popularity of platforms like online review sites and personal blogs on the Internet, opinions and user reviews are readily available on the web. Opinion mining has always been a hot field of research (Khan et al. 2009 ; Poria et al. 2016 ). From Table 4 , we can see that the link between C3 and C4 has 1306 lines. In opinion mining, researchers use many text mining methods to discover users’ opinions on goods or services, and then help improve the quality of corresponding products or services (Da’u et al. 2020 ; Lo and Potdar 2009 ; Martinez-Camara et al. 2011 ). In addition, scholars have found that the consideration of user opinions can help improve the overall quality of recommender systems (Artemenko et al. 2020 ; Da’u et al. 2020 ; Garg 2021 ; Malandri et al. 2022 ). Therefore, "recommendation system" has a strong correlation with "opinion mining."

The keyword co-occurrence network for C4 community

Evaluation metrics for quantifying the existing approaches are also a popular topic related to opinion mining. There is a keyword named "performance sentiment" in the C4 community. Precision, recall, accuracy and F1-score are the most commonly used evaluation metrics (Dangi et al. 2022 ; Jain et al. 2022 ; JayaLakshmi and Kishore 2022 ; Li et al. 2017 ; Wang et al. 2021 ; Yi and Niblack 2005 ). Some researchers have also used runtimes to calculate the model efficiency (Abo et al. 2021 ; Ferilli et al. 2015 ), p-value to statistically evaluate the relationship or difference between two samples of classification results (JayaLakshmi and Kishore 2022 ; Salur and Aydin 2020 ), paired sample t-tests to verify that the results are not obtained by chance (Nhlabano and Lutu 2018 ), and standard deviation to measure the stability of the model (Chang et al. 2020 ). There have also been researchers who have used G-mean (Wang et al. 2021 ), Pearson Correlation Coefficient (Corr) (Yang et al. 2022 ), Mean Absolute Error (MAE) (Yang et al. 2022 ), Normalized Information Transfer (NIT) and Entropy-Modified Accuracy (EMA) (Valverde-Albacete et al. 2013 ), Mean Squared Error (MSE) (Mao et al. 2022 ), Hamming loss (Liu and Chen 2015 ), Area Under the Curve (AUC) (Abo et al. 2021 ), sensitivity and specificity (Thakur and Deshpande 2019 ), etc.

4.1.2.5 Analysis on research methods and topics of the C5 & C6 communities

Both sub-communities C5 (Fig.  8 ) and C6 (Fig.  9 ) are small in size. The C5 community has 25 nodes and the C6 community has 41 nodes. The core content of the C5 community is "Arabic sentiment analysis." Before 2011, most resources and systems built in the field of sentiment analysis were tailored to English and other Indo-European languages. It is increasingly necessary to design sentiment analysis systems for other languages (Korayem et al. 2012 ), and researchers are increasingly interested in the study of tweets and texts in the Arabic language (Heikal et al. 2018 ; Khasawneh et al. 2013 ; Oueslati et al. 2020 ). They use technologies such as named entity recognition (Al-Laith and Shahbaz 2021 ), deep learning (Al-Ayyoub et al. 2018 ; Heikal et al. 2018 ), and corpus construction (Alayba et al. 2018 ) to enhance the accuracy of sentiment analysis.

The keyword co-occurrence network for the C5 community

The keyword co-occurrence network for the C6 community

The contents of the C6 community are not very concentrated. From the size of the circle, we can see that the keywords "domain adaptation"(Blitzer et al. 2007 ; Glorot et al. 2011 ), "domain sentiment," and "cross-domain" appear more frequently. Cross-domain sentiment classification is intended to address the lack of mass labeling data (Du et al. 2020a ). It has attracted much attention (Du et al. 2020b ; Hao et al. 2019 ; Yang et al. 2020b ). Advances in communication technology have provided valuable interactive resources for people in different regions, and the processing of multilingual user comments has gradually become a key challenge in natural language processing (Martinez-Garcia et al. 2021 ). Therefore, some keywords related to "lingual" have appeared. Other keywords, such as "transfer learning," "active learning," and "semi-supervised learning," are mainly related to sentiment analysis technologies.

4.2 Evolution of research methods and topics of sentiment analysis

4.2.1 overall evolution analysis.

Annual changes in keyword frequency in sentiment analysis research can reflect the evolution of research methods and topics in this field. Based on the keyword community network (Fig.  3 ), we counted the frequency of keywords in each sub-community for each year. The keyword community evolution diagram is shown in Fig.  10 . Since there were fewer papers published before 2006, we combined the occurrences of keywords from 2002 to 2006. We can see that the C1 community and the C3 community have shown a significant growth trend. The C2 community was in a state of growth until 2019, and the frequency of keywords decreased year by year after 2019. The frequency of C4 community keywords continued to increase until 2018 and declined after 2018. The number of keywords in the C5 community and in the C6 community both had a slow growth trend, but the trend was not obvious.

Keyword community evolution diagram

4.2.2 Evolution analysis of sub-communities

We selected the high-frequency keywords under each category and plotted the change of word frequency in each year, as shown in Figs.  11 and 12 . In the C1 community, "social medium," "Twitter," "social network," "covid-19," "Latent Dirichlet Allocation," "topic model," and "text analysis" all had significant increases in word frequency, and the growth trend in 2021 was obvious. "Covid-19" appears in 2020, and the word frequency increased rapidly in 2021. Social media platforms have always been the focus of researchers’ attention. Under the influence of COVID-19, more people express their emotions, stress, and thoughts through social media platforms. Sentiment analysis on data from social media platforms related to COVID-19 has become a hot topic (Boon-Itt and Skunkan 2020 ). We believe that due to the impact of COVID-19, the widespread use of social platforms in 2020–2021 has led to a surge in the number of C1-related keywords.

C1, C2, C5, C6 communities: High-frequency keyword evolution diagram

C3, C4 communities: High-frequency keyword evolution diagram

The C2 community focuses on the method of "machine learning," and the C3 community focuses on the methods of "deep learning" and "natural language processing." The keywords in the two communities are mainly related to the techniques and methods of sentiment analysis. We have found that before 2016 (Fig.  10 ), the frequency of keywords in the C2 community was higher than that in the C3 community, and in 2016 and later, the frequency of keywords in the C3 community gradually accounted for a larger proportion of the total. This reflects the fact that deep learning-related technologies and methods have become a research hotspot, and the attention given to SVM, Naive Bayes, supervised learning, and other technologies in machine learning has declined. In addition to deep learning models such as Bi-LSTM, Long Short-term Memory, and recurrent neural network in the C3 community, the number of "aspect based" and "feature extraction" keywords have also been growing, which shows that researchers now pay more attention to the aspect level of text granularity in the field of sentiment analysis.

Among the keywords found in the C4 community, the word frequency of the "opinion mining" keyword has decreased since 2018. This shows that in the field of sentiment analysis, researchers have begun to reduce the attention they give to sentiment analysis of opinions on product or service quality, while still maintaining a certain degree of attention to "user review" and "online review." In addition, the number of keywords for "sentiment lexicon" and "lexicon-based" has declined. It may be because, in the context of the widespread application of deep learning technology in recent years, the lexicon-based method requires more time and higher labor costs (Kaity and Balakrishnan 2020 ). However, its accuracy still attracts attention due to the high involvement of experts, especially in non-English languages (Bakar et al. 2019 ; Kydros et al. 2021 ; Piryani et al. 2020 ; Tammina 2020 ; Xing et al. 2019 ; Yurtalan et al. 2019 ).

The high-frequency keywords in the C5 and C6 communities are "Arabic language," "Arabic sentiment analysis," and "transfer learning." Arabic has 30 variants, including the official Modern Standard Arabic (MSA) (ISO 639–3 2017). Arabic dialects are becoming increasingly popular as the language of informal communication on blogs, forums, and social media networks (Lulu and Elnagar 2018 ). This makes them challenging languages for natural language processing and sentiment analysis (Alali et al. 2019 ; Elshakankery and Ahmed 2019 ; Sayed et al. 2020 ). Transfer learning can solve the problem by leveraging knowledge obtained from a large-scale source domain to enhance the classification performance of target domains (Heaton 2018 ). In recent years, based on the success of deep learning technology, this method has gradually attracted attention.

5 Research hotspots and trends

Through the analysis in Sects.  4.1 and 4.2 , we found that the research methods and topics of sentiment analysis are constantly changing. The keyword topic heat map is shown in Fig.  13 . From this map, we can see that in the past two decades, research hotspots have included social media platforms (such as "social medium," "social network," and "Twitter"); sentiment analysis techniques and methods (such as "machine learning," "svm," "natural language processing," "deep learning," "aspect-based," "text mining," and "sentiment lexicon"), mining of user comments or opinions (e.g., "opinion mining," "user review," and "online review"), and sentiment analysis for non-English languages (e.g., "Arabic sentiment analysis" and "Arabic language").

Keyword topic heat map

With the popularity of digitization, a large amount of user-generated content has appeared on the Internet, where users express their opinions and comments on different topics such as the news, events, activities, products, services, etc. through social media. This is especially so in the case of the Twitter mobile platform, launched in 2006, which has become the most popular social channel (Kumar and Jaiswal 2020 ). However, online text data is mostly unstructured. In order to accurately analyze users’ sentiments, the research methods for sentiment analysis, such as natural language processing technology, and automatic sentiment analysis models have become the focus of researchers’ works. From Fig.  11 , we can see that early technologies and methods are dominated by machine learning and that SVM and Naive Bayes have always been favored by researchers. This has also been confirmed in studies by Neha Raghuvanshi (Raghuvanshi and Patil 2016 ), Harpreet Kaur (Kaur et al. 2017 ), and Marouane Birjali (Birjali et al. 2021 ). With the improvement of neural network and artificial intelligence technology, deep learning technology has been widely used in sentiment analysis, and has resulted in good outcomes (Basiri et al. 2021 ; Ma et al. 2018 ; Prabha and Srikanth 2019 ; Yuan et al. 2020 ). However, deep learning technology still has room for improvement, and the hybrid methods combining sentiment dictionary and semantic analysis are gradually becoming a trend (Prabha and Srikanth 2019 ; Yang et al. 2020a ).

The granularity of sentiment analysis ranges from the early text level to the sentence level and finally to the aspect level, which is currently gaining strong attention. The granularity of sentiment analysis is gradually being refined, but the method is immature at present, and further research work in the future is needed (Agüero-Torales et al. 2021 ; Li et al. 2020 ; Trisna and Jie 2022 ).

Early sentiment analysis was mainly in the English language. In recent years, non-English languages such as Chinese (Lai et al. 2020 ; Peng et al. 2018 ), French (Apidianaki et al. 2016 ; Pecore and Villaneau 2019 ), Spanish (Chaturvedi et al. 2016 ; Plaza-del-Arco et al. 2020 ), Russian (Smetanin 2020 ), and Arabic (Alhumoud and Al Wazrah 2022 ; Ombabi et al. 2020 ) have attracted more and more attention. Furthermore, cross-domain sentiment analysis technology is in urgent need of research and discussion by researchers (Liu et al. 2019 ; Singh et al. 2021 ).

6 Conclusion and future work

6.1 conclusion.

Judging from the increasing number of papers related to sentiment analysis research every year, sentiment analysis has been on the rise. Although there are many surveys on sentiment analysis research, there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. This paper has used keyword co-occurrence analysis and the informetric tools to enrich the perspectives and methods of previous studies. Its aims have been to outline the evolution of the research methods and tools, research hotspots and trends and to provide research guidance for researchers.

By adopting keyword co-occurrence analysis and community detection methods, we analyzed the research methods and topics of sentiment analysis, as well as their connections and evolution trends, and summarized the research hotspots and trends in sentiment analysis. We found that research hotspots include social media platforms, sentiment analysis techniques and methods, mining of user comments or opinions, and sentiment analysis for non-English languages. Moreover, deep learning technology, with its hybrid methods combining sentiment dictionary and semantic analysis, fine-grained sentiment analysis methods, and non-English language analysis methods, and cross-domain sentiment analysis techniques have gradually become the research trends.

6.2 Practical implications and technical directions of sentiment analysis

Sentiment analysis has a wide range of application targets, such as e-commerce platforms, social platforms, public opinion platforms, and customer service platforms. Years of development have led to many related tasks in sentiment analysis, such as sentiment analysis of different text granularity, sentiment recognition, opinion mining, dialogue sentiment analysis, irony recognition, false information detection, etc. Such analysis can help structure user reviews, support product improvement decisions, discover public opinion hotspots, identify public positions, investigate user satisfaction with products, and so on. As long as user-generated content is involved, sentiment analysis technology can be used to mine the emotions of human actors associated with the content. The improvement of sentiment analysis technology can help machines better understand the thoughts and opinions of users, make machines more intelligent, and make better decisions for policy leaders, businessmen, and service people. However, most of the current sentiment analysis methods are based on sentiment dictionaries, sentiment rules, statistics-based machine learning models, neural network-based deep learning models, and pre-training models, and have yet to achieve true language understanding in the sense of comprehension at the deep semantic level, though this does not prevent them from being useful in certain practical applications.

As an important task in natural language understanding, sentiment analysis has received extensive attention from academia and industry. Coarse-grained sentiment analysis is increasingly unable to meet people's decision-making needs, and for aspect-level sentiment analysis and complex tasks, pure machine learning is still unable to flexibly achieve true language understanding. Once the scene or domain changes, problems such as the domain incompatibility of the sentiment dictionary and the low transfer effect of the model involved keep appearing. At present, the accuracy of sentiment analysis provided by machines is far less than that of humans. To achieve human-like performance for machines, we believe that it is necessary to incorporate human commonsense knowledge and domain knowledge, as well as grounded definitions of concepts, in order for machines to understand natural language at a deeper level. These, combined with rules for affective reasoning to supplement interpretable information, will be effective in improving the performance of sentiment analysis. Future research in this direction can be strengthened to achieve true language understanding in machines.

6.3 Limitations and future work

There are some research limitations in this paper. First, we only studied papers written in English and searched from the Web of Science platform. We believe there are papers in other languages or other databases (e.g., Scopus, PubMed, Sci-hub, etc.) that also involve sentiment analysis but that were not included in our study. In addition, the keywords we chose to search in the Web of Science were mainly "sentiment analysis," "sentiment mining," and "sentiment classification." There may be papers related to our research topic that do not have these keywords. To track developments in sentiment analysis research, future studies could replicate this work by employing more precise keywords and using different literature databases.

Second, we selected the main high-frequency keywords for analysis, and some important low-frequency keywords may have been ignored. In future work, we can analyze the changes in each keyword in detail from the perspective of time and obtain more comprehensive analysis results.

Third, the results show that the themes of sentiment analysis cover many fields, such as computer science, linguistics, and electrical engineering, which indicates the trend of interdisciplinary research. Therefore, future work should apply co-citation and diversity measures to explore the interdisciplinary nature of sentiment analysis research.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The authors would like to thank the China Scholarship Council (CSC No. 202106850069) for its support for the visiting study.

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What are the Different Types of Research Papers?

There is a diverse array of research papers that one can find in academic writing. Research papers are a rigorous combination of knowledge, thinking, analysis, research, and writing. Early career researchers and students need to know that research papers can be of fundamentally different types. Generally, they combine aspects and elements of multiple strands or frameworks of research. This depends primarily on the aim of the study, the discipline, the critical requirements of research publications and journals and the research topic or area. Specifically, research papers can be differentiated by their primary rationale, structure, and emphasis. The different types of research papers contribute to the universe of knowledge while providing invaluable insights for policy and scope for further advanced research and development. In this article, we will look at various kinds of research papers and understand their underlying principles, objectives, and purposes.  

Different types of research papers

• Argumentative Research Paper:  In an argumentative paper, the researcher is expected to present facts and findings on both sides of a given topic but make an extended and persuasive argument supporting one side  over  the other. The purpose of such research papers is to provide evidence-based arguments to support the claim or thesis statement taken up by the researcher. Emotions mustn’t inform the building up of the case. Conversely, facts and findings must be objective and logical while presenting both sides of the issue. The position taken up by the researcher must be stated clearly and in a well-defined manner. The evidence supporting the claim must be well-researched and up-to-date, and the paper presents differing views on the topic, even if these do not agree or align with the researcher’s thesis statement. 
• Analytical Research Paper:  In an analytical research paper, the researcher starts by asking a research question, followed by a collection of appropriate data from a wide range of sources. These include primary and secondary data, which the researcher needs to analyze and interpret closely. Critical and analytical thinking skills are therefore crucial to this process. Rather than presenting a summary of the data, the researcher is expected to analyze the findings and perspectives of each source material before putting forward their critical insights and concluding. Personal biases or positions mustn’t influence or creep into the process of writing an analytical research paper. 
• Experimental Research Paper:  Experimental research papers provide a detailed report on a particular research experiment undertaken by a researcher and its outcomes or findings. Based on the research experiment, the researcher explains the experimental design and procedure, shows sufficient data, presents analysis, and draws a conclusion. Such research papers are more common in fields such as biology, chemistry, and physics. Experimental research involves conducting experiments in controlled conditions to test specific hypotheses. This not only allows researchers to arrive at particular conclusions but also helps them understand causal relationships. As it lends itself to replicating the findings of the research, it enhances the validity of the research conducted. 

Some more types of research papers

In addition to the above-detailed types of research papers, there are many more types, including review papers, case study papers, comparative research papers and so on.  

• Review papers   provide a detailed overview and analysis of existing research on a particular topic. The key objective of a review paper is to provide readers with a comprehensive understanding of the latest research findings on a specific subject. 
• Case study papers  usually focus on a single or small number of cases. This is used in research when the aim is to obtain an in-depth investigation of an issue.  
• Comparative research papers  involve comparing and contrasting two or more entities or cases that help to identify and arrive at trends or relationships. The objective of relative research papers is to increase knowledge and understand issues in different contexts. 
• Survey research papers  require that a survey be conducted on a given topic by posing questions to potential respondents. Once the survey has been completed, the researcher analyzes the information and presents it as a research paper. 
• Interpretative paper s  employ the knowledge or information gained from pursuing a specific issue or research topic in a particular field. It is written around theoretical frameworks and uses data to support the thesis statement and findings.  

Research papers are an essential part of academic writing and contribute significantly to advancing our knowledge and understanding of different subjects. The researcher’s ability to conduct research, analyze data, and present their findings is crucial to producing high-quality research papers. By understanding the different types of research papers and their underlying principles, researchers can contribute to the advancement of knowledge in their respective fields and provide invaluable insights for policy and further research.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

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How to Create a Structured Research Paper Outline | Example

Published on August 7, 2022 by Courtney Gahan . Revised on August 15, 2023.

A research paper outline is a useful tool to aid in the writing process , providing a structure to follow with all information to be included in the paper clearly organized.

A quality outline can make writing your research paper more efficient by helping to:

• Organize your thoughts
• Understand the flow of information and how ideas are related
• Ensure nothing is forgotten

A research paper outline can also give your teacher an early idea of the final product.

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Research paper outline example, how to write a research paper outline, formatting your research paper outline, language in research paper outlines.

• Definition of measles
• Rise in cases in recent years in places the disease was previously eliminated or had very low rates of infection
• Figures: Number of cases per year on average, number in recent years. Relate to immunization
• Symptoms and timeframes of disease
• Risk of fatality, including statistics
• How measles is spread
• Immunization procedures in different regions
• Different regions, focusing on the arguments from those against immunization
• Immunization figures in affected regions
• High number of cases in non-immunizing regions
• Illnesses that can result from measles virus
• Fatal cases of other illnesses after patient contracted measles
• Summary of arguments of different groups
• Summary of figures and relationship with recent immunization debate
• Which side of the argument appears to be correct?

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Follow these steps to start your research paper outline:

• Decide on the subject of the paper
• Write down all the ideas you want to include or discuss
• Organize related ideas into sub-groups
• Arrange your ideas into a hierarchy: What should the reader learn first? What is most important? Which idea will help end your paper most effectively?
• Create headings and subheadings that are effective
• Format the outline in either alphanumeric, full-sentence or decimal format

There are three different kinds of research paper outline: alphanumeric, full-sentence and decimal outlines. The differences relate to formatting and style of writing.

• Alphanumeric
• Full-sentence

An alphanumeric outline is most commonly used. It uses Roman numerals, capitalized letters, arabic numerals, lowercase letters to organize the flow of information. Text is written with short notes rather than full sentences.

• Sub-point of sub-point 1

Essentially the same as the alphanumeric outline, but with the text written in full sentences rather than short points.

• Additional sub-point to conclude discussion of point of evidence introduced in point A

A decimal outline is similar in format to the alphanumeric outline, but with a different numbering system: 1, 1.1, 1.2, etc. Text is written as short notes rather than full sentences.

• 1.1.1 Sub-point of first point
• 1.1.2 Sub-point of first point
• 1.2 Second point

To write an effective research paper outline, it is important to pay attention to language. This is especially important if it is one you will show to your teacher or be assessed on.

There are four main considerations: parallelism, coordination, subordination and division.

Parallelism: Be consistent with grammatical form

Parallel structure or parallelism is the repetition of a particular grammatical form within a sentence, or in this case, between points and sub-points. This simply means that if the first point is a verb , the sub-point should also be a verb.

Example of parallelism:

• Include different regions, focusing on the different arguments from those against immunization

Coordination: Be aware of each point’s weight

Your chosen subheadings should hold the same significance as each other, as should all first sub-points, secondary sub-points, and so on.

Example of coordination:

• Include immunization figures in affected regions
• Illnesses that can result from the measles virus

Subordination: Work from general to specific

Subordination refers to the separation of general points from specific. Your main headings should be quite general, and each level of sub-point should become more specific.

Example of subordination:

Division: break information into sub-points.

Your headings should be divided into two or more subsections. There is no limit to how many subsections you can include under each heading, but keep in mind that the information will be structured into a paragraph during the writing stage, so you should not go overboard with the number of sub-points.

Ready to start writing or looking for guidance on a different step in the process? Read our step-by-step guide on how to write a research paper .

Cite this Scribbr article

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Gahan, C. (2023, August 15). How to Create a Structured Research Paper Outline | Example. Scribbr. Retrieved March 12, 2024, from https://www.scribbr.com/research-paper/outline/

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'all of us' research project diversifies the storehouse of genetic knowledge.

Results from a DNA sequencer used in the Human Genome Project. National Human Genome Research Institute hide caption

Results from a DNA sequencer used in the Human Genome Project.

A big federal research project aimed at reducing racial disparities in genetic research has unveiled the program's first major trove of results.

"This is a huge deal," says Dr. Joshua Denny , who runs the All of Us program at the National Institutes of Health. "The sheer quantify of genetic data in a really diverse population for the first time creates a powerful foundation for researchers to make discoveries that will be relevant to everyone."

The goal of the $3.1 billion program is to solve a long-standing problem in genetic research: Most of the people who donate their DNA to help find better genetic tests and precision drugs are white.

"Most research has not been representative of our country or the world," Denny says. "Most research has focused on people of European genetic ancestry or would be self-identified as white. And that means there's a real inequity in past research."

For example, researchers "don't understand how drugs work well in certain populations. We don't understand the causes of disease for many people," Denny says. "Our project is to really correct some of those past inequities so we can really understand how we can improve health for everyone."

But the project has also stirred up debate about whether the program is perpetuating misconceptions about the importance of genetics in health and the validity of race as a biological category.

New genetic variations discovered

Ultimately, the project aims to collect detailed health information from more than 1 million people in the U.S., including samples of their DNA.

In a series of papers published in February in the journals Nature , Nature Medicine , and Communications Biology , the program released the genetic sequences from 245,000 volunteers and some analysis of those data.

"What's really exciting about this is that nearly half of those participants are of diverse race or ethnicity," Denny says, adding that researchers found a wealth of genetic diversity.

"We found more than a billion genetic points of variation in those genomes; 275 million variants that we found have never been seen before," Denny says.

"Most of that variation won't have an impact on health. But some of it will. And we will have the power to start uncovering those differences about health that will be relevant really maybe for the first time to all populations," he says, including new genetic variations that play a role in the risk for diabetes .

Shots - Health News

Researchers gather health data for 'all of us'.

But one concern is that this kind of research may contribute to a misleading idea that genetics is a major factor — maybe even the most important factor — in health, critics say.

"Any effort to combat inequality and health disparities in society, I think, is a good one," says James Tabery , a bioethicist at the University of Utah. "But when we're talking about health disparities — whether it's black babies at two or more times the risk of infant mortality than white babies, or sky-high rates of diabetes in indigenous communities, higher rates of asthma in Hispanic communities — we know where the causes of those problem are. And those are in our environment, not in our genomes."

Race is a social construct, not a genetic one

Some also worry that instead of helping alleviate racial and ethnic disparities, the project could backfire — by inadvertently reinforcing the false idea that racial differences are based on genetics. In fact, race is a social category, not a biological one.

"If you put forward the idea that different racial groups need their own genetics projects in order to understand their biology you've basically accepted one of the tenants of scientific racism — that races are sufficiently genetically distinct from each other as to be distinct biological entities," says Michael Eisen , a professor of molecular and cell biology at the University of California, Berkeley. "The project itself is, I think, unintentionally but nonetheless really bolstering one of the false tenants of scientific racism."

While Nathaniel Comfort, a medical historian at Johns Hopkins, supports the All of Us program, he also worries it could give misconceptions about genetic differences between races "the cultural authority of science."

Denny disputes those criticisms. He notes the program is collecting detailed non-genetic data too.

"It really is about lifestyle, the environment, and behaviors, as well as genetics," Denny says. "It's about ZIP code and genetic code — and all the factors that go in between."

And while genes don't explain all health problems, genetic variations associated with a person's race can play an important role worth exploring equally, he says.

"Having diverse population is really important because genetic variations do differ by population," Denny says. "If we don't look at everyone, we won't understand how to treat well any individual in front of us."

• diversity in medicine
• human genome
• genetic research

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• Published: 12 March 2024

How do cancer research scientists deal with machines and consumables? Exploring research ethics from an inductive ethnographic perspective

• Salaheddine Mnasri   ORCID: orcid.org/0000-0001-5790-6952 1 &
• Fadi Jaber 1  

Humanities and Social Sciences Communications volume  11 , Article number:  392 ( 2024 ) Cite this article

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This paper started from an inductive ethnography conducted within a cancer research lab in Belgium. The primary objective was to explore how researchers make decisions and rationalize their scientific practices. Through data collected from participant observation, interviews, and analysis of research protocols, the study exposes serious knowledge gaps that compromise research ethics. Specifically, the findings reveal the scientists’ need for more understanding of the validity of their lab machines and the readymade consumables procured from external providers. Moreover, without questioning this dependency, our participants (scientists) rely heavily on machines and consumables for almost all their research protocols. The findings suggest that cancer researchers place unjustifiable trust in the lab’s machines and the external providers’ reliability; this compromises the following three fundamental ethical principles: research integrity, responsible conduct, and the responsibility of using resources and technologies.

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Amanda M. Lindkvist, Lina Koppel & Gustav Tinghög

On the need to explicitize the unstated argument in cancer research: an ethnography on scientific argumentation

Salaheddine Mnasri & Marina Jovic

Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia

Yuwan Malakar, Justine Lacey, … Denis C. Bauer

Introduction

Several social scientists and communication scholars have conducted ethnographic studies in the natural habitat of experimental scientists, such as research labs, to provide authentic insights into scientific knowledge construction. How knowledge emerges through the social processes and interactions within research communities is an intriguing question, especially when we view knowledge as a communication construction (Mnasri and Papakonstantinidis, 2023 ) rather than a product in human brains. Unlike essentialists, who focus on the underlying facts that shape human thoughts, social constructionists focus on interactions that shape knowledge (Schudson and Gelman, 2023 ). Following this interest, Latour and Woolgar ( 1979 ) were among the first social scientists who observed research labs and explored the social and cultural factors that shape the production of scientific “facts” (i.e., knowledge). They emphasized the importance of observing scientists in everyday interactions to understand scientific knowledge construction. Latour ( 1987 ) refuted the traditional notion of scientific knowledge as a purely objective and detached pursuit. Latour ( 2005 ) and Callon ( 1986 ), among others, propose the “actor-network theory” (ANT) that explores the importance of understanding the network of actors. To do so, they emphasize the need to trace the whole network of scientists, instruments, materials, and institutions, as well as the complex interactions between these elements and their influence on the production of scientific knowledge. Using ANT, social scientists can explore knowledge construction on the go (in the making), i.e., how knowledge builds up through interactions. From this perspective, ANT offers some visualization of knowledge construction, which remains intangible.

The laboratory environment is crucial for scientific research, innovation, and knowledge construction. As researchers delve into cutting-edge research and push the boundaries of knowledge, several ethical implications and challenges may arise and affect their decision-making (Resnik, 2015 ), necessitating careful observation, consideration, and evaluation. Accordingly, researchers should be aware of possible ethical issues during laboratory experiments and research (Nuffield Council on Bioethics, 2012 ; O’Mathúna, 2007 ; Sugarman and Bredenoord, 2020 ). As an interdisciplinary field, bioethics is pivotal in addressing the ethical aspects of scientific research and knowledge construction. It encompasses various principles and frameworks that guide ethical decision-making in research practices, including respect for autonomy, beneficence, non-maleficence, and justice (Beauchamp and Childress, 2019 ). However, despite established research ethical principles, the laboratory environment presents unique challenges that require continuous attention and adaptation. One critical ethical implication in the laboratory setting relates to the responsible and ethical use of machines and consumables.

Closely observing the practices and interactions within a cancer research lab in Belgium, this paper aims to examine the ethical implications of scientific knowledge construction. In effect, the main objective of this paper is to advocate for responsible and reflective scientific practices from a transdisciplinary stance. Accordingly, the paper adopts an inductive approach within the framework of ethnographic studies as applied to experimental sciences. Initially, the study aimed to provide a detailed and nuanced account of the daily activities and interactions within the cancer research lab. The study provides insights into the research ethical implications embedded in the scientists’ practices and the construction of the lab’s scientific knowledge. However, the analysis responded to the ethical issues from empirical data and findings. Specifically, trust as a fundamental ethical principle compels attention, prompting a central focus on addressing it. This study underscores the suitability of ethnographic research and participant observation in shedding light on previously unexamined aspects of scientific practices. Therefore, we highlight the importance of taking a holistic approach to understanding science in the making, considering the technical and methodological aspects and the transdisciplinary stances to comprehend such multi-faceted matters better.

Trust as fundamental research ethical principle

Ethical principles are crucial in guiding ethical decision-making and responsible conduct of research. Trust emerges as a fundamental ethical principle underpinning the integrity and credibility of the entire process. Since our data analysis explores how lab scientists build trust with lab machines, the following section sheds light on trust’s role in communicating knowledge to the public. Hardin ( 2002 ) worked on trust in scientific research. He made significant contributions to understanding how trust develops in scientific research. Hardin posited that trust should come as a rational choice rather than merely an emotional or irrational belief. Trust in research is a strategic decision based on individuals’ assessments of the credibility and reliability of researchers, institutions, machines, and processes. He emphasized the importance of well-designed institutional structures and mechanisms that promote trust, such as transparent regulations, accountability, and rigorous peer review processes. Hardin also recognized the role of social norms in shaping trust within the research community and highlighted how trust facilitates collaboration, knowledge sharing, and the advancement of scientific research. He acknowledged the significance of public trust in research, emphasizing the need to establish and maintain trust with the broader public to ensure scientific findings’ acceptance and meaningful application.

In the health care context, several declarations (i.e., European Code of Conduct for Research Integrity 2017; Hong Kong Principles 2019; Montreal Statement 2013; Singapore Statement 2010) identified critical ethical principles and outlined the key components of trustworthy research and principles of research integrity. The three fundamental ethical principles identified in the European Code of Conduct for Research Integrity ( 2017 ), Hong Kong Principles ( 2019 ), Montreal Statement ( 2013 ), and Second World Conferences on Research Integrity ( 2010 ) are: research integrity, responsible conduct of research, and responsible use of resources and technologies. The first principle, “research integrity,” emphasizes the importance of honesty, accuracy, and transparency in scientific research. Researchers and scientists should conduct their research and experiments with integrity that ensures the reliability of their findings. The second principle, “responsible conduct of research,” corresponds to the imperative of conducting work in a responsible and accountable manner. This principle entails that researchers adhere to ethical guidelines and regulations, accurately report methods and results, and address conflicts of interest. Finally, the “responsible use of resources and technologies” principle, including funding, laboratory equipment, biological materials, and consumables, is particularly pertinent to any lab environment. Researchers and scientists should use lab resources efficiently and in a manner that benefits scientific progress and the public good.

The central pillar of the three principles mentioned above is trust. Trust serves as the glue that binds scientists, researchers, participants, and the public together, ensuring the validity and ethicality of scientific research. However, scientific research can question trust regarding the relationship between researchers and participants in data analysis, reporting, and communicating scientific knowledge to the public.

Additionally, many articles have been dedicated to the topic of research integrity, covering areas such as the impact of hyper-competitiveness and inadequate training on research quality, the uncritical and ineffective use of metrics in evaluating researchers, and the presence of systematic biases in peer review and publication (Mejlgaard et al., 2020 ). Mark Yarborough ( 2014 , 2021 ) examined the question: Is research routinely conducted ethically? He found that researchers place excessive reliance on professional norms, peer review, research regulations, research integrity programs, and mandatory training in responsible research conduct to establish the trustworthiness of the research community. Consequently, research teams and institutions must implement additional safeguards if they genuinely aim to warrant the public trust they seek when conducting scientific research. This situation reiterates the challenge of ensuring researchers and research institutions adopt a more mindful approach when addressing research improvement and integrity.

Efforts to incorporate ethics into lab research must be actively pursued, implemented, and substantiated by developing appropriate strategies (Bærøe et al., 2022 ; Zwart and Ter Meulen, 2019 ). Accordingly, Buedo et al. ( 2023 ) explored the ethical implications and challenges that researchers who work in biotechnology laboratories may encounter. The authors provided a concrete strategy to promote the integration of research ethics with laboratory practice and to strengthen responsibility in laboratory research. The aim of the proposed strategy was (i) to integrate ethics into laboratory research to identify bioethical problems early, (ii) to create input for normative evaluation and (iii) to establish a research integrity environment. This strategy hinges on three theoretical and practical approaches: (i) Ethics Parallel Research, (ii) Social Labs, and (iii) the Responsible Research and Innovation framework. Ethics Parallel Research (EPR) serves the purpose of ethically guiding the development of biotechnology throughout the process while providing normative evaluation. Social Labs are recognized as practical tools that integrate and drive social change within specific contexts, emphasizing a clear and defined focus. These labs, designed to operate in real-world settings, prioritize practical applications rather than abstract concepts. Responsible Research and Innovation (RRI) offers strategies to proactively anticipate, evaluate, and enhance societal engagement while identifying potential implications (Burget et al., 2017 ). The overarching goal of the RRI framework is to foster inclusivity and sustainability within the research process, promoting a more comprehensive and socially conscious approach.

Buedo et al. ( 2023 ) added that focus group meetings facilitated proactive discussions and fostered the exchange of experiences, uncertainties, and ideas within the research process, specifically for those working in laboratory settings. The assessment of this experience revealed several benefits of integrating ethics within research consortiums, including researchers’ commitment to ethics in their work methods and research objectives, the actions taken following the intervention, and the emergence of supplementary activities resulting from the collaborative generation of ideas and reflections. However, the authors revealed that despite numerous ethics guidelines and increased awareness of research ethics, effectively incorporating them into day-to-day research practices, particularly in laboratory settings, poses a severe challenge (Laas et al., 2022 ; Resnik et al., ( 2023 )).

The objective of the current study is to explore the role of trust between lab scientists and machines and how this relationship unfolds to the public. In this sense, Besley et al. ( 2018 ) claim that scientific trust extends beyond the scientific community to the general public. Effective science communication is crucial in building public trust in research outcomes. In addition, transparent communication about methods, results, and potential implications fosters understanding and confidence in scientific endeavors. On the other hand, several risks may emerge if scientists fail to justify their trust in lab machines. For instance, it may affect the credibility of scientific institutions and organizations and erode public confidence in scientific findings and the scientific community.

Methodology

This paper adopts an ethnographic research design within an oncology research lab to investigate the ethical implications of the scientists’ practices as represented in their knowledge construction. The cancer lab comprises ten postdoctoral and doctoral researchers, technicians, and a principal investigator with a medical background and prior experience at leading institutions. The principal investigator had served as a Postdoctoral fellow and Research Associate at a prominent medical institution in Boston. The lab has 255 publications, owns several patents, and achieved an H index of 81 in 2022. With this strong record of publications, the lab is well-regarded in the field of oncology. It conducts fundamental cancer research (i.e., on cells only, not humans). The data comprises 18 months of direct participant observation, individual and collective interviews, and detailed field notes. The interviews aimed to gather information on how scientists utilize machines and consumables. The first author conducted individual interviews with each participant and a collective interview 6 months later. The individual interviews were analyzed in a synchronic manner, while the collective interview was analyzed diachronically. The data collected through participant observation and interviews were analyzed using qualitative and inductive approaches. The systematic analysis of the data rests on the following ethical principles that are relevant to the laboratory environment: research integrity, responsible conduct of research, and responsible use of resources. Therefore, the study addresses the following research question and its sub-question:

RQ1: How do researchers use the lab machines and consumables in their research practices?

RQ1a: What are the ethical implications of such use of machines and consumables?

To answer the research questions, we designed a semi-structured interview. The first author interviewed each participant. Six months later, he asked the same questions in a collective interview to explore discrepancies and contradictions. The interview comprises five questions: “1) What processes do you typically follow to verify your results?” 2) “Do you trust machines?” 3) “Do you trust products from other companies?” 4) “What is the proportion of operations performed by other parties in your experiments?” 5) “Can you provide a diagram illustrating the different stages or operations in your current research?”

We coded the interviews using the Jefferson system of transcription notation, detailed in Atkinson and Heritage ( 1984 ). The names of the participants have been changed into the following pseudonyms to protect their identity and privacy:

Paul: The lab director/Principal Investigator

Stephan, Daniel, Brian, and Dilara: Postdoctoral researchers

Cédric and Gaelle: PhD researchers

Dalila and Marie: Technicians

Joane: a master’s student

The lab has numerous machines; some are simple, and some are very sophisticated. They range from freezers, hoods, incubators, centrifuges, and the like to smaller tools used in what they call western blotting and the like. In addition to machines, the lab members consume products that range from substances such as medium, trypsin, powders, gels, and the like, to small tools such as pipettes, plates, tips, filters, and many more. These consumables have to be certified, and they have to be purchased from a reliable provider because their chemical composition may influence the cancer cells and, therefore, skew the research results. The consumption of these products is ongoing every second, all around the clock. The lab members are constantly consuming without stopping.

Under the current research design, research operations are only feasible using these products.

Data analysis and discussion

Trusting machines and implications for machine validity.

The lab researchers unanimously said they trust machines almost the same way. The researchers’ responses varied when asked how they ascertained the lab machines’ ability to measure what they initially intended. Some participants admitted to not knowing, while others were able to explain how the machines work, indicating a better understanding of their accuracy. Certain participants minimized the need for such considerations, implying a lack of interest or time to investigate machine performance’s “philosophical” aspects. In this sense, researchers expressed trust in the machines they utilize, asserting that they perform the intended functions correctly and provide accurate measurements. However, their understanding of the machines’ functionality differed. Some participants believed that the machines were appropriately designed and that they functioned as intended. In contrast, others relied on the reputation of the machine providers or the fact that others had already used the machines to validate their trust. In line with Hardin’s ( 2002 ) conception of trust outlined in the previous paragraph, all the researchers’ responses lack rational justification for machine validity.

For instance, Cédric responded to the question “Do you trust machines?” determinedly by saying, “Yeah.” When I asked him, “Is it like you have to trust them, or you need to trust them?” he confirmed his position: “no, they are designed to do what they are doing.” As a doctoral researcher, Cédric seems focused on his research proposal and is not yet well positioned to rethink the machines’ validity.

Paul- the principal investigator- said right from the beginning that he had to trust machines, hence acknowledging that it is a matter of obligation rather than a choice. I explained to Paul that what I meant by the question is whether he is sure that the machine measures what he (as a biologist) wants it to measure, not just that the machine runs well or is well calibrated. He replied: “Yes”. When I explained my idea by saying that machines can be consistent and well-calibrated, but they may not in reality measure what we want to measure, he said:

I don’t ask myself these kind of questions (.) I’m I consider that is being measured is uh reflects the reality (.) I don’t doubt the except if again we have obtained ten times the same thing and the eleventh time we do the experiments we have u:h we have uh bizarre behavior than we could start to check whether is the machine is working properly but we do the maintenance of the machine in order to: to get them: uh as uh as efficient as possible (.) this is uh usually checked for the machine why you have them the highest risk of uh of problems this is checked by companies uh who knows the machine better than we do and who come to validate them using specific QC protocols or quality control protocols (.) so we are prone to believe (.) the machine.

After I explained again to Paul, he almost repeated the same idea: basically that they buy from highly reputable providers and that the machines “have been validated by others uh (.) so uh we have reference on what we should get with ourselves”. Paul, who had already said that he does not ask himself such questions, now told me that he does not purchase black boxes, a term which I did not use myself:

We discuss with colleagues already using the machines so we make our minds in order to (.) finally buy the machine which (.) for which we are convinced that it does measure what we (.) u::h want to s to be measured and then it’s a real it does truly reflect the parameter we we uh which is the one to investigate so we uh uh it’s not like we we’re dealing with black box we’re dealing with machine which which are uh which have been validated by by others uh (.).

Joanne did almost as Paul and Cédric, acknowledging that she is not proficient in these areas. She said that she trusted machines because others also do: “Because I don’t, I don’t really know u::h machines and stuff like that but u::h if it is used by everybody like I have to trust them ((laughter)).” When I tried to explain more, she interrupted me by saying: “[it’s not stupid people who’re making those machines.]” Then again, Joanne insisted that “yes they must produce something it’s like it’s not like u:h for example if you use a ma machine to check the concentration and stuff like that it’s not a stupid thing (.) it’s correct.”

Thus, all the researchers described machines as reliable tools that consistently produce accurate results without recognizing the need for further examination of machine validity. Several participants mentioned that they rely on the views and expertize of their peers or experienced individuals who have used the machines to make informed decisions. They trust these individuals’ knowledge and believe their recommendations would ensure accurate measurements. In contrast, some participants did not consider that machines may not measure what they intended, assuming that the manufacturers or service providers would address potential problems. Additionally, some participants exhibited a simplistic view of knowledge and assumed that answers to complex questions regarding machine accuracy could be easily obtained through documents or by consulting the right person. They may need to fully grasp the complexity of determining whether the machines truly measure what researchers intend to measure.

For example, during the collective interview, the first author mentioned a specific machine—the photo spectrometer - and asked what they knew about how it works. They said they do know and mentioned that it is a matter of different filters and emissions of light that interact with the fluorescent liquid they put on cells before inserting them into the machine. When he asked them to describe how the machine responds to the compounds (the liquid) validly, they described the machine’s technique. Again, they should have noticed the technique’s validity in measuring what they intend to assess (in this case, the density, absorbance or fluorescence of any protein they want to detect). He restated the concern by saying: “This is what I’m saying (.) the machine may be consistent (.) every time you give it this dose it will give this (.) and then you give a higher dose it will give you that (.) and in terms of graphs and colors and shapes it’s consistent no problem (.) but how do you know that it is measuring what you want to measure?”. Here Gaelle responded to the concern. She said: “because if you put water, for example, it will not u:h measure.” Everyone seemed to enjoy this idea and supported it one after the other. However, the first author responded that while the machine may work with cells and not with water, it may not measure the specific aspect of the cell that the scientists target, but rather a different thing. At this point, Stephan’s volume dropped significantly, admitting that the machine may measure other things: “↓ Maybe like contamination or something.” This case shows that the researchers are building their research based on findings from machines they use as black boxes, assuming that they are doing what they want.

Trusting consumables and the associated risks

The lab members consume products ranging from medium, trypsin, powders, gels, and the like, to small tools such as pipettes, plates, tips, filters, and many more. These consumables have to be certified, and they have to be purchased from a reliable provider because their chemical composition may influence the cancer cells and, therefore, skew the research results. The lab members are constantly consuming without stopping. Following the current research design, research operations are only feasible using these products.

Paul first responded to the question: “Do you trust the products that you purchase from providers?” by saying that he trusts providers because it is easy for him to check if the needed protein is in the product that they purchase:

uh uff (.) again we (.) they are products for which it’s quite easy to see whether it works or not (.) if you take an antibody (.) u:h it’s easy for us to see whether it works or not (.) u:h you do the: the experiments (.) you try to detect a protein (.) you know (.) that protein is there or is not there (.) and you know at which size and some characteristic of the protein so (.) if the antibody does not recognize the protein then you know that the antibody does not work (.) u::h having done this kind of excerce:::: exercise several times we know that there are company which are not trustable (.) we know that everybody knows that in in science that if you buy an antibody from a company called ((company name removed)) u:h you have uh much less chance than with any other type of companies that the antibody will work (.) so they are less uh reliable.

I then told him that he only mentioned a case in which he could check and asked him if they could check if the product (a substance) would contain a molecule. At this level, he said that they are not and that they would completely blind themselves: “Yeah, there you are (.) you are blinded (.) you are acting as uh as uh being just prone to believe that uh what you are using is uh doing what you what it is described to do”. Paul now realizes that “trust” can be about other scenarios, such as the one I mentioned, and is not limited to the case he mentioned. Paul’s last account illustrates that their trust is not scientifically based and, therefore, represents a considerable threat to their experiments’ validity. I wanted to make sure that this condition is also applicable to other labs in the world—at least from his point of view- so I said: “and the issue is that that’s (.) consumables are used everywhere in the world it’s not only in this lab so may be if there is a mistake or a problem it would be everywhere so you wouldn’t know (.) nobody would figure it out”. Paul confirmed my statement, by saying: “clearly (.)”.

When I asked Cédric how much of purchased products he uses, he said, “Everything (.) almost”. I asked him to draw me a diagram where he would mention all his current research steps, stating the ones provided and those prepared in the lab. He did so and then confirmed his previous statement again: “Everything is coming from different stuff […] Yeah, everything”. I then asked him to account for the situation of depending on external providers; he said that it is a good situation because in that way, if labs from all over the world purchase from the same provider, then they can be sure that they are using the same products:

For me it’s (.) can be a good thing like the medium (.) it’s a really good thing (.) because that means that everyone in u::h the world (.) can use the same media that you are using (.) because if they just buy from the same provider then you buy it from (.) then they are sure they get the same (.) the same medium that you are getting.

Joanne’s rationale was almost identical to Cédric; she said, “Uhm yes (.) I have to trust them otherwise ((laughter)).” When I told her if the providers would alter the purchased products, would she be able to know, she replied: “I won’t. I won’t know about that (.) but you have to use it”, hence confirming that she is rather obliged to trust, because she has no more options.

Daniel first said that only 30% of the products they use were purchased readymade. While he was drawing the diagram, he realized that it was much more than that, so he kept saying: “A lot of providers ((laughter)) even in this simple u::h experiment,” then “ok (.) ok so they are quite a lot ((laughter))”. When I asked him if he only discovered this situation now, he said: “Yeah (.) u:hm never thought about that but”. Unlike his colleagues, Daniel realized how much they purchase readymade only after drawing the diagram. When I asked him for the first time to account for the situation of depending on providers, he said: “Of course but u:h I hope we can trust otherwise (.) this would change a lot.”

Daniel then confirmed that they do not check after providers and that the results would shift if the products are not as described. Then when I asked him again, he said: “No for me it’s (.) it’s fine because u:h (.) they gi:ve they give us information about the product we:: we want to buy so (.) we know (.) we know what we’re buying so”. Daniel’s latter account justified the situation by saying that providers give them information about what they buy, stressing that they know what they buy. At this level, I asked him: “You know, based on what they say?”. He said: “((laughter)) yeah, but we cannot for everything”, which means that they do not know. Daniel then explained more why they should trust these products: “in these companies, there are: a lot of steps also to check purity quality of the products so”. He confirmed that the trust on which he built his position is entirely out of his control by mentioning what the companies do rather than what he does to ensure he receives exactly what he needs. One more time, I wanted to make sure that the lab does not do anything to ensure that the parameters of its experiments are under its control, so I said: “but it’s a problem because if uh you’re doing research and things are not under your control (.) you’re not 100% sure that these things are correct.”

Gaelle’s was straightforward. She said that they trust providers because they have quality controls: “Yes (.) otherwise we will not ((laughter)) use them (;) and they have to::: take uh check so they have quality control in the industry so:::”. She added: “but they have quality u::h quality test and quality u:::h they have good manufacturing procedures ((laughter))”. She also said that they depend on providers because they cannot do everything by their own: “Yes (.) we do the::: without the: company we can’t do a lot of things”. When I asked Gaelle whether they can check if providers would alter the products, she said: “No we don’t check u::h”. She then said: “uhm (.) but since we cannot check (.) we will not (.) we are not able to check ((laughter))”. I asked if providers are able to totally know their products and if they do would they tell everything to them; she said: “Yes but if they know what they put in (.) they will (.) it’s u::h a nonsense to not tell uh (.) the people (.) who buy it so (.) there is no reason to tell you that it’s the wrong uh”.

The results suggest that researchers typically have confidence in the machines they utilize. However, there is a discrepancy in their comprehension of the machines’ functioning and ability to measure the intended variables accurately. We here refer to all sorts of machines used as black boxes in fundamental research, ranging from centrifuges and microscopes to other tools such as western blotting. Some participants rely on the manufacturers’ reputation, colleagues’ recommendations, or the assumption that the machines are well designed without further questioning. There is a lack of awareness or concern about the potential for machines to produce inaccurate measurements or the need for critical examination of their functioning. The researchers take the machines’ validity for granted and do not question whether they measure what they initially intended. This lack of critical evaluation can lead to potential skew or inaccuracies in research outcomes. Ethically, researchers have a responsibility to ensure the validity and reliability of the tools and equipment they use in their research, as they should be held accountable for the outcomes of their research. While it seems unrealistic for researchers to check or monitor every aspect of their experiments by themselves, they should be aware of the degree to which they do their research. Ongoing situational awareness is crucial in maintaining accountability and understanding the scope of their contributions. By any means, the paper does not blame the researchers. It simply exposes a status quo that the broader scientific community should know. It is worth noting that researchers need more comprehensive awareness and ownership regarding their research in today’s science structure. However, future substantial reforms or profound changes in scientific research practices could mitigate or entirely prevent such a shortcoming.

The findings also highlight a contradiction related to the use of consumables (e.g., medium, trypsin, cell lines, buffers, disks, flasks, filters, and more). On the one hand, the researchers expressed their need to trust providers. On the other hand, they described the potential risks associated with deviations from specified standards and the challenges in detecting and addressing mistakes or inconsistencies in these materials. The findings indicate that before our research, the scientists lacked awareness or concern about the potential risks of total reliance on external providers. Critical examination is necessary to maintain scientific research’s integrity and boost knowledge-building progress. The data analysis also implies complacency or shifting of responsibility onto external providers, assuming that they have no interest in altering the composition of the consumables. Given this fact, researchers are responsible for critically evaluating the materials they use and actively participating in ensuring the validity and reliability of their research. Researchers should have access to accurate and comprehensive information about the materials they use to make informed decisions and mitigate potential biases or inaccuracies in their research.

Machines and consumables are part of the worldwide standards, and if mistakes or problems exist, they are likely to be widespread, making it difficult for individual labs to identify them. In our case, the researchers express trust in machines and external providers, but this trust emanates from subjective value judgments rather than factual accounts. There appears to be a need for more awareness or consideration regarding the possibility of providers intentionally or unintentionally altering the composition of their products. While some participants dismiss this idea, they acknowledge the unknown consequences of such actions (i.e., blinding). Table 1 below summarizes our findings:

Concluding remarks

Upon conducting ethnographic research within the cancer research lab, this study unveils some seen but unnoticed ethical pitfalls that come into play while building scientific knowledge. The findings highlight critical ethical implications in lab research practices. Specifically, they should show a more comprehensive understanding of the machine’s functionality, which is crucial for research integrity. Furthermore, the reliance on external providers without verifying the contents of the consumables raises concerns about the validity of research outcomes. In line with previous ethnographies conducted by Mnasri, Papakonstantinidis ( 2020 ) and by Mnasri and Jovic ( 2023 ) within cancer research labs, this situation poses a risk to research integrity, compromises the responsible conduct of research and questions the responsible use of resources and technologies.

This ethnographic study emphasizes the need for a critical evaluation and a deeper understanding of machine’s validity in research. It also underscores the potential risks and challenges of heavily relying on external providers for consumable materials. The researchers’ trust in machines and consumables emanates from subjective value judgments and assumptions rather than scientific evidence or control over research elements. These conclusions accentuate the importance of critical awareness, evaluation, and consideration of potential risks and uncertainties to ensure the reliability and integrity of scientific research outcomes. It is also worth noting that the current study has at least one limitation. The current study’s small sample size may limit the results’ generalizability to a broader population of cancer research labs. In a single cancer research lab, the number of researchers is relatively small, leading to a limited pool of potential participants for the study.

Data availability

The datasets generated and/or analyzed during the current study are available here: https://doi.org/10.6084/m9.figshare.25325899.v1 .

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Mnasri, S., Jaber, F. How do cancer research scientists deal with machines and consumables? Exploring research ethics from an inductive ethnographic perspective. Humanit Soc Sci Commun 11 , 392 (2024). https://doi.org/10.1057/s41599-024-02920-x

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DOI : https://doi.org/10.1057/s41599-024-02920-x

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Tax Policy and Investment in a Global Economy

We evaluate the 2017 Tax Cuts and Jobs Act. Combining reduced-form estimates from tax data with a global investment model, we estimate responses, identify parameters, and conduct counterfactuals. Domestic investment of firms with the mean tax change increases 20% versus a no-change baseline. Due to novel foreign incentives, foreign capital of U.S. multinationals rises substantially. These incentives also boost domestic investment, indicating complementarity between domestic and foreign capital. In the model, the long-run effect on domestic capital in general equilibrium is 7% and the tax revenue feedback from growth offsets only 2p.p. of the direct cost of 41% of pre-TCJA corporate revenue.

We thank Agustin Barboza, Emily Bjorkman, Walker Lewis, Anh-Huy Nguyen, Shivani Pandey, Sarah Robinson, Francesco Ruggieri, Sam Thorpe, and Caleb Wroblewski for excellent research assistance; our discussants Eyal Argov, Steven Bond, Manon François, Andrea Lanteri, and Jason Furman; and seminar and conference participants for comments, ideas, and help with data. We thank Michael Caballero, Anne Moore, and Laura Power for insights on multinational tax data and Tom Winberry for helpful discussions about his adjustment cost estimates. Chodorow-Reich gratefully acknowledges support from the Ferrante Fund and Chae fund at Harvard University. Zwick gratefully acknowledges financial support from the Booth School of Business at the University of Chicago. Zidar thanks the NSF for support under grant no. 1752431. Disclaimer: All data work for this project involving confidential taxpayer information was done at IRS facilities, on IRS computers, and at no time was confidential taxpayer data ever outside of the IRS computing environment. The views expressed herein are those of the authors and do not necessarily represent the views of the IRS, the U.S. Department of the Treasury, or the National Bureau of Economic Research. All results have been reviewed to ensure that no confidential information is disclosed. The model-implied revenue estimates are not revenue estimates of the TCJA.

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