ethereum research paper

Blockchain smart contracts: Applications, challenges, and future trends

• Published: 18 April 2021
• Volume 14 , pages 2901–2925, ( 2021 )

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• Shafaq Naheed Khan 1 ,
• Faiza Loukil   ORCID: orcid.org/0000-0003-4753-060X 2 ,
• Chirine Ghedira-Guegan 3 ,
• Elhadj Benkhelifa 4 &
• Anoud Bani-Hani 1  

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In recent years, the rapid development of blockchain technology and cryptocurrencies has influenced the financial industry by creating a new crypto-economy. Then, next-generation decentralized applications without involving a trusted third-party have emerged thanks to the appearance of smart contracts, which are computer protocols designed to facilitate, verify, and enforce automatically the negotiation and agreement among multiple untrustworthy parties. Despite the bright side of smart contracts, several concerns continue to undermine their adoption, such as security threats, vulnerabilities, and legal issues. In this paper, we present a comprehensive survey of blockchain-enabled smart contracts from both technical and usage points of view. To do so, we present a taxonomy of existing blockchain-enabled smart contract solutions, categorize the included research papers, and discuss the existing smart contract-based studies. Based on the findings from the survey, we identify a set of challenges and open issues that need to be addressed in future studies. Finally, we identify future trends.

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1 Introduction

For more than a decade, the blockchain is established as a technology where a distributed database records all the transactions that have happened in a peer-to-peer network. It is regarded as a distributed computing paradigm that successfully overcomes the issue related to the trust of a centralized party. Thus, in a blockchain network, several nodes collaborate among them to secure and maintain a set of shared transaction records in a distributed way without relying on any trusted party. In 2008, Satoshi Nakamoto introduced Bitcoin [ 69 ] that was the first proposed cryptocurrency introducing the blockchain as a distributed infrastructural technology. It allowed users to transfer securely crypto-currencies, known as “bitcoins” without a centralized regulator. Besides, Ethereum [ 16 ], NXT [ 71 ], and Hyperledger Fabric [ 4 ] were also proposed as blockchain-based systems used for the cryptocurrency. Unlike Bitcoin, they can use smart contracts (SC). Blockchain technology overlaps traditional contracts by including the terms of agreements between two or more parties, but surpasses them thanks to smart contracts by automating the execution of agreements in a distributed environment when conditions are met.

Smart contracts are executable codes that run on top of the blockchain to facilitate, execute, and enforce an agreement between untrustworthy parties without the involvement of a trusted third-party [ 16 ]. Smart contracts gave network automation and the ability to convert paper contracts into digital contracts. Compared to traditional contracts, smart contracts enabled users to codify their agreements and trust relations by providing automated transactions without the supervision of a central authority [ 89 ]. In order to prevent contract tampering, smart contracts are copied to each node of the blockchain network. By enabling the execution of the operations by computers and services provided by blockchain platforms, human error could be reduced to avoid disputes regarding such contracts.

Although smart contracts have made progress in recent years, they still face many challenges. For instance, one infamous malicious attack took place in 2016 when the Decentralized Autonomous Organization (DAO) smart contract was manipulated to steal around 2 Million Ether Footnote 1 (50 Million USD on the time) because of its re-entrancy vulnerability [ 103 ]. In addition to the vulnerability problem, smart contracts face several challenges including privacy, legal, and performance issues.

To understand current topics on smart contracts, we conduct a comprehensive survey, with the aim of better identifying and mapping research areas that need further studies. The focus of this survey is studying smart contracts from the technical point of view (e.g., codifying, security, performance issues) and the usage point of view (e.g., smart contract applications in finance, healthcare, etc). The major contributions of this paper are summarized as follows:

We propose a taxonomy of studies based on blockchain-enabled smart contracts including two categories, namely SC improvement and SC usage.

We categorize 200 papers that we have extracted from different digital databases and discuss the existing smart contract-based studies.

Based on the findings from the survey, we identify a set of smart contract challenges and open issues that need to be addressed in future studies. Therefore, this survey provides a helpful reference to the researchers who want to target smart contract improvement or usage in their future studies.

Finally, we discuss future trends of smart contracts and explain how they provide better solutions to the open research challenges.

Considering the above contributions, the remainder of this paper is structured as follows. Section  2 discusses background information about blockchain and smart contracts technologies. Section  3 discusses existing reviews studying smart contract-based approaches. Section  4 describes the adopted survey methodology and the solution taxonomy used to categorize existing smart contract-based solutions. In Sections  5 – 8 , we present existing advances in modeling-driven smart contract improvement, optimization-driven smart contract improvement, resource-driven smart contract usage, and cross-organizational collaboration-driven smart contract usage. Section  9 discusses the study results by introducing challenges and future trends in the studied field. Finally, Section  10 concludes the paper.

2 Background

As aforementioned, blockchain technology has emerged as a distributed computing paradigm that successfully overcomes the problem related to the trust of a centralized party. Thus, in a blockchain network, several nodes collaborate among them to secure and maintain a set of shared transaction records in a distributed way without relying on any trusted party. Specific nodes in the network known as miners are responsible for adding new blocks to a distributed public ledger known as the blockchain.

The first system was Bitcoin [ 69 ], which allowed users to transfer securely the currency (bitcoins) without a centralized regulator. In the blockchain network, miners are responsible for collecting transactions, solving challenging computational puzzles (proof-of-work) in order to reach consensus, and adding the transactions as blocks to the blockchain. Since then, several blockchain-based development platforms have been proposed offering the ability to host/ use smart contracts to execute automatically events and actions., namely NXT [ 71 ], Ethereum [ 16 ], Hyperledger Fabric [ 4 ], etc.

We detail below the smart contract operational process and then discuss some blockchain platforms that support the development of smart contracts.

2.1 Operational process of smart contracts

A smart contract is a common agreement between two or more parties. It stores information, processes inputs, and writes outputs thanks to its pre-defined functions [ 16 ]. For instance, the smart contract can define the constructor function that enables the smart contract creation. Hosting a new smart contract in the blockchain is enabled by invoking the constructor function through a transaction, whose sender becomes the smart contract owner. A self-destruct function is another example of the functions that can be defined in a smart contract. Usually, only the smart contract owner can destruct the contract by invoking this function.

A smart contract is likely to be a class that includes state variables, functions, function modifiers, events, and structures [ 16 ] which is intended to execute and control relevant events and actions according to the contract terms. Besides, it can even call other smart contracts. Each smart contract includes states and functions. The former are variables that hold some data or the owner’s wallet address (i.e., the address in which the smart contract is deployed). We can distinguish between two state types, namely constant states , which can never be changed, and writable states , which save states in the blockchain. The latter are pieces of code that can read or modify states. We can distinguish between two function types, namely read-only functions , which do not require gas Footnote 2 to run and write functions that require gas because the state transitions must be encoded in a new block of the blockchain. Furthermore, paying currency is required to avoid infinitely smart contract runs.

As aforementioned, a smart contract is hosted in the blockchain by invoking its constructor function through a transaction submitted to the blockchain network, then the constructor function is executed, and the final code of the smart contract is stored on the blockchain. Once deployed, the creator of the smart contract got the returned parameters (e.g., contract address), then users can invoke any available smart contract’s function by sending a transaction.

2.2 Platforms for Smart Contracts

Smart contracts can be developed and deployed in different blockchain platforms (e.g., NXT, Ethereum, and Hyperledger Fabric). Several platforms offer distinctive features for developing smart contracts including contract programming languages, contract code execution, and security levels. Some platforms support high-level programming languages to develop smart contracts.

Bitcoin [ 69 ] is a public blockchain platform that can be used to process cryptocurrency transactions, but with a very limited computing capability. Bitcoin uses a stack-based bytecode scripting language. The ability to create a smart contract with rich logic using the Bitcoin scripting language is very limited. Major changes would need to be made to both the mining functions and the mining incentivization schemes to enable smart contracts proper on Bitcoin’s blockchain [ 52 ].

NXT [ 71 ] is an open-source blockchain platform that relies entirely on a proof-of-stake consensus protocol. It includes a selection of smart contracts that are currently living. However, it is not Turing-complete, meaning only the existing templates can be used and no personalized smart contract can be deployed.

Ethereum [ 16 ] is the first blockchain platform for developing smart contracts. It supports advanced and customized smart contracts with the help of a Turing-complete virtual machine, called the Ethereum virtual machine (EVM). EVM is the runtime environment for smart contracts, and every node in the Ethereum network runs an EVM implementation and executes the same instructions. Solidity, as a high-level programming language, is used to write smart contracts, and the contract code is compiled down to EVM bytecode and deployed on the blockchain for execution. Ethereum is currently the most popular development platform for smart contracts and can be used to design various kinds of decentralized applications (DApps) in several domains.

Rather than the public blockchain, such as Bitcoin and Ethereum that any party can participate in the network, Hyperledger Fabric [ 4 ] is permissioned with only a collection of business-related organizations can join in through a membership service provider, and its network is built up from the peers whose are owned and contributed by those organizations. Hyperledger Fabric is an open-source enterprise-grade distributed ledger technology platform, proposed by IBM and supports smart contracts. It offers modularity and versatility for a broad set of industry use cases. The modular architecture for Hyperledger Fabric accommodates the diversity of enterprise use cases through plug and play components.

Ethereum and Hyperledger Fabric smart contracts differ in multiple aspects. While Solidity is the well-known programming language used to write Ethereum smart contracts, Hyperledger Fabric supports multi-language smart contracts, such as Go, Java, and Javascript [ 4 ]. For contract code execution, the contract code in Ethereum is included in a transaction, which is propagated in the peer-to-peer network, and any miner that receives this transaction can execute it in its local virtual machine [ 16 ]. In Hyperledger Fabric, when a transaction is created by the application, the transaction is only executed and signed by specified peers (endorsing peers). After receiving the application’s transaction proposal, each of these endorsing peers independently executes it by invoking the chain-code to which the transaction refers [ 4 ]. For security, chaincode runs within a container environment (e.g., Docker) for isolation.

These blockchain-based development platforms are used in the existing studies that we detail in the following sections.

3 Related literature reviews/surveys

We provide a brief overview of the existing reviews that have studied blockchain-enabled smart contracts.

While several literature reviews/surveys are published in order to study the blockchain-enabled smart contracts, there are still some ongoing challenges that have not been addressed. Table  1 presents a comparative summary of the existing blockchain-enabled smart contract reviews/surveys according to six criteria, namely proposing a taxonomy, considering several blockchain platforms, considering application domains, covering smart contract improvement tools, identifying research gaps, and scope of literature review. We observe that there is a lack of taxonomy focusing on smart contract improvement (i.e., addressing smart contract security, privacy, and performance issues) and smart contract usage (i.e., addressing domain-specific issues).

To sum up, it can be said that the existing surveys concerning blockchain-enabled smart contracts focus on classifying the papers based on smart contract issues. Our work extends the existing surveys by studying the smart contract application domains, analyzing the smart contract challenges, and introducing some research gaps that need to be addressed in future studies.

4 Research Methodology and Solution Taxonomy

We describe below the adopted research methodology, such as the search strategy, filtering process, and inclusion and exclusion criteria. Besides, we present the solution taxonomy used to categorize the final set of included papers.

4.1 Systematic Literature Review

We used three existing databases, namely ScienceDirect, IEEEXplore, and ACM Digital Library to search for relevant works using the “smart contract” string keyword. In the first phase, we found 523 publications as shown in both Fig.  1a , which depicts the percentage of the acquired research paper per digital database as well as Fig.  1b , which depicts the total number of preliminary studies acquired from each digital database.

Publication trend

To choose the relevant papers to be analyzed in our review, we filtered the primary studies retrieved from the databases. To do so, we defined a set of inclusion and exclusion criteria, which are summarised in Table  2 . Based on the outcomes of the first phase, we applied the set of inclusion and exclusion criteria to exclude the publications considered outside the scope of this review. Thus, we only included studies that satisfy all the inclusion criteria. We excluded duplicate publications, surveys, and literature reviews by filtering studies based on the title, the abstract, and the list of keywords.

As a result of the filtering process, we excluded 323 publications and included 200 relevant publications for this systematic review. Figure  1b depicts also the number of the relevant studies included in this research from each digital database.

4.2 Publication trends and Categorization

To examine the trend of the smart contract field in terms of the publication date, Fig.  2 depicts the number of included studies published each year from 2015 to September 2020. We observe that the total number of published papers in the studied field increases in the past few years, indicating the importance of the topic. Thus, the smart contract field is rapidly growing in recent years.

Included articles per year

As a result of an in-depth analysis of the included studies in this review, a comprehensive taxonomy is constructed to provide an additional support for designers to understand the various dimensions that they have to consider when designing a smart contract. The major motivations of this survey are to identify (i) the main publications about smart contracts, (ii) the current state of research in this field, and (iii) possible gaps in the literature that could become research problems to be solved by the scientific community. Through this survey, we aspire not only to define the conceptual background of blockchain-enabled smart contracts, but also to identify research issues to be explored at new studies. Indeed, we categorize existing smart contract research into two major categories, namely smart contract improvement and smart contract usage. The former includes studies aiming at addressing the smart contract challenges, such as functionality verification, performance, vulnerabilities, and lack of trustworthy data feeding. The latter includes studies aiming at addressing domain-specific challenges using smart contracts. Figure  3 depicts the proposed taxonomy of blockchain-enabled smart contracts, including modeling-driven smart contract improvement (see Section  5 ), optimization-driven smart contract improvement (see Section  6 ), resource-driven smart contract usage (see Section  7 ), and cross-organizational collaboration-driven smart contract usage (see Section  8 ).

Taxonomy of blockchain-enabled smart contract based studies

5 Modeling-driven smart contract improvement

Smart contracts have suffered from multiple security vulnerabilities in the past few years [ 8 ], which have resulted in both theft and gigantic financial losses. Such vulnerabilities could have been avoided with the help of formal analysis and verification of such smart contracts before deploying them on the blockchain. Since existing programming languages, such as Solidity are not built for formal verification, several researchers have proposed alternative approaches in order to improve the smart contract functionality verification. In this category, we discussed modeling-driven smart contract improvement solutions, which can be categorized into programming-centric solutions (see Table  3 ) and formal verification-centric solutions (see Table  4 ).

5.1 Programming-centric solutions

The essence of a smart contract is the computer code that can be executed automatically on the computer, so programming smart contracts correctly is an important research direction. Several researchers argued that developing new contract languages is an effective way to write a correct smart contract. Table  3 presents some newly proposed programming languages such as SmaCoNat [ 78 ], Flint [ 83 ], and Scilla [ 85 ]. For instance, Regnath and Steinhorst [ 78 ] proposed a human-readable, security, and executable programming language, called SmaCoNat. The authors converted programming language grammar into natural language sentences in order to improve program readability.

New contract languages promised to address the existing domain-specific language vulnerabilities. However, since they have not been put into practice, they could have their vulnerabilities. Thus, designing and implementing secure smart contracts still require adaptive software engineering technologies and expertise from multiple research domains, such as networking, programming languages, formal methods, and cryptography.

5.2 Formal verification-centric solutions

Typically, formal testing is applied to ensure that a software behaves and performs as expected in its specifications and requirements based on all possible inputs’ conditions. For smart contracts, Truffle [ 93 ], is an example of a development framework for Ethereum that enables writing formal test cases based on certain mathematical logic and rules for smart contracts written in JavaScript or Solidity languages. These test cases can be written in JavaScript and can be executed on a test network to check several properties of smart contracts. As aforementioned, formal testing can only make sure that a smart contract did what it is supposed to do based on its specification, however, it cannot help the smart contract developers to find bugs or vulnerabilities. Therefore, automated formal verification is a promising approach to detect bugs and other errors to guarantee the functional correctness of smart contracts. According to [ 2 ], formal verification can provide the highest level of confidence in the correct behavior of smart contracts. At present, the use of formal methods to verify smart contracts has been widely adopted by several researchers, and significant results have been achieved in practice. Table  4 presents some formal verification-centric solutions. For instance, Amani et al. [ 2 ] extended an existing EVM formalization in Isabelle/HOL by a sound program logic at the level of bytecode. The principle of the method is to organize the bytecode sequences into linear code blocks and create a logic program, where each block is processed as a set of instructions. Each part of the verification is validated in a single trusted logical framework from the perspective of bytecode.

Currently, formal verification tools are still in the experimental stage and have not been widely used. Therefore, the smart contract formalization research direction deserves a lot of attention, thus it provides the highest level of confidence about the correct behavior of smart contracts. Real progress in this research field can improve trust in the smart contract, especially when used to develop critical systems, such as financial, healthcare, and banking systems.

6 Optimization-driven smart contract improvement

Smart contracts have emerged as a new promising solution for developing fully decentralized applications without involving a trusted third-party. Despite the bright side of smart contracts, several concerns continue to undermine their adoption, namely performance issues, security threats, and privacy issues. Indeed, new smart contract applications are more demanding in terms of contract execution time, execution cost, security, and privacy fields. In this category, we discuss optimization-driven smart contract improvement solutions, which can be categorized into performance optimization-centric solutions (see Table  5 ) and security optimization-centric solutions (see Table  6 ).

6.1 Performance optimization-centric solutions

Smart contract performance refers to the ability of smart contract systems to deliver in a reasonable response time and sustain performance when the number of contracts is increasing [ 1 ]. Table  5 presents some examples of performance optimization-centric solutions. Some performance issues in blockchain systems, not limited to, are throughput bottleneck, limited scalability, transactions latency. To overcome performance issues in smart contract systems, some researchers have proposed solutions to execute smart contracts in parallel instead of sequentially [ 26 , 34 ]. For instance, Gao et al. [ 34 ] have proposed a parallel execution scheme that relies on two key techniques, namely a fair contract partition algorithm leveraging integer linear programming to partition a set of smart contracts into multiple subsets, and a random assignment protocol assigning subsets randomly to a subgroup of users. Other studies have been proposed for smart contract optimization by saving gas. In fact, if the smart contract execution exceeds an amount of gas (known as gas limit), an out-of-gas exception is raised, interrupting the current execution. For instance, GasReducer [ 18 ] is a tool for automatically detecting EVM operation sequences that can be replaced with other operations that have the same semantics but need less gas, and then replacing them with efficient code.

6.2 Security optimization-centric solutions

Security of a smart contract refers to its robustness against attacks from malicious users that exploit generally the contract security vulnerabilities to gain profit or the lack of trustworthy data feeding to inject malicious data. Table  6 presents some examples of vulnerability detection tools, transactional privacy models, and trustworthy data feeding solutions.

6.2.1 Vulnerability Detection

Discovering potential vulnerabilities in the execution of contracts is important to improve the security and credibility of contracts. Indeed, several studies systematically summarized the contract vulnerabilities and analyzed the security risks [ 8 , 77 , 81 ]. For instance, Atzei et al. [ 8 ] have provided a taxonomy of smart contract vulnerabilities of three levels, namely Solidity, EVM, and Blockchain. In recent years, the most notorious attack is the Decentralized Autonomous Organization (DAO) attack that exploited a re-entrancy vulnerability to steal around 2 Million Ether from a smart contract [ 103 ]. Another attack has happened to the SmartBillions, which presented a fully decentralized and transparent lottery system when an attacker successfully manipulated the block hash of the smart contract’s lottery function twice, and forced the result in his favor to get 400 Ether [ 62 ]. To solve the smart contract vulnerabilities, several vulnerability detection solutions have been proposed. Some studies have given solutions to common vulnerabilities, such as Oyente [ 59 ], SmartInspect [ 15 ], and ContractFuzzer [ 47 ]. Some other work focused on specific vulnerabilities, such as ReGuard [ 54 ] to detect re-entrancy bugs and EthRacer [ 48 ] to detect event-ordering bugs.

6.2.2 Transactional privacy

The privacy issue represents a real challenge for smart contracts to keep critical functions secret, apply cryptography, and avoid disclosing data on the blockchain to the public. The lack of transactional privacy could limit the adoption of smart contracts. To address this issue, Kosba et al. [ 49 ] have proposed Hawk, a decentralized smart contract system. Hawk is a tool allowing smart contract developers to build privacy-preserving contracts without the need for implementing any cryptography. Its compiler automatically generated an efficient cryptographic protocol where contractual parties interact with the blockchain, using cryptographic primitives such as zero-knowledge proofs.

6.2.3 Trustworthy data feeding

The smart contract execution requires some external data about real-world states and events from outside the blockchain. Therefore, trustworthy data feeding mechanisms (known as Oracles) are required to build a bridge between blockchain and the external world (e.g., Web API). For instance, Zhang et al. [ 113 ] have proposed Town Crier, which acted as a link between existing commonly trusted non-blockchain based websites and smart contracts to provide authenticated data to smart contracts while preserving confidentiality with encrypted parameters. However, in case of malicious code or bad data fed to a smart contract, the latter processes the input as is, producing an incorrect and unpredictable outcome. Thus, oracles retain an enormous amount of power over smart contracts in how they are executed because the data they provide determines how the smart contracts execute.

To sum up, research on improving smart contract security and performance has emerged in recent years. While running smart contracts in parallel can speed up contract execution, it faces a challenge in how to execute contracts that depend on each other at the same time. Moreover, optimizing smart contract codes can effectively reduce potential vulnerabilities in contracts and ensure efficient and secure execution of contracts. However, the existing studies are still immature, and unknown vulnerabilities or bugs cannot be detected to be replaced. Thus, the optimization of smart contracts needs further research.

After discussing the smart contract from the technical point of view, we present in the following two sections the existing solutions focusing on smart contract usage in several domains.

7 Resource-driven smart contract usage

As we know, smart contracts are executable code hosted in the blockchain that store information, process inputs, and write outputs thanks to their pre-defined functions. They are used to improve data handling transparency, decentralize resource-constrained device management, and enable changes of the agreement terms at runtime while running on top of a decentralized and transparent network. In this category, we discuss resource-driven smart contract usage solutions, which can be categorized into data management-centric solutions (see Table  7 ), device management-centric solutions (see Table  8 ), and cloud-related solutions (see Table  9 ).

7.1 Data management-centric solutions

In the past, raw data are transferred to a cloud server to be stored and analyzed. However, this centralized solution has caused serious concerns regarding several aspects, such as the necessity to trust the cloud infrastructure security, control loss once data are externalized, and lack of data handling transparency. Consequently, blockchain-based data management emerged as a platform to facilitate transparent data transactions between untrustworthy involved parties on the network. Indeed, peer-to-peer-network-based data management is a more fair system as compared to a system where all transactions are handled by a central server. Table  7 presents some examples of data management-centric solutions concerning data provenance, data access, and data sharing.

7.1.1 Data Provenance

Data provenance refers to a historical record of the data and its origins showing which and how data item is stored, accessed, and processed by whom and for what purpose. Ensuring data provenance can increase data transparency and enforce data integrity. In this regard, a blockchain can offer an immutable storage of records and smart contracts can be used as a responsible for verifying the data origins before storing them. Similar ideas are applied in [ 6 , 44 ], where a blockchain is used as a decentralized and immutable storage for enabling data provenance. For instance, Javaid et al. [ 44 ] have proposed a blockchain-based data provenance and integrity for secure IoT environments framework, called BlockPro. Ethereum and two smart contracts were used to implement it. The first smart contract established data provenance by interacting with the IoT devices and making sure they are legit and the data being uploaded is coming from a known and trusted origin. The second smart contract can only be called by the first one to storing data on and retrieving data from the blockchain.

7.1.2 Data Access

Data access is ensured according to rights given to involved parties in a network to perform some operations on data. These rights are expressed using access control policies, which consist of a set of conditions that are evaluated against the current context to make the access decision each time a request is received [ 40 ]. Recently, for obtaining decentralized self-evaluating policies, access control policies have been codified as executable code and have been managed through a peer-to-peer network while eliminating a central entity. For this purpose, smart contracts can be used to express access control policies to transform the policy evaluation process into a distributed smart contract execution. In this context, several studies [ 36 , 61 , 74 , 88 , 112 , 114 ] have been proposed. For instance, Maesa et al. [ 61 ] proposed to exploit a blockchain to store access control policies and manage attributes, as well as to execute the access decision process. The access control policy is represented through a smart contract that evaluated the stored conditions to make the access decision.

7.1.3 Data Sharing

Data sharing refers to make data available to other parties by the data owner. However, two types of challenges faced data sharing schemes, namely (i) achieving good data sharing while losing the control over the shared data or (ii) remaining poor at sharing in order to keep strong control over the data. To address these challenges, blockchain technology is used because it offers immutable storage of records that improve data handling transparency and can host executable codes (i.e., smart contracts) that authenticate users, verify authorizations, and thereby ensure an efficient and secure data sharing in a peer-to-peer network. Several studies using blockchain-enabled smart contracts have been proposed for data sharing in healthcare [ 22 , 72 , 111 ], cloud environment [ 70 ], and for digital document version control [ 30 ]. In the healthcare context, medical devices and health care applications have been increasingly adopted by patients. However, wireless body sensors collect health records that are sensitive to individuals. Existing electronic health record management systems struggle with balancing data privacy and data access. Blockchain technology is an emerging technology that enables data sharing in a decentralized and transactional fashion. For instance, Dagher et al. [ 22 ] have proposed a blockchain-based framework, called Ancile for secure and efficient access to medical records by patients, providers, and third-parties while preserving the patients’ privacy. Ancile employed smart contracts, data obfuscation techniques, and cryptographic techniques in order to improve privacy and security in the healthcare domain. Recently, Yu et al. [ 111 ] have proposed a blockchain-based medical research support platform, which employed the characteristics of the alliance chain on which hospitals and medical research institutions are treated as nodes. Among them, users such as patients, doctors, and researchers needed to register and authenticate on the alliance chain. Smart contracts are used to upload the pseudonymous addresses of CEMRs to the alliance chain.

7.2 Device management-centric solutions

One of the technical challenges of having billions of devices deployed worldwide is the ability to manage and synchronize them. Using the current model of the server-client system may have some limitations for device management thus, several researchers are studying the benefits of the blockchain use in this field. Specifically, smart contracts are chosen to guarantee authentication, synchronization, and data integrity while running on top of a decentralized and transparent network. Table  8 presents some newly proposed device management-centric solutions [ 29 , 45 , 58 , 90 , 96 , 104 , 116 ]. For instance, Ellul and Pace [ 29 ] have proposed a split-virtual machine architecture to enable the integration of resource-constrained devices with blockchain systems, called AlkylVM. Each blockchain-connected device would run an instance of AlkylVM, which allows communication between blockchain and IoT devices using the Aryl blockchain node. The latter is responsible for monitoring smart contract transactions and events that would require interaction with IoT devices.

7.3 Cloud-related solutions

In cloud computing, both service requester and service provider agree on a set of requirements, obligations, and rights that is valid for the whole contract life-cycle. Recently, blockchain-enabled smart contracts have been used to enable changes in the agreement terms at runtime through the definition of conditions and actions. Table  9 presents some proposed cloud-related solutions [ 42 , 84 , 98 , 118 ]. For instance, Zhou et al. [ 118 ] have proposed a witness model for enforcing cloud Service Level Agreement (SLA) using smart contracts. The game theory is leveraged to analyze that the witness has to offer honest monitoring service in order to maximize its revenue. The service provider needs to prepay fees to the smart contract for hiring witnesses. The service customer then decides whether to accept the SLA. If yes, it also needs to prepay fees including the service fee and its part of the hiring fee for witnesses. However, a small bug or attack on smart contracts can result in significant issues like privacy leakage or system logic modifications. Some of critical security vulnerabilities can include timestamp dependencies, mishandled exceptions, re-entrancy attacks on smart contracts in cloud-related solutions.

Although smart contracts fulfill many conditions related to data/device management, they have some drawbacks, based on basic design principles of blockchain technology. First, the data stored on smart contracts are publicly readable through public transactions with no read access restrictions. Thus, it is required to avoid storing private or device keys on smart contracts to the public availability of the information. For solving transparency problems related to blockchain, future research might investigate deploying complex cryptographic solutions for securing data stored on smart contracts without boosting cost. Second, the cost of storing data on the blockchain is very high. Therefore, creating hybrid solutions is required to benefit from the traceability of data transactions that are offered by blockchain networks and the efficient and private access and storage of data provided by external data repositories.

After discussing the resource-driven smart contract usage solutions, we present in the following section the existing solutions focusing on cross-organizational collaboration-driven smart contract usage.

8 Cross-organizational collaboration-driven smart contract usage

Smart contracts help to record an agreement between several untrustworthy parties in the form of code that cannot be altered or changed once deployed on the blockchain. Thus, smart contract development allows substituting traditional contracts and develops business growth in several industries, namely supply chain management, logistics and shipping, insurance, and charity. In this category, we discuss cross-organizational collaboration-driven smart contract usage solutions, which can be categorized into profit-centric solutions (see Tables  10 ,  11 , and  12 ) and non-profit-centric solutions (see Table  13 ).

8.1 Profit-centric solutions

The smart contract protocol aims at making contracts more secure, executed in real-time, and more transparent, which are the exact challenges with the existing profit-centric cross-organizational collaboration. Profit-centric solutions aim at increasing the profit by reducing real-time tracking costs, improving cross-border payments, and enhancing distributed problem-solving transparency. Tables  10 ,  11 , and  12 present some examples of profit-centric solutions concerning tracking-based solutions, digital asset-based solutions, and crowdsourcing-related solutions, respectively.

8.1.1 Tracking-based solutions

Although business processes may operate well within a centralized mechanism managing internal activities with individual local databases, there still exists a demand for transparency across processes and trust relationships among involved parties. Indeed, real-time tracking may reduce the unnecessary wait for the confirmation of information. Thus, using a distributed system can enhance the transparency and performance of business processes. Smart contracts can be used to automate the transfer of various types of ownership of assets, property, and value and therefore, lead to more visible and less-intermediated working processes. In this context, several studies using smart contracts have been proposed for supply chain management of foods [ 11 , 17 , 53 ], manufactured products [ 24 , 43 , 50 , 102 ], shipped items [ 39 ], bio-drugs [ 105 ], and imported products [ 108 ]. For instance, Casado-Vara et al. [ 17 ] have proposed a model for agriculture tracking involving blockchain, smart contracts, and a multi-agent system. The blockchain is used to store all transaction information in the supply chain. Besides, the multi-agent system used smart contracts to manage the entire supply chain process more efficiently while removing intermediaries. Furthermore, according to industry estimations, the global halal food market will reach USD 2.55 trillion by 2024 [ 92 ]. Thus, several companies are using blockchain to improve traceability in the halal food supply chain. For instance, a UK based company has partnered with a blockchain platform provider in order to track livestock and fresh food from farm to table through the halal food chain using the blockchain technology [ 92 ].

8.1.2 Digital asset-based solutions

Because of their resilience to tampering, smart contracts are appealing in many scenarios, especially in those which require transfers of money to respect certain agreed rules like in financial services. Therefore, smart contracts in the finance application domain manage, gather, and/or distribute the money as a preeminent feature. The lack of a centralized authority reduced costs and in theory provided more control and access to the investors [ 46 ]. To this end, some smart contracts are used for cross-border payments without relying on banks. For instance, the blockchain payment provider, called Ripple is a blockchain solution for payments that is proven in the real world by connecting existing bank ledgers to facilitate near real-time cross-border payments. Ripple may also reduce costs and provide additional pricing transparency of real-time cross-border payments [ 3 ]. Table  11 presents other smart contracts that implemented data/good trading service [ 7 , 65 , 106 ], insurance service [ 9 ], rent/exchange good service [ 14 , 28 ], energy trading and demand management service [ 55 , 100 ], social credit system [ 107 ], and mobile payment system [ 110 ]. For instance, smart contracts are exploited in the insurance industry to automate claims processing, verification, and payment, thus to increase the speed of claim processing as well as to prevent fraud and reduce manual mistakes. Recently, a smart contract-based flight insurance system has been proposed to refund automatically the insured passengers in case of a flight delay [ 13 ]. Moreover, blockchain-based systems can provide solutions to the cyber insurance challenges by realizing an automated, real-time, and immutable feedback loop between the insurer, its customer, and potential auditors [ 20 ]. Moreover, blockchain technology can mitigate the problems faced by traditional insurance while complying with religious principles [ 67 ]. Indeed, a smart insurance model based on Islamic insurance, called Takaful is proposed in [ 64 ]. The main difference between Takaful and conventional insurance that in Takaful, insured funds belong to them, the insurance company is just a manager. Thus, by using blockchain and smart contract technologies, insurance companies can be more transparent, which is the highest feature requested by customers. The authors in [ 64 ] have suggested transforming the traditional insurance policies into smart contracts that can be executed automatically in order to refund the policyholders without causing compensations for fake incidents.

8.1.3 Crowdsourcing-related solutions

Crowdsourcing is an online, distributed problem-solving and production model in which individuals or organizations obtain goods and services from a large group of participants. For instance, crowdfunding has become one popular form of collective funding among several categories of crowdsourcing. Crowdfunding is a process, in which small donations or investments, made by groups of people, support the development of new projects in exchange for free products or different types of recognition. Traditional crowdsourcing is based on a central system where requesters post tasks on a central server or platform, however, this centralized model currently faces various challenges such as prohibitive cost, single point of failure, and vulnerability to malicious attacks. To this end, blockchain is considered as a promising technology that aims at addressing the aforementioned challenges by eliminating the single point of failure, enhancing transparency, and enforcing rules using smart contracts. In this context, several studies using blockchain-enabled smart contracts [ 38 , 87 , 97 , 119 ] have been proposed, as shown in Table  12 . For instance, Zichichi et al. [ 119 ] have proposed a smart contract-based social decentralized autonomous organization for crowdfunding, called LikeStarter where social network site users can raise funds for other users through a simple “like”, built on top of the Ethereum blockchain. Smart contracts are used to control and manage funds without the need for a trusted third entity. LikeStarter assigns Likoins (i.e. tokens related to an artist) to users that fund a given project. These tokens can be employed and converted to buy artifacts and they provide users with voting capabilities (i.e. they can contribute to the decision of the price of certain artifacts).

8.2 Non-profit-centric solutions

Blockchain technology is needed in a cross-organizational collaboration area suffering from a decline in trust from involved parties (e.g., volunteers, donors, voters, etc.) who are unable to know how their contributions are spent/handled. Indeed, smart contracts enable “fully auditable” performance data, which is secure and extremely difficult to falsify or hack.

Table  13 presents some examples of non-profit-centric solutions including volunteer system [ 19 ], philanthropic-related systems [ 32 , 82 , 91 , 94 ], e-voting service [ 75 ], system for educational institutions [ 86 ], and copyright protection [ 115 ]. For instance, Cheng et al. [ 19 ] have proposed VOLTimebank, a volunteer time bank system for a mutual pension based on blockchain and smart contracts. VOLTimebank provides a channel for volunteers to serve the elderly and gives volunteers a way to exchange the services they can offer today with the services that they hope to get in the future. In the philanthropy context, the collection processes are not transparent, and due to this, the involved organizations struggle to gain donors’ trust and interest. Thus, some efforts have been made to map the charity collection process on blockchain technology, for instance, Farooq et al. [ 32 ] have proposed a charity collection platform, which is based on blockchain technology, and is transparent for donors and legal authorities to conduct an audit. The design uses smart contracts and digital wallets to transfer money in real-time with complete data security and an auditable trail of every transaction. These smart contracts have been introduced to securely transfer donations to individual beneficiaries, organization, and their associated projects. Zhao and O’Mahony [ 115 ] have proposed BMCProtector, a prototype implementation based on an Ethereum blockchain and smart contract technologies, for effective protection of music copyright and rights of copyright owners. The deployed smart contract is responsible for sharing the copyright parameters.

Despite the benefits of blockchain and smart contracts in reforming operations in a wide variety of industries, namely supply chain, insurance, and charity, certain challenges to their widespread adoption still exist. These challenges include legal issues, lack of standards and protocols, privacy issues, and error intolerance. Arguments that smart contracts are no panacea for all financial use cases doubt the applicability of smart contracts to certain scenarios as far as agreement type and scale.

9 Discussion

We discuss below the study results and present challenges and future development trends in smart contract research.

9.1 Challenges and Open issues

As an emerging technology, smart contracts currently face many challenges, such as legal, reliance on “off-chain” resources, immutability, scalability, and consensus mechanism issues (see Fig.  4 ).

Challenges and open issues

9.1.1 Legal issues

The legal issue of smart contracts is another crucial aspect of smart contract challenges. For example, the European General Data Protection Regulation (GDPR) [ 35 ] stipulates that citizens have a “right to be forgotten” which is inconsistent with the immutable nature of blockchain-enabled smart contracts. Other legal issues can be cited including, (i) each country has its own laws and regulations, hence, it is complicated to ensure compliance will all regulations, (ii) law clauses or conditions are not quantifiable, thus it is still complicated to model these conditions in smart contracts so that they are appropriate and quantifiable for a machine to execute them, and (iii) governments are interested in a regulated and controlled use of the blockchain technology in many applications, however, this means that the untrustworthy network will regress to a third-party trusted network, losing part of its essence [ 79 ].

9.1.2 Reliance on “off-chain” Resources

Several smart contracts require receiving information or parameters from resources that are not on the blockchain itself, so-called off-chain resources. For this purpose, oracles are used as trusted third parties that retrieve off-chain information and then push that information to the blockchain at predetermined times. Although existing oracles are well tested, their use may introduce a potential “point of failure”. For instance, an oracle might be unable to push out the necessary information, provide erroneous data, or go out of business. Therefore, smart contracts will need to account for these eventualities before their adoption can become more widespread [ 51 ].

9.1.3 Immutability issue

The immutability feature is an important characteristic of smart contracts. Indeed, once a smart contract is deployed, the code cannot be changed by any party. However, the dark side of the immutability concept in smart contracts lies mainly in the fact that in the event of any errors made in the code, the immutability feature of a smart contract prevents it from being rectified. Similarly, if circumstances change (e.g., the parties have mutually agreed to change the parameters of their business deal, or if there is a change in law, etc.), no simple path to amend a smart contract is possible. Therefore, extensive and possibly expensive reviews of the smart contract performed by experts before its deployment in a blockchain are required to address the immutability issue.

Another limitation in the blockchain itself that impacts the smart contracts is the irreversible nature of the blockchain, thus once the smart contracts are deployed, they cannot be changed. Moreover, any blockchain nodes can be hacked or misused to report erroneous data that will be logged on the blockchain in an immutable manner.

9.1.4 Scalability issue

Scalability is the primary concern for many blockchain networks. For instance, the Ethereum blockchain can verify 14 transactions per second, which is slow as compared with Visa that can handle up to 24,000 transactions per second. Indeed, the scalability issue leads to network congestion, increased commission fees for transactions, and an increase in the time required to confirm the transactions [ 80 ]. In order to address the scalability issue, extensive research focusing on increasing the number of transactions per second by smart contract platforms is required in the future. However, the transaction verification depends on the consensus mechanism used by the smart contract platforms. Therefore, scalability depends on consensus mechanisms, which is another issue in smart contracts.

9.1.5 Consensus mechanism issue

The consensus mechanism plays the leading role to maintain security, scalability, and decentralization in the blockchain networks at the same time. There are several existing consensus algorithms, including Proof-of-Work (PoW), Proof-of-Stake (PoS), etc. Although the PoW algorithm enables security in the blockchain, it wastes resources. Thus, many organizations switch from the PoW algorithm to new consensus mechanisms that promise lower fees for transactions as well as lower energy costs for the block production process. Therefore, future studies can use new consensus mechanisms, such as proof-of-activity (PoA) or delegated proof-of-stake (DPoS) in order to test them and eventually improve their quality.

9.2 Future Development Trends

Future development trends of smart contracts are introduced from two aspects namely, Layer 2 protocols, and contract management solutions.

9.2.1 Layer 2 protocols

In order to address the aforementioned challenges faced by smart contracts, a viable solution, called Layer 2 is appeared to tackle the blockchain scalability problem. While Layer 1 is the used term to describe the underlying main blockchain architecture, Layer 2 is an overlaying network that lies on top of the underlying blockchain. Indeed, Layer 2 refers to the multiple solutions or protocols being built on top of an existing blockchain system. The main goal of Layer 2 protocols is to solve the transaction speed and scaling difficulties that are being faced by the major cryptocurrency networks. Therefore, Layer 2 protocols refer to a secondary framework, where blockchain transactions and processes can take place independently of Layer 1 (“main-chain”). Two major examples of Layer 2 solutions are the Bitcoin Lightning Network [ 27 ] and the Ethereum Plasma [ 76 ]. The Lightning Network, which in part developed at the MIT Media Lab’s Digital Currency Initiative, is a lightweight software solution for scaling public blockchains and cryptocurrency interoperability. It aims at greatly reducing cost and time constraints by shifting small transactions to a cryptographically secure “off-chain” environment so that only large netting transactions need to be directly settled into a resource-constrained blockchain [ 27 ]. Ethereum Plasma is a series of smart contracts, which allows for many blockchains within a root blockchain. The root blockchain enforces the state in the Plasma chain. The root chain is the enforcer of all computation globally but is only computed and penalized if there is proof of fraud. Many Plasma blockchains can co-exist with their business logic and smart contract terms. Indeed, Plasma enables persistently operating decentralized applications at a high scale [ 76 ]. To sum up, thanks to Layer 2, a great portion of the work that would be performed by the “main-chain” can be moved to the second layer. So while the “main-chain” provides security, the second layer protocols provides better solutions for the scalability issue by offering high throughput, being able to perform hundreds, or even thousands, of transactions per second.

9.2.2 Contract management solutions

Smart contracts encompass far more than just the benefits of blockchain technology. Rather, the term captures the entire digital life cycle of a contract, from negotiation to control and verification of the fulfillment of contractual obligations. Now, it is already possible to use smart contracts even without blockchain technology. Thus, contract management solutions could overcome both the immutability issue and the irreversible nature of blockchain by handling the contract’s life-cycle while eliminating limitations of the technology itself. In state-of-the-art contract management solutions [ 31 ], all parties to the contract must provide proof of identity and authenticate their access to data in order to ensure the basis of trust. Besides, all documents that are associated with the contract are stored in a revision-secure manner and encrypted form on a cloud-based platform developed and operated in Europe. This ensures transparency and traceability for all events, the actions associated with these events, and the designation of the persons responsible [ 23 ]. For instance, Fabasoft Contracts [ 31 ] is one of the latest contract management solutions that is ready-to-use, cloud-based software to support users throughout the entire contract life cycle: from cross-company contract preparation, efficient handling of review and approval processes, to the revision-secure contract archiving. It enables the modeling of contract rights and obligations, which can be automatically verified and enforced. There are several benefits offered by revision-secure contract management, including providing traceability when monitoring the cold chain of food delivery or proving the authenticity of spare automotive parts, as opposed to counterfeit articles [ 23 ].

10 Conclusion

The decentralization, auto-enforcing ability, and verifiability characteristics of smart contracts enable their encoded business rules to be executed in a peer-to-peer network, where each node is “equal” and none has any special authority without the involvement of a trusted authority or a central server. Thus, smart contracts are expected to revolutionize many traditional industries, such as financial, healthcare, energy, etc. In this paper, we presented a comprehensive survey of blockchain-enabled smart contracts from both technical and usage points of view. Thus, we introduced a taxonomy of existing blockchain-enabled smart contract solutions, categorized the included research papers, and discussed the existing smart contract-based studies. Based on the findings from the survey, both smart contract challenges and open issues are identified to be addressed in further studies. Finally, we discussed future trends of smart contracts. This study provides informational support to stakeholders interested in the research of smart contracts.

Ether (ETH): the cryptocurrency of Ethereum apps that is digital, global money.

gas: a unit that measures the amount of computational effort that it will take to execute certain operations.

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Psycholinguistic and emotion analysis of cryptocurrency discourse on X platform

• Moein Shahiki Tash 1 ,
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• Zahra Ahani 1 &
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This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our goal was to conduct a psycholinguistic and emotional analysis of social media content associated with these cryptocurrencies. Such analysis can enable researchers and experts dealing with cryptocurrencies to make more informed decisions. Our work involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns emerging in each coin’s community. To achieve this, we utilized advanced text analysis techniques. Additionally, this work unveiled an understanding of the interplay between these digital assets. By examining which coin pairs are mentioned together most frequently in the dataset, we established co-mentions among different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from X. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable perception into the linguistic nuances of various digital coins’ online communities and provides a deeper understanding of their interactions in the cryptocurrency space.

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Introduction

How people employ words daily can reveal a wealth of information about their beliefs, fears, thought processes, social connections, and personal characteristics 1 . Nowadays, online social media platforms significantly affect human life, and people freely pen their thoughts on social networks 2 . Furthermore, the extensive use of social media platforms has been instrumental in spreading awareness about groundbreaking projects. The proliferation of digital technologies has been facilitated by the process of globalization 3 . Owing to cryptocurrencies’ digital character, wide-ranging conversations occur in online forums and on social media platforms, including X (formerly known as Twitter) and Facebook. These platforms serve as significant determinants of the prevailing sentiment among the general public regarding cryptocurrencies and to a certain extent, influence their market valuations 4 , 5 . Notably, these networks are home to a huge user base, encompassing billions of individuals and a vast, intricate web of interconnected relationships among them 6 .

On January 29, 2021, Elon Musk, the world’s wealthiest individual at that time 7 , took a surprising step by adding the hashtag #bitcoin to his X bio. This unexpected move triggered an outpouring of excitement and prompted a surge in cryptocurrency enthusiasts rushing to buy Bitcoin. Remarkably, this seemingly minor action had a significant impact, quickly driving the price of Bitcoin from around $32,000 to over $38,000, ultimately leading to a remarkable increase of $111 billion in the cryptocurrency’s market capitalization 8 .

The majority of data produced on social networks is unstructured, making it challenging to quantify. As a result, it is typically analyzed using various characteristic features 9 . In the above instance, we observed a significant role that social media plays in shaping the cryptocurrency market.

Prior research in the field of cryptocurrency and blockchain technology has explored a wide range of subjects and methodologies. For instance, scholars have utilized natural language processing (NLP) techniques to analyze various aspects such as miner extractable value (MEV) in social media discussions 10 . Similarly, others developed strategies for maximizing wealth through Initial Coin Offerings (ICOs) in blockchain ventures 11 . Additionally, there have been endeavors to predict cryptocurrency prices and investigate the societal ramifications of these emerging technologies in contemporary business environments 12 . However, very few studies have addressed the psycholinguistics and emotions associated with the discourse on cryptocurrencies in social media. To contribute to these existing avenues of inquiry, our study aims to bridge a significant gap in the literature. Specifically, we intend to conduct psycholinguistic and emotion analyses, alongside with assessing the readability of cryptocurrency comments on social platforms, with NLP methods. By adopting this innovative approach, we seek to get more knowledge of the psychological and emotional dimensions in cryptocurrency discourse, which have thus far received limited attention in scholarly research. Our objective in this endeavor is twofold: to enhance awareness among newcomers in digital marketing to prevent misguided investments, and to offer support to traders who rely on metrics such as the fear and greed index in their trading strategies.

We analyzed nine distinct digital coins using psycholinguistic methods to assist cryptocurrency enthusiasts. The cryptocurrencies examined in this paper encompass Bitcoin 13 , Ethereum 14 , Ripple 15 , Binance 16 , Dogecoin 17 , Shiba 18 , Fantom 19 , Matic 20 , and Cardano 21 . Psycholinguistics is the examination of how linguistic elements and psychological aspects are interconnected. It is important to emphasize that we did not consider user-specific characteristics; our primary focus was solely on textual data. To clarify, we utilized psycholinguistic attributes that often convey the underlying meaning communicated by text. The text analysis we conducted comprises the following categories:

1. LIWC (Linguistic Inquiry and Word Count) 22

2. Sentiment analysis 23

3. Emotion analysis 24

4. Assessment of readability 25 , 26 , 27 , 28

Concerning the features we used for our computerized text analysis, first, we employed subcategories of LIWC 29 . We utilized only a selection of such subcategories, including Analytical Thinking, Clout, Drives, Affect, Money, Hope, Attention, Netspeak, and Filler. This internal dictionary encompasses an extensive compilation of more than 12,000 words, word stems, phrases, and specific emoticons. Each dictionary entry is associated with one or more categories, or subdictionaries, strategically designed to evaluate a wide range of psychosocial constructs 22 .

Investors typically initiate an assessment of public sentiment surrounding a particular cryptocurrency before making investment decisions 30 . Consequently, sentiment and emotion analysis in cryptocurrency markets has gained significant prominence 31 . Research indicates that tweets expressing positive sentiments can exert a substantial influence on cryptocurrency demand, and conversely, negative sentiments can have a similar effect 32 , 33 .

Readability refers to the level of ease with which a piece of writing can be understood or comprehended, primarily influenced by its writing style and presentation 34 . Readability not only relates to how easily a text can be understood with respect to its writing style but also takes into account how well readers comprehend it, read it at an appropriate speed, and find it engaging 35 .

Moreover, we went a step further by investigating the “reasons and significance” aspect. In simpler terms, we sought to determine which characteristics among the aforementioned four hold more importance for novice investors. To accomplish this, we explored the following research questions.

RQ1: Do psycholinguistic characteristics vary among digital coins?

RQ2: What are the dominant feelings expressed by X users regarding the cryptocurrencies under study?

RQ3: Does the readability level of tweets exhibit uniformity across all selected digital currencies?

RQ4: Is there any co-mention among different cryptocurrencies?

To address these research inquiries, we analyzed tweets related to nine distinct cryptocurrencies. We conducted psycholinguistic investigations and emotion analysis to respond to RQ1, RQ2, and RQ3 and extracted the above categories of features from the dataset, including LIWC, Readability, Sentiment, and Emotions analysis. To answer RQ4, we established a co-mention among different cryptocurrency coins, identifying which two coins tend to be mentioned together more frequently.

Related work

A cryptocurrency is a form of digital currency designed for use as a means of exchange. It relies on robust cryptographic techniques to secure financial transactions, regulate the creation of additional units, and validate asset transfers 36 . Because of their substantial market values, cryptocurrencies have gained considerable interest, with some individuals regarding them as legitimate currencies and others as attractive investment prospects 33 .

Sentiment and emotion analysis

Aslam et al. 37 focused on sentiment analysis and emotion detection in cryptocurrency-related tweets collected using specific hashtags such as ’#cryptocurrency’, ’#cryptomarket’, and ’#Bitcoin’, amassing a total of 40,000 tweets. The authors employed traditional feature extraction methods like Bag-of-Words (BoW), TF-IDF, and Word2Vec, along with machine learning models including Random Forest (RF), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Logistic Regression (LR). Additionally, they leveraged advanced deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to classify tweet sentiments as positive, negative, or neutral. Notably, they introduced an ensemble model that merges LSTM and Gated Recurrent Unit (GRU) 38 , 39 , achieving remarkable accuracy scores of 99% for sentiment analysis and 92% for emotion prediction.

Research in Ibrahim et al. 40 centered on predicting early market movements of Bitcoin by harnessing sentiment analysis of X data 41 , 42 . The primary objective of their work was to introduce a Composite Ensemble Prediction Model (CEPM) built upon sentiment analysis. They employed a combination of data mining techniques, machine learning algorithms, and natural language processing to decipher public sentiment and mood states pertaining to cryptocurrencies. The research evaluated various models, such as Logistic Regression, Binary Classified Vector Prediction, Support Vector Machine, Naïve Bayes, and a single XGBoost 43 for sentiment analysis. The remarkable point to be highlighted is the CEPM’s outperformance of other approaches, demonstrating its effectiveness in forecasting early Bitcoin market movements via the analysis of sentiment in X data.

Shahzad et al. 44 presented a framework for performing sentiment analysis on X data with the aim of predicting the future price of Bitcoin. They highlighted the significance of NLP in bridging the gap between human communication and digital data and emphasized the growing importance of sentiment analysis in the field. The authors utilized three artificial intelligence tools, namely, LR, LSTM, and Deep Neural Network (DNN) Regressor to evaluate their performance in predicting Bitcoin prices. The best performance was demonstrated by the LSTM model.

Rahman et al. 45 explored the usage of various natural language processing models for sentiment analysis in the context of cryptocurrency and financial market prediction. They used a dataset of approximately 100,000 news items, including tweets and Reddit posts, gathered from 77 public X timelines and Reddit subreddits over a six-month period from July to December 2021. The study also examined the creation of ensemble models, encompassing all 22 selected models as well as a subset of the top three models labeled as “ensemble (all) “ and “ensemble (top 3) “, which included Aigents+, Aigents, and FinBERT. The “ensemble (top 3)” method exhibited a higher degree of correlation with other models compared to the rest.

Huang et al. 46 collected a substantial dataset comprising 24,000 cryptocurrency-related tweets and 70,000 comments from Sina-Weibo using specific keywords. The study adopted a methodology that utilized a training dataset consisting of posts from the top 100 crypto investor’s accounts on Sina-Weibo over the most recent seven days, while the subsequent day’s posts served as testing data. Remarkably, their sentiment analysis approach based on LSTM surpassed the time series auto-regression (AR) method by 87.0% in precision and 92.5% in recall.

The authors of 47 aimed to detect sentiment and emotion in X posts and utilized this information for recommendations. They used a dataset containing tweets and user data and manually annotated 7,246 tweets and replies. Their approach involved text preprocessing and applying a Naïve Bayes classifier with cross-validation. The findings demonstrated that analyzing the entire text provided superior accuracy compared to focusing on specific words (NAVA). Moreover, as the number of cross-validation folds increased, the accuracy showed improvement. Specifically, in the realm of emotion analysis, the Naïve Bayes classifier achieved an accuracy of 47.34%. Furthermore, in sentiment analysis, Naïve Bayes outperformed other classifiers significantly, attaining an accuracy of 66.86%.

The researchers in 48 utilized the AIT-2018 dataset 49 to construct a model for detecting emotions expressed in tweets. The dataset of tweets was acquired through the X API by extracting tweets containing emotion-related hashtags such as ’#angry’, ’#annoyed’, ’#panic’, ’#happy’, ’#love’, and ’#surprised’. The proposed model integrated lexical-based approaches, employing emotion lexicons like WordNet-Affect and EmoSenticNet, along with supervised classifiers to autonomously classify multi-class emotions from the dataset. The authors conducted experiments employing three machine learning classifiers: Naïve Bayes, DT, and SVM. Their findings demonstrated that when filtering tweets using EmoSenticNet words, the precision in detecting emotions significantly improved. Specifically, the SVM classifier achieved a high precision rate of 89.28% in the Anger class, surpassing previous results obtained using logistic regression.

Psycholinguistic analysis

Psycholinguistics utilizes various methods to comprehend language in the context of psychological processes. These methods encompass observational research, analysis, experimental studies, and the application of neuroimaging techniques 50 . Researchers also make use of text analysis models to interpret findings related to the language system. This section explores the methodologies employed by researchers in this field. Butt et al. 29 presented a comprehensive analysis of the psycholinguistic aspects of rumors on online social media (OSM). Using the PHEME dataset 51 , which encompasses nine breaking news events, the researchers examined source tweets (rumor and non-rumor) and response tweets. They integrated various psycholinguistic features, including LIWC, SenticNet 52 , readability indices, and emotions to uncover user behavior patterns. Rumor source tweets were found to be characterized by language related to the past, prepositions, and motivations associated with reward, risk, and power. In contrast, non-rumor source tweets exhibited affective and cognitive processes, present-oriented language, and motivations linked to affiliation and achievement. Emotional analysis revealed that non-rumor tweets tended towards neutrality, while rumor-source tweets evoked fear and grief, subsequently prompting anger and fear in reactions.

Narman et al. 53 reported an analysis of Reddit comments employing seven readability techniques to discern the education levels of users interested in eight cryptocurrencies. The data collection process involved gathering comments data from subreddits of eight cryptocurrencies by selecting ten to seventy top posts for each coin to collect distinct usernames. For education level information, they used Reddit.com to gather and categorize the collected comments data. The analysis was performed using seven text readability techniques. Interestingly, the results indicate that a majority, approximately 60%, possess an education level equivalent to middle school, with 30% at the high school level, while the remaining 10% span other educational levels.

The researches in 54 aimed to assess the readability of tweets for English language learners. Their task involved collecting a dataset of 14,659 tweets and obtaining readability judgments from participants representing different language groups. For methodology, they analyzed various linguistic and content-related factors in the tweets, including emojis, hashtags, mentions, and links, as well as traditional readability measures like Flesch Reading Ease and Dale-Chall scores. The results revealed that demographic factors, such as language proficiency and education, were stronger predictors of tweet readability than any other single feature.

The proposal in 55 included a framework to analyze linguistic features and cultural distinctions in climate-related tweets from the UK and Nigeria. A dataset of 81,507 English-language tweets was collected, comprising 44,071 from the UK and 37,436 from Nigeria. The study combined transformer networks with linguistic feature analysis, including the application of the (LIWC-22) software 56 ,version 15.0, to address small dataset limitations and identify cultural differences 22 . Findings reveal that Nigerians tend to use more leadership language and informal words in climate change discussions on X, emphasizing the urgency of the issue. In contrast, UK discourse on climate change is characterized by more formality, logic, and longer words per sentence. The study also confirmed the geographical attribution of tweets using DistilBERT 55 , achieving an 83% accuracy rate.

This section provides a detailed overview of the data acquisition processes we employed. We clarify the exact steps undertaken during preprocessing and explore the complexities of conducting co-mention analyses among various cryptocurrency coins.

Data collection

The data collection process commenced with the acquisition of X data pertaining to nine popular cryptocurrencies: Cardano, Bitcoin, Binance, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple 57 . These specific cryptocurrencies were selected for inclusion in the dataset due to their widespread usage across various research studies conducted by different scholars 18 , 19 , 20 , 58 , 59 . This endeavor yielded a substantial dataset comprising 832,559 tweets spanning from September 2021 to March 2023. After undergoing essential preprocessing steps, the dataset available for analysis was refined, resulting in a curated dataset consisting of 115,899 tweets. Table  1 presents dataset statistics both before and after preprocessing. Additionally, it lists the names of the coins and their respective symbols, which we utilized as keywords for extracting tweets from X. This extraction process was conducted separately for each coin, using both the name and the symbol as search criteria.

Data preprocessing

The utilization of the Tweepy 60 API was instrumental in our tweet data collection procedure, as it empowered us to filter tweets according to diverse criteria, including date, location, language, and various tweet attributes, for example, the number of retweets. In the final phase, we focused exclusively on English-language tweets, excluding unnecessary fields such as ’username’, ’id’, ’date’, ’likeCount’, and ’retweetCount’ retaining only the actual tweet content. After obtaining the dataset, we conducted a multi-step data preprocessing procedure to refine and enhance the data. This procedure involved the following key steps:

URL Removal: We applied a regular expression pattern to identify and subsequently remove any URLs. Text Cleaning: This step included the removal of special characters, such as punctuation marks, with the assistance of a designated dictionary of special characters. Additionally, we excluded words that had a length less than or equal to two characters. The result was a cleaned version of the text data.

Data labeling

In the process of data labeling, we examined each tweet systematically, with the primary objective of identifying any references to the selected cryptocurrencies. Notably, the search encompassed both “Bitcoin” and “Btc” in a case-insensitive manner, with any discovery leading to the classification of the tweet as Btc. This procedure was iteratively applied to all cryptocurrencies listed in Table  1 , encompassing both their complete nomenclature and associated abbreviations.

Further, we encountered instances where tweets discussed multiple cryptocurrencies simultaneously which was uncovered as co-mention among these cryptocurrencies. The results of this co-occurrence analysis are considered in Section 3.4. To tackle this challenge effectively, a comprehensive set encompassing the names of all pertinent cryptocurrencies was devised. For instance, to annotate tweets as of Bitcoin, tweets mentioning any other cryptocurrency present in this predefined set were systematically excluded. The set itself comprised a roster of cryptocurrency names, notably including “Cardano”, “Ada”, “Fantom”, “Ftm”, “Matic”, “Shiba”, “Shib”, “Dogecoin”, “Doge”, “Ripple”,“Xrp”, “Ethereum”, “Eth”, “Binance” and, “Bnb“.

Subsequently, the inclusion of both Bitcoin and Btc into this enumerated list facilitated the resolution of similar issues encountered with other cryptocurrencies, with the same process being replicated across each cryptocurrency to ensure comprehensive data labeling. As an example, in the dataset, a tweet was identified as featuring the keyword Matic. The content of the tweet is provided below: APompliano Good day sir, I have 100$ to invest in a coin right, small but what I can afford for now. So, Im thinking It I should rather go for $ada $matic, $doge What do you suggest fam.

This tweet was acquired using the keyword Matic, and the keywords to be examined for the Matic coin included:’Cardano’, ’Ada’, ’Fantom’, ’Ftm’, ’Bitcoin’, ’Btc’, ’Shiba’, ’Shib’, ’Dogecoin’, ’Doge’, ’Ripple’, ’Xrp’, ’Ethereum’, ’Eth’, ’Binance’ and, ’Bnb’. The exclusion criterion described above ensured that if any of these keywords were present in a tweet, except for the keywords related to the specific coin for which we used keywords to extract tweets, that tweet should be removed. In our example, it’s evident that the tweet contains both ’ada’ and ’doge’ keywords, indicating that it should be removed.

Figure  1 illustrates the processing steps for a tweet.

Example tweet processing.

Cryptocurrencies co-mention

During the labeling process, we examined co-mention and co-occurrence among various cryptocurrencies in tweets. Such analysis resulted in an interesting observation: multiple cryptocurrencies often co-occurred in the same tweets, indicating a significant level of co-mention, which led us to reconsider our labeling model, as previously detailed.

In this section, we explore specific, noteworthy co-mentions among cryptocurrency coins. These co-mentions provide valuable information for our investigation, enhancing our understanding of the relationships and trends emerging in the cryptocurrency ecosystem as reflected in social media discourse. The co-mention matrix provided in Table  2 serves as a tool for assessing the relationships between different cryptocurrencies, particularly concerning their trends and market dynamics, as opposed to a sole focus on price movements. An illustrative example lies in the substantial positive co-mention of Bitcoin (Btc) and Ethereum (Eth) in 53.52% of tweets. This significant co-occurrence indicates that when Bitcoin undergoes an upward trend, or garners increased market attention, Ethereum frequently follows suit. This co-occurrence can be attributed to the prominent positions both cryptocurrencies occupy in the market, as well as to their substantial influence on overall market sentiment.

In contrast, co-mention values nearing 0% in Table  2 signify a lack of substantial co-occurrence among cryptocurrencies. This absence of mention underscores the potential for diversification strategies for designing a cryptocurrency portfolio. Cryptocurrencies exhibiting low or negative co-mention can be strategically employed to diversify a portfolio, potentially resulting in reduced overall portfolio risk. Conversely, cryptocurrencies demonstrating high positive co-mention may offer limited diversification benefits as they tend to move in sync with one another.

Inside the domain of cryptocurrency portfolio management and risk mitigation, these co-mention observations underscore the critical importance of accurate asset selection and allocation, especially in light of the observed co-mention among cryptocurrencies. Such strategic decision-making becomes paramount in achieving diversified and risk-optimized cryptocurrency portfolios.

In this research, we examined cryptocurrency data, concentrating on a specific group of cryptocurrencies. Our choice of these particular coins was driven by their significant popularity among users, as well as the limited availability of substantial data for other coins. To interpret the data, we applied four analytical methods explained in section " Introduction ". Here we present the outcomes of our analysis for each of the aforementioned cryptocurrencies. The selection of features was made considering their past influence 29 , 61 . In the analysis conducted, LIWC assessments were applied to nine cryptocurrencies, resulting in an extensive collection of nine distinct analyses. We selected values that were highly informative for extracting linguistic interpretations relevant to cryptocurrencies. Our choice was made to capture key aspects of sentiment, linguistic style, and thematic content pertinent to discussions around cryptocurrencies. By narrowing down our focus to these particular features, we aimed to mine information from the psychological and linguistic dimensions of cryptocurrency discourse, thus aligning analysis with our goals. these categories encompass analytical thinking (metric of logical, formal thinking), clout (language of leadership), drives (related to personal motivations and psychological desires), affect (linguistic expressions associated with emotional and affective states expressed by a given text), money (refers to a set of linguistic cues or indicators related to financial terms, wealth, and economic aspects, Want (a human ability that allows individuals to envision future events with flexibility), attention (crucial subset of the “Perception” category), netspeak (represents a subset of the conversational category) and filler (non-essential sounds, words, or phrases, commonly used in speech to fill in pauses and maintain the flow of conversation without altering its meaning). In the drives and affect categories, additional features will be elaborated upon in the following discussion. Our examination indicated that Fantom attracts a larger number of tweets centered on technical aspects and holds a higher level of trust in comparison to other cryptocurrencies. For Binance, our observations revealed that the tweets predominantly revolve around themes of affiliation, achievements, and the pursuit of power and wealth. This pattern in discussions on Binance suggests a focus on notable accomplishments and financial success, indicative of a unique narrative and sentiment surrounding the coin. For Matic, the tweets primarily center around emotional impact compared to other cryptocurrencies. This emphasis on affective responses suggests that the coin is particularly influenced by emotional novelty. This distinctive characteristic could be considered a contributing factor to the fluctuations in the coin’s price, as emotional sentiment plays a significant role in shaping market dynamics and investor behavior. Our analysis revealed that Dogecoin exhibits a higher prevalence of netspeak, the informal language commonly used on the internet, compared to other cryptocurrencies. Conversely, Ethereum appears to attract more attention relative to other coins. This distinction suggests that Dogecoin is characterized by a more casual and internet-centric communication style, while Ethereum stands out for its ability to capture increased Attention and interest. A deeper understanding of the communication dynamics and community sentiment surrounding different coins may aid investors in making more informed choices, aligning their investment strategies with the unique qualities and trends associated with each cryptocurrency. From an emotional perspective, most cryptocurrencies exhibit a generally moderate and harmonious emotional profile. Notably, there is a distinct focus on the emotional category of Anticipation, with Dogecoin taking the forefront in this aspect. In this context, Anticipation likely signifies the expectation or excitement surrounding the future prospects, developments, or events associated with these cryptocurrencies.The outcomes of our analysis are presented in Table  5 . In terms of readability, the analysis revealed that Dogecoin’s tweets are relatively more challenging to read and comprehend, as indicated by lower scores on the Flesch Reading Ease measure. The Flesch-Kincaid and Dale-Chall Measures suggest an average reading difficulty level akin to content tailored for college graduates. Conversely, Ethereum’s tweets, as per the Gunning Fog Index, demand a higher level of reading proficiency, indicating a more complex and advanced readability suitable for individuals with a college-level education and vocabulary. To explore additional results, refer to Figs. 5 and 6 s, as well as Table  6 .

The LIWC model revolutionized psychological research by making the analysis of language data more robust, accessible, and scientifically rigorous than ever before. LIWC-22 examines over 100 textual dimensions, all of which have undergone validation by esteemed research institutions globally. With over 20,000 scientific publications utilizing LIWC, it has become a widely recognized and trusted tool in the field 62 giving way to novel approaches in analysis 63 , 64 . Although LIWC provides several benefits, it has its limitations. One drawback is its dependence on predefined linguistic categories, which might not encompass nuances and variations present in natural language. Furthermore, LIWC may encounter challenges in accurately deciphering sarcasm, irony, and other subtle forms of language usage, potentially resulting in text misinterpretation.

To effectively convey the outcomes of our analysis, average values among all the tweets were computed for each of LIWC categories. Averages can help identify broadscale sentiment trends over time. By tracking changes in average scores across key linguistic categories, such as sentiment, emotion, or cognitive processes, one can observe shifts in user sentiment and attitudes towards cryptocurrencies, market developments, or external events. Therefore, the average was calculated by summing up the scores of all comments related to each coin for each LIWC feature and then dividing by the total number of comments for that coin. These computed averages provide information along the linguistic and psychological dimensions intertwined with the selected digital currencies. A comprehensive presentation of these average values for each category can be found in Table  3 .

Analytical thinking and clout

Analytical Thinking, when showing high numerical values, signifies a formal, logical, and hierarchical thought process. Conversely, lower numbers suggest a more informal, personal, present-focused, and narrative style of thinking 65 . The values of this category computed for tweets related to cryptocurrency, reach their highest average score of 67.76 in texts mentioning Fantom. This fact indicates that, on average, discussions in this domain exhibit a relatively high level of logical and formal thinking. Conversely, the lowest average score of 52.00 was found for Ripple, which might suggest that discussions concerning this particular cryptocurrency place slightly less emphasis on logical and analytical thinking compared to the cryptocurrency domain’s average.

Clout is one of the four summary variables in LIWC designed to assess the degree of confidence and certainty conveyed in the text 66 , 67 . Our analysis revealed that the cryptocurrency Fantom exhibits a relatively high Clout score, with an average result of 70.91. This suggests that discussions and conversations related to Fantom often convey a strong sense of confidence and certainty. This high Clout score may also indicate a substantial degree of assurance in Fantom stability. In contrast, the cryptocurrency Ripple demonstrates a comparatively lower Clout score with an average result of 43.39. Figure  2 presents a comparative evaluation of Analytical Thinking and Clout scores across different cryptocurrencies. This suggests that discussions related to Ripple may not consistently display the same level of confidence and certainty found in the Fantom discussions. In essence, when Fantom demonstrates higher Clout values, it signifies that the users who composed the tweets are expressing increased confidence. This, in turn, leads us to infer a heightened level of knowledge on their part. In both analyses, we observed that Fantom consistently had the highest scores, indicating a higher level of analytical thinking and confidence in discussions related to it. Conversely, Ripple consistently had the lowest scores in both categories, suggesting a relatively lower emphasis on analytical thinking and a lower degree of expressed confidence in discussions related to it. While these observations suggest a correlation between analytical thinking and confidence in these specific cryptocurrency discussions, it’s important to note that correlation does not imply causation. Other factors, such as market conditions, community sentiment, and news events, can also influence these results. For example, when we examined Binance, we foound that it ranks as the second-highest in terms of Analytical Thinking scores among the various cryptocurrencies. However, when we assess it as the position in the Clout category, Binance ranks fifth. The results of Analytical Thinking and Clout analysis related to digital currencies can be viewed in Table  3 .

Comparative evaluation of analytical thinking and clout scores across different cryptocurrencies.

Drives and affect

Drives is a comprehensive dimension that encapsulates various needs and motives 65 . In our LIWC analysis, we concentrated on the Drives, particularly examining the aspects of Affiliation, Achievement, and Power. We observed that the presence of Affiliation-related language (such as “us” and “help“) is comparatively lower in discussions related to Cardano, while it appears more frequently in conversations about Dogecoin. Similarly, in terms of Achievement-related language (including “work”, “better”, and “best“), Dogecoin tends to have fewer instances compared to Matic. Furthermore, when examining Power-related language (like “allow” and “power“), we found that Dogecoin exhibits a lower frequency, while Bitcoin discussions tend to feature a greater occurrence of such language. These patterns highlight variations in linguistic expressions across different cryptocurrencies, shedding light on the distinctive characteristics of discussions over different digital coins. Upon closer examination, it became evident that tweets originating from Binance sources tended to include a higher frequency of words associated with Drives, whereas Fantom source tweets had a notably lower occurrence of Drives-related words. Additional details can be found in Fig.  3 .

Frequency of language associated with affiliation, achievement, power, and drives across different cryptocurrency discussions.

In the Affect 1 subset, our analysis encompassed various emotional dimensions, including Positive Emotion, Negative Emotion, Anxiety, Anger, Sadness, and Swear Words. In the upcoming Emotion section, we delve deeper into affective analysis. However, in this preliminary report, we provide an overview of the affective processes observed in the LIWC analysis. It can be observed in Table  3 that there is a variation in affective (good, well, new, love) content among different cryptocurrencies. Notably, Matic coin exhibits a higher level of affective language, while Ada appears to have a lower level. This distinction becomes clearer when we explore the affective subcategories including Positive tone (new, love), Negative tone (bad, wrong, too much, hate), Emotion (good, love, happy, hope), and Swear words (shit, fuckin*, fuck, damn), as depicted in Fig.   4 . It becomes evident that Matic coin scores higher in Positive tone and Emotion, while Bitcoin registers a higher Negative tone. Additionally, Ripple stands out with a higher score in Swear words, indicating potential user dissatisfaction. When we further break down the Emotion category into its subsets, which encompass Anxiety (worry, fear, afraid, nervous), Anger (hate, mad, angry, frustr), and Sadness (sad, disappoint, cry), we notice that Dogecoin exhibits a higher score in Anxiety, Ripple in Anger, while most of the nine analyzed coins show similar values for Sadness. These observations contribute to our analysis and highlight the varying affective language usage across different cryptocurrencies, which we explore in greater detail in the subsequent Emotion section.

Comparative analysis of affective language dimensions-positive tone, negative tone, emotion, and swear words-across different cryptocurrencies.

Want words signify the author’s desires or preferences. Typically, wants are philosophically differentiated from needs by conceptualizing needs as innate and essential for survival, while wants are learned and generally linked to additional satisfaction beyond basic necessities 68 . What is important for cryptocurrency analysis in this category is the aspect of hope (want, hope, wanted, wish) as Want, or Hope, is a remarkable human ability that allows individuals to envision future events and their potential outcomes with flexibility 69 . Many users have high hopes for the future of cryptocurrency, anticipating greater benefits from their investments. From Table  3 , it becomes evident that Shiba is the cryptocurrency that garners most hope among users. The range of hope scores falls between 0.19 and 0.41, with the lowest level of hope associated with Fantom. This data suggests that Shiba is particularly promising in the eyes of cryptocurrency enthusiasts, while Fantom elicits comparatively less optimism.

Another important LIWC category is Money (business, pay, price, and market) 22 . The range of Money scores, from 2.46 for Shiba to 10.51 for Binance, indicates varying degrees of discussion or emphasis on cryptocurrency financial aspects. Notably, Binance stands out with the highest score, suggesting a significant emphasis on business and financial aspects in discussions related to this coin. Conversely, Shiba has the lowest score, indicating relatively less emphasis on these financial terms in conversations related to it. These findings offer a glimpse into the importance placed on financial and business-related aspects and potentially shed light on the perception and use of the cryptocurrencies in the broader context of market and economy.

At the dawn of experimental psychology, William James wrote that everyone knows what attention is. It is the taking possession by the mind, in a clear and vivid manner 70 . When users include the term Attention in their tweets, it signifies their intention to draw focus to a significant event or topic. Upon reviewing Table  3 , it becomes evident that Ethereum tweets receive more attention than tweets about the other cryptocurrencies, indicating a heightened interest or emphasis on Ethereum-related matters. Conversely, tweets concerning Dogecoin appear to attract less attention when compared to tweets about the other coins, suggesting a relatively lower level of interest or engagement in discussions related to it. For Shiba, our observations indicate a prevalent sense of hope and an increased use of filler words compared to the other cryptocurrencies. This heightened expression of hope suggests a more optimistic sentiment surrounding Shiba when contrasted with the other coins. Additionally, the frequent use of filler words, including expressions like “wow”, “sooo”, and “youknow” signifies a more conversational and engaged discourse. This linguistic pattern may reflect a greater level of enthusiasm and interaction among Shiba enthusiasts.

Netspeak and filler

This analysis includes words commonly used in social media and text messaging, such as “bae”, “lol” and basic punctuation-based emoticons like “:)” and “;)” 65 , 71 . This mode of communication is widely employed by netizens during computer-mediated communication (CMC). In the context of cryptocurrency discussions, which predominantly transpire on online forums, social media platforms, and chat groups, it is customary for participants to incorporate netspeak into their interactions. Through the analysis of netspeak, researchers can understand more the degree of user engagement and interaction. Notably, the adoption of terms such as “HODL” (a deliberate misspelling of “hold”, indicating a long-term investment strategy) or “moon” (indicating an expectation of significant price increases) serves as meaningful pointers to user sentiment and active participation in discussions. In the obtained results, Matic stands out prominently with a notably high netspeak score, signaling the prevalence of internet-specific expressions and informal language related to it. The results can be found in Table  3 . Fillers (wow, sooo, youknow) are non-essential sounds, words, or phrases, such as “well”, “erm” or “hmm” commonly used in speech to occupy pauses and maintain the flow of conversation without altering its meaning 65 , 72 , 73 . The filler analysis results highlight that Shiba and Dogecoin exhibit higher scores in this category compared to the other cryptocurrencies, with scores ranging between 0.02 and 0.04 for the remaining coins, as depicted in Table  3 . In the sentiment analysis, it’s clear that Fantom distinguishes itself with a notably elevated positive score in comparison to the other cryptocurrencies. A consistently positive sentiment can enhance investor confidence, attract new stakeholders, and contribute to a more favorable market perception. Table  3 presents the remaining outcomes for the other cryptocurrencies.

Sentiment and emotions analysis

Table  4 provides a detailed sentiment analysis, encompassing positive, neutral, and negative percentages for various digital coins. In the world of cryptocurrency investments, it’s common for investors to assess public sentiment before making their decisions, as highlighted in prior research 30 . Consequently, sentiment analysis has gained substantial importance on cryptocurrency markets 74 . Studies have shown that tweets expressing positive emotions wield substantial influence over cryptocurrency demand, while negative sentiments can have the opposite effect 32 , 33 .

Analyzing the data in Table  4 , it becomes apparent that Fantom distinguishes itself by displaying a notably higher positive sentiment percentage in comparison to its digital counterparts, which strongly suggests an elevated degree of interest and enthusiasm among investors towards this digital coin.

Examining opinions involves another aspect known as emotion detection. In contrast to sentiment, which can be positive, negative, or neutral, emotions offer richer categorization over personality traits by revealing experiences of joy, anger, and more. Automated methods for emotion detection have been developed to enhance the analysis of individual sentiments. The primary goal of emotion analysis is to identify the specific words or sentences conveying emotions 75 . To achieve such analysis, we employed the NRCLex library to extract and categorize emotions from text 24 . NRCLex is a Python library designed for natural language processing and sentiment analysis. The acronym stands for “Natural Resources Canada Lexicon”, and it is particularly focused on assessing sentiment in text based on word associations. NRCLex is built upon a lexicon that assigns sentiment scores to words, allowing users to analyze the sentiment of individual words, sentences, or entire documents 76 . Table  5 provides the outcomes of our emotion analysis, revealing a narrow range of results for various emotions: Anger (0.02-0.04), Surprise (0.01-0.02), Sadness (0.01-0.03), Disgust (0.01-0.02), and Joy (0.02-0.04). These consistent findings suggest that most of the coins evoke similar emotional responses, highlighting their emotional proximity.

In contrast, when it comes to emotions such as Fear and Trust, there are more noticeable differences between the coins. For instance, when examining the sentiment of Cardano, the fear score is 0.0324, while the trust score is higher at 0.1252. Similarly, for Ripple, the fear score is 0.0416, with a trust score of 0.1172. The scores provide a difference in the emotional tones associated with these cryptocurrencies, indicating the levels of fear and trust expressed in the analyzed content.

Furthermore, the emotion of Anticipation stands out with higher scores in tweets, indicating that many users are keen on anticipating the future of these coins. Notably, Dogecoin (0.3752) and Shiba (0.3467) generate more anticipation among users when compared to the other coins.

Readability

In this section, we pay attention to the readability of data, utilizing metrics such as the Flesch Reading Ease 25 , Flesch-Kincaid Grade Level 26 , Gunning Fog Index 27 , and Dale-Chall Readability Score 28 . Assessing readability helps distinguish between text that is straightforward to grasp and text that is complex and demands a high level of education or intelligence to comprehend. Numerous readability metrics exist for text evaluation, and we have chosen to employ the above four measures as the most widely recognized tests to assess tweets.

Table  6 presents the significant differences in readability scores across tweets related to nine different digital coins.

The Flesch Reading Ease score provides an indication of how easily a text can be understood, with higher scores indicating greater readability. Flesch Reading Ease score can be observed in Fig.  5 . The Flesch-Kincaid Grade Level is a metric that estimates the educational grade level required to understand a piece of text based on factors like sentence length and word complexity. Analyzing the readability scores for the tweets related to each digital coin shows the linguistic complexity employed in discussions surrounding these coins. The presence of significant differences in readability scores suggests variations in the accessibility and comprehension levels required to engage with these tweets. Negative scores in some readability metrics, such as the Flesch Reading Ease and Flesch-Kincaid Grade Level, indicate higher levels of complexity, while positive scores indicate greater ease of comprehension. Refer to Fig.  6 for the necessary details to assess the readability levels of the specified analyses (Flesch-Kincaid Grade Level, Gunning Fog Index, Dale-Chall Readability Score). Table  6 provides evidence on the fact that Dogecoin possesses a notably lower score in Flesch Reading Ease compared to the other cryptocurrencies, which suggests that the communication pertaining to Dogecoin might present hurdles in accessibility and comprehension for the typical reader. Getting rid of such readability obstacles have the potential to amplify the effectiveness of communication, expand audience involvement, and cultivate heightened comprehension and acceptance of cryptocurrencies among varied stakeholders. This observation aligns with Fig. 5 77 , where we notice a pronounced level of complexity in comprehending tweets related to Dogecoin. To gain a better understanding of the varied readability levels, it’s essential to consider both Fig.  5 78 , 79 and Table  6 . When examining the Flesch-Kincaid Grade Level and Dale-Chall Readability in Table  6 , Dogecoin emerges with higher values compared to the other cryptocurrencies, signifying an average grade level and a college reading level, respectively. Furthermore, an examination of the results pertaining to the Gunning Fog Index, as depicted in Table  6 and Fig.  6 , reveals that Ethereum stands out with a higher score. This observation implies that understanding tweets related to Ethereum requires a reading comprehension level equivalent to a college education.

Flesch reading ease score.

Dale-Chall Readability Score, Gunning Fog Index, Flesch-Kincaid Grade Level.

In the process of labeling our data, we identified a notable co-mention among various cryptocurrencies. We resolved this issue by excluding tweets that mentioned more than one coin or used abbreviations for coins not relevant to our research. Consequently, we focused our analysis on the specific set of cryptocurrencies pertinent to our study.

During the utilization of the LIWC software in our analysis, it became apparent that not all its components contributed substantively to the objectives of our research. In light of this, we exercised judicious discernment in the curation of our selection, focusing exclusively on those specific LIWC analyses that bore direct relevance to the concerns and interests of researchers, investors, and individuals engaged in the realm of digital marketing. Additionally, we imposed constraints on our scrutiny of emotional aspects, as certain LIWC framework components exhibited redundancy with one another.

When comparing the Analytical Thinking and Clout aspects to other LIWC features, we could see that these two scores are higher across all coins. This suggests that the tweets generally lean toward logical and formal thought. Notably, among all the coins, Fantom has the highest scores in these aspects, indicating that discussions regarding it are particularly characterized by logical and formal thinking compared to the other cryptocurrencies. After conducting a LIWC analysis, it became apparent that the highest scores were associated with two features that we considered above with concepts such as money, personal drives, and emotional affect. Furthermore, concerning the category of money and personal drives, Binance displayed notably higher scores compared to the other cryptocurrencies. Notably, Matic exhibited significantly higher levels of emotional affect when compared to the other cryptocurrencies. In contrast, other features such as Hope, Attention, Netspeak, and Filler exhibited remarkably low scores, nearly hovering around one percent, when compared to the features we previously discussed. This suggests that the tweets of users are primarily centered on analytical thinking, clout, personal drives, emotional affect, and financial matters. In the sentiment analysis, Fantom stands out with a higher positive score when compared to the other cryptocurrencies. On the contrary, Ripple registers a significantly elevated negative score. This information suggests that Fantom is generating a higher level of positive sentiment, possibly due to positive news, community sentiment, or price performance, while Ripple is experiencing more negative sentiment, which could be linked to negative news or market sentiment surrounding the coin. Emotionally, the majority of cryptocurrencies displayed a relatively modest and well-balanced emotional profile. Interestingly, there was an emphasis on the emotional category of Anticipation, and in this aspect, Dogecoin took the lead. Anticipation in this context likely refers to the expectation or excitement surrounding the future prospects, developments, or events related to these cryptocurrencies. The reason could be upcoming upgrades, partnerships, or any other factors that create a sense of anticipation among the cryptocurrency community. Regarding readability as assessed by Flesch Reading Ease, Dogecoin’s tweets scored lower on this measure. This implies that the content related to Dogecoin is relatively more difficult to read and comprehend, as its language and sentence structure are complex. Concerning Flesch-Kincaid and Dale-Chall measures, Dogecoin’s tweets received higher scores on these measures, indicating that the reading difficulty is at an average level, similar to what one might expect from a college graduate. While the Flesch-Kincaid measure estimates the U.S. grade level needed to understand the text, the Dale-Chall measure also assesses reading difficulty and is often used as a more accurate indicator for texts aimed at older audiences. Speaking of the Gunning Fog Index, Ethereum’s content, on the other hand, registered higher scores on this measure, implying a need for college-level reading proficiency. This means that content related to Ethereum is more challenging to read and understand, requiring a higher level of education and vocabulary.

Limitations

One significant challenge encountered during the data collection phase revolved around sentences containing references to multiple cryptocurrencies. Deciphering the intended cryptocurrency from such sentences posed a considerable complexity, leading to inaccurate analysis for each coin. Therefore, these data were excluded for a more precise analysis of psycholinguistics and emotions for each coin. Additionally, the sheer volume of data presented logistical hurdles, rendering manual labeling impractical in terms of both time and financial resources.

Moreover, the dynamic nature of the cryptocurrency landscape poses another limitation, as sudden events or developments can influence user comments and sentiments, leading to shifts in behavior and sentiment. For instance, specific events like the collapse of Terra Luna or Celsius in 2022 80 , 81 , led to significant market price decreases. Despite efforts to mitigate these impacts through regular monitoring and updates, the inherent volatility of the cryptocurrency market presents challenges in maintaining the consistency and relevance of the dataset over time.

These limitations underscore the necessity for a cautious interpretation of the study’s findings. Future research endeavors in this domain should strive to address such methodological challenges through enhanced data collection techniques and strategies tailored to the dynamic nature of cryptocurrency discourse.

Conclusions and future work

This paper presents a substantial dataset of English tweets related to cryptocurrencies, which were labeled using cryptocurrency keywords and abbreviations (e.g., ADA for Cardano, Ftm for Fantom, Matic for Matic, Btc for Bitcoin, Shib for Shiba, Dogecoin for Doge, Xrp for Ripple, Eth for Ethereum, and Bnb for Binance). Initially, we collected 832,559 tweets, which were reduced to 115,899 tweets after preprocessing. These tweets span from September 2021 to March 2023 and pertain to nine digital coins, namely Cardano, Bitcoin, Binance, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple.

This study conducted psycholinguistic and sentiment analyses on this dataset, utilizing tools such as LIWC, Emotion, Sentiment, and Readability analysis. To avoid LIWC framework redundancy, constraints were applied to the examination of emotional aspects. Our analysis revealed distinct linguistic characteristics and sentiment patterns associated with various cryptocurrencies.

Our investigation into the psycholinguistic characteristics of digital coins showed notable variations among different cryptocurrencies. Through detailed analysis of tweets related to nine distinct digital currencies, we discerned prevalent sentiments expressed by users, assessed the consistency of readability levels across various coins, and identified co-mention between different cryptocurrencies. Leveraging techniques such as psycholinguistic investigations, emotion analysis, and co-mention studies, we obtained valuable estimation of users’ perceptions and interactions.

In a broader context, our study revealed significant psycholinguistic differences among cryptocurrency data. We observed variations in sentiment and emotion analyses, as well as disparities in the readability levels associated with different cryptocurrencies. In future research, we aim to diversify our analysis techniques to delve deeper into the psychological aspects of cryptocurrency discourse. Specifically, we plan to explore sentiments of hope 69 , 82 and regret 83 in textual data using various Natural Language Processing (NLP) methodologies. Additionally, we intend to leverage Large Language Models (LLMs) to conduct psycholinguistic analyses, with an expectation to a deeper analysis of underlying linguistic patterns and emotional tones present in cryptocurrency discussions. Furthermore, our future work will involve classification algorithms with diverse machine learning approaches to distinguish bullish and bearish sentiments in comments, utilizing manual labeling for training data.

Data availability

The datasets generated and/or analysed during the current study are available in the GitHub repository, https://github.com/moeintash72/cryptocurrency-data- .

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Acknowledgements

The work was done with partial support from the Mexican Government through the grant A1-S-47854 of CONACYT, Mexico, grants 20241816, 20241819, and 20240951 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico. The authors thank the CONACYT for the computing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologas del Lenguaje of the Laboratorio de Supercómputo of the INAOE, Mexico and acknowledge the support of Microsoft through the Microsoft Latin America PhD Award.

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Olga Kolesnikova

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Moein Shahiki Tash, Zahra Ahani & Grigori Sidorov

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M.S.T., O.K., Z.A., and G.S. contributed to this manuscript as follows: M.S.T. conceived and designed the study, collected and analyzed the Twitter data, performed the psycholinguistic and emotion analysis, and drafted the manuscript. O.K. contributed to the data analysis, provided expertise in statistical modeling, and critically revised the manuscript for important intellectual content. Z.A. assisted in data collection, conducted literature review, and contributed to the interpretation of results. G.S. provided guidance on computational methodologies, supervised the research process, and contributed to manuscript revisions.

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Tash, M.S., Kolesnikova, O., Ahani, Z. et al. Psycholinguistic and emotion analysis of cryptocurrency discourse on X platform. Sci Rep 14 , 8585 (2024). https://doi.org/10.1038/s41598-024-58929-4

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