Price
1h %
24h %
7d %
Market Cap
Volume(24h)
Circulating Supply
Last 7 Days
1
$1.2T$1,197,218,363,906
2
$293.93B$293,926,484,270
3
$118.39B$118,393,379,248
4
$81.3B$81,300,037,492
5
$65B$64,999,259,956
6
$35.77B$35,769,018,236
7
$32.3B$32,299,658,891
8
$15.59B$15,593,575,367
9
$14.62B$14,620,053,680
10
$12.97B$12,973,891,120
Welcome to CoinMarketCap.com! This site was founded in May 2013 by Brandon Chez to provide up-to-date cryptocurrency prices, charts and data about the emerging cryptocurrency markets. Since then, the world of blockchain and cryptocurrency has grown exponentially and we are very proud to have grown with it. We take our data very seriously and we do not change our data to fit any narrative: we stand for accurately, timely and unbiased information.
Here at CoinMarketCap, we work very hard to ensure that all the relevant and up-to-date information about cryptocurrencies, coins and tokens can be located in one easily discoverable place. From the very first day, the goal was for the site to be the number one location online for crypto market data, and we work hard to empower our users with our unbiased and accurate information.
Each of our coin data pages has a graph that shows both the current and historic price information for the coin or token. Normally, the graph starts at the launch of the asset, but it is possible to select specific to and from dates to customize the chart to your own needs. These charts and their information are free to visitors of our website. The most experienced and professional traders often choose to use the best crypto API on the market. Our API enables millions of calls to track current prices and to also investigate historic prices and is used by some of the largest crypto exchanges and financial institutions in the world. CoinMarketCap also provides data about the most successful traders for you to monitor. We also provide data about the latest trending cryptos and trending DEX pairs .
We receive updated cryptocurrency prices directly from many exchanges based on their pairs. We then convert the number to USD. A full explanation can be found here .
Related Links Are you ready to learn more? Visit our glossary and crypto learning center. Are you interested in the scope of crypto assets? Investigate our list of cryptocurrency categories. Are you interested in knowing which the hottest dex pairs are currently?
We calculate our valuations based on the total circulating supply of an asset multiplied by the currency reference price. The topic is explained in more detail here .
We calculate the total cryptocurrency market capitalization as the sum of all cryptocurrencies listed on the site.
Almost. We have a process that we use to verify assets. Once verified, we create a coin description page like this . The world of crypto now contains many coins and tokens that we feel unable to verify. In those situations, our Dexscan product lists them automatically by taking on-chain data for newly created smart contracts. We do not cover every chain, but at the time of writing we track the top 70 crypto chains, which means that we list more than 97% of all tokens.
At the time of writing, we estimate that there are more than 2 million pairs being traded, made up of coins, tokens and projects in the global coin market. As mentioned above, we have a due diligence process that we apply to new coins before they are listed. This process controls how many of the cryptocurrencies from the global market are represented on our site.
The very first cryptocurrency was Bitcoin . Since it is open source, it is possible for other people to use the majority of the code, make a few changes and then launch their own separate currency. Many people have done exactly this. Some of these coins are very similar to Bitcoin, with just one or two amended features (such as Litecoin ), while others are very different, with varying models of security, issuance and governance. However, they all share the same moniker — every coin issued after Bitcoin is considered to be an altcoin.
The first chain to launch smart contracts was Ethereum . A smart contract enables multiple scripts to engage with each other using clearly defined rules, to execute on tasks which can become a coded form of a contract. They have revolutionized the digital asset space because they have enabled decentralized exchanges, decentralized finance, ICOs, IDOs and much more. A huge proportion of the value created and stored in cryptocurrency is enabled by smart contracts.
Price volatility has long been one of the features of the cryptocurrency market. When asset prices move quickly in either direction and the market itself is relatively thin, it can sometimes be difficult to conduct transactions as might be needed. To overcome this problem, a new type of cryptocurrency tied in value to existing currencies — ranging from the U.S. dollar, other fiats or even other cryptocurrencies — arose. These new cryptocurrency are known as stablecoins , and they can be used for a multitude of purposes due to their stability.
NFTs are multi-use images that are stored on a blockchain. They can be used as art, a way to share QR codes, ticketing and many more things. The first breakout use was for art, with projects like CryptoPunks and Bored Ape Yacht Club gaining large followings. We also list all of the top NFT collections available, including the related NFT coins and tokens.. We collect latest sale and transaction data, plus upcoming NFT collection launches onchain. NFTs are a new and innovative part of the crypto ecosystem that have the potential to change and update many business models for the Web 3 world.
Play-to-earn (P2E) games, also known as GameFi , has emerged as an extremely popular category in the crypto space. It combines non-fungible tokens (NFT), in-game crypto tokens, decentralized finance (DeFi) elements and sometimes even metaverse applications. Players have an opportunity to generate revenue by giving their time (and sometimes capital) and playing these games.
One of the biggest winners is Axie Infinity — a Pokémon-inspired game where players collect Axies (NFTs of digital pets), breed and battle them against other players to earn Smooth Love Potion (SLP) — the in-game reward token. This game was extremely popular in developing countries like The Philippines, due to the level of income they could earn. Players in the Philippines can check the price of SLP to PHP today directly on CoinMarketCap.
CoinMarketCap does not offer financial or investment advice about which cryptocurrency, token or asset does or does not make a good investment, nor do we offer advice about the timing of purchases or sales. We are strictly a data company. Please remember that the prices, yields and values of financial assets change. This means that any capital you may invest is at risk. We recommend seeking the advice of a professional investment advisor for guidance related to your personal circumstances.
TThe data at CoinMarketCap updates every few seconds, which means that it is possible to check in on the value of your investments and assets at any time and from anywhere in the world. We look forward to seeing you regularly!
Crypto Theses for 2024
Product Updates
Messari is hiring!
Sep 10, 2024 ⋅ 16 min read
Upgrade to Messari Pro to read this report
Research Reports covering trends and assets in crypto
Full access to Asset and Fundraising screener to track trends across Assets, funding rounds, M&A Deals, and Investors
Unlocked Enterprise Research Reports 90 Days after they are published
Full access to advanced asset screening with custom filters, queries, and metrics
Real-Time Governance Tracker
Advanced AI digest with all features in lite plus Key Developments
Already a member? Sign In
Prior to joining Messari, Chris was the Growth Lead at a Paradigm and a16z-backed gaming startup called LootRush. His primary focus is Consumer Products, Music x Crypto, Gaming, Digital Art, and market buying local tops.
Mentioned in this report
About the author
You have full access to this open access article
In the last decades, crypto assets have become particularly popular in financial markets. However, public awareness of the crypto asset landscape is rather limited, and usually associated with sensationalized media coverage of a handful of cryptocurrencies. Moreover, while users of crypto assets primarily collect information on Internet, there is a limited understanding of the relational (online) structures supporting the diffusion of information about these financial products. Therefore, the aim of this study is to uncover the structure of online information referral networks dedicated to crypto assets. By adopting a multi-method approach consisting of web scraping, web analytics, and social network analysis, we use data from the top 200 crypto assets by market capitalization to identify pivotal websites and the overall connectedness of the information referral networks. Our results show that social media and news channel sites play a key role in the information diffusion process, while market and trading sites signal innovation adoption. Overall, cryptocurrencies’ websites do not seem key in the referral network, as opposed to social media websites which, however, cannot be considered mature hubs because of their low connectivity.
Avoid common mistakes on your manuscript.
Over the last 2 decades, a new phenomenon has revolutionized financial markets: the introduction of digital currencies adopting blockchain technology (Griffin and Sharms 2020 ; Nakamoto 2008 ). Blockchains provide an immutable system which forms the backbone of crypto assets: transactions cannot be deleted or altered, and this enables transaction histories to be stored and transmitted globally through peer-to-peer computers (Koroma et al. 2022 ). However, public understanding of crypto assets, and the full scope of the crypto asset landscape, remains nascent at best or ill-informed at worst. Recent studies have shown that this limited understanding is linked to sensationalized media coverage of a handful of cryptocurrencies during events of high crypto-market volatility (Olney 2022 ). Researchers looked at specific groups and interactions in the community of media-sensationalized currencies such as Bitcoin (Hedman et al. 2021 ) or Ethereum (Bizzi and Labban 2019 ) to test public engagement, finding that cryptocurrencies’ performance is strongly influenced by the media narrative, which tend to be rather sensationalistic and not always objective. Despite the growth of cryptocurrencies’ market capitalization—which reached over 3 trillion USD in 2022 (Forbes 2022 )—the main information sources leading to such upsurge of crypto assets remains unclear (Antonakakis et al. 2019 ).
In contrast to other financial assets, the decentralized nature of crypto assets results in lack of formal control; this leads to self-regulating behaviors where social interactions determine the dynamics of the crypto assets landscape (Chiu 2021 ). This is why recent studies have started to look at the crypto asset landscape as a socio-technical ecosystem, emphasizing that individuals do not act in isolation, but they interact with technologies to the extent that they influence each other (e.g. Shin and Rice 2022 ). Online websites become key in the spread of information, and in the crypto asset landscape they become particularly relevant since users’ activities are mediated by IT tools. This online space can offer novel insights about the influence process related to innovation; however, existing research so far has not investigated in depth the (online) relational aspect characterizing the crypto asset landscape.
We assume that individuals are embedded in complex relational patterns, and they rely on information shared via networks of social interactions (Yi et al. 2020 ). This idea is the central feature of the innovation diffusion network perspective, where innovation is spread through the social networks of those who are perceived as the most influential and trustworthy sources of information (Valente 2012 ; Valente and Rogers 1995 ). However, research also shows that innovation diffusion may vary according to the context (Arieli et al. 2020 ) and social awareness (Müller and Peres 2019 ), which may lead to social behaviors that are not linear by nature but depend on the network features of the social system. This paper goes beyond the literature that looks at crypto assets as socio-economic artifacts (Li et al. 2019 ; Shin and Rice 2022 ), their geographic dispersion (Park and Park 2020 ), and their financial determinants (Feyen et al. 2022 ), and aims to investigate the importance of online interactions in supporting awareness of crypto assets and their diffusion. By using a multi-method approach based on web scraping, web analytics, and social network analysis (SNA), we identify and map a referral network whereby hyperlink referrals are seen as footprints of user behavior. As such, we describe how networks support the adoption of innovation propagated across web hyperlinks. As a result, we identify the most influential websites in disseminating information within the referral network and uncover their connection patterns. Specifically, we describe the impact of the information referral networks as channels for spreading the diffusion process by analyzing the most central or pivotal websites in the network and showcase how popular websites drive the herd behavior in adoption of crypto assets. The primary research question that this study seeks to address is the following: how does network position in the information referral network is affected by, and affects, (crypto asset) information diffusion? In this vein, we seek to unveil the role of (online) relationships and understanding their influence on the crypto asset landscape.
The paper is organized as follows. First, we review the technical features of crypto assets, and we introduce the relational perspective used for understanding innovation diffusion. Second, we describe the data collection process and the method of analysis. Third, we present the results of our analysis, focusing on the key nodes (i.e. websites) that are supporting the spread of information related to crypto assets. Finally, we discuss the practical implications of our analysis—how crypto asset developers should use central websites to reach a broader audience.
2.1 understanding crypto assets.
Crypto assets are digital assets that use digitalization technologies such as Public Key Infrastructure (PKI), cryptographic techniques, and Distributed Ledger Technology (DLT)—which rely on the blockchain technology. While initially designed with the purpose of transaction storage, it has since been used for implementing several decentralized applications like asset tracking (Rosenfeld 2012 ), smart contracts (Drummer and Neumann 2020 ; Mohanta et al. 2018 ), and distributed databases (McConaghy et al. 2016 ), to name a few. While there is currently no standard taxonomy provided for crypto assets, there are international standards in place for blockchains and DLT created by the International Organization for Standardization (ISO). Footnote 1 Crypto assets can be classified into 3 main categories. The first includes payment or exchange tokens commonly referred to as cryptocurrencies: peer-to-peer (P2P) alternatives to legal tender issued by governments based on PKI and cryptographic mechanisms, which are used as a general medium of exchange with the ability to convert it to a legal tender (Hays and Kirilenko 2019 ). The second category is decentralized finance (DeFi), which relies on the use of smart contracts—self-executing agreements between a seller and a buyer stored in a decentralized and distributed blockchain network (Bartoletti and Pompianu 2017 ). The third category of assets are Non-Fungible Tokens (NFTs) and Collectibles commonly referred to as Play to Earn (PTE) tokens. In the cryptocurrency markets, ubiquitous speculation exists with games that are offered in the blockchain environment (Gandal et al. 2018 ); PTEs are part of collective initiatives and are provided in the form of puzzles, avatars, or NFTs that can be used in the game.
A major line of contemporary blockchain research describes the crypto asset landscape as a socio-technical ecosystem that encourages interactions among participants (Park and Park 2020 ). Here, the socio-technical component of the crypto landscape underlines the link between social factors and technological factors in understanding the ecosystem (Gandal et al. 2018 ): social factors are strictly related to individuals’ behavior and attitude, while technological factors are associated with the characteristics of technology (Bostrom and Heinen 1977 ). Individuals and technologies interact to the extent that individuals use and apply technologies. As such, a crypto asset “is seen as a network with a socio-technical structure since the systems are composed of technical infrastructure and the social relations between users of the crypto ecosystem” (Park and Park 2020 ). A relational framework therefore explains the interaction between social and technological factors as it highlights the importance of interactions among participants: participants are embedded in a network of social relationships through which tangible and intangible resources are exchanged. If the socio-technical framework characterizes crypto assets and their link to legal and ethical aspects (Dowling 2022 ), the relational framework can be identified by both structures (in terms of social relations) and processes (or mechanisms, which generate these structures). As such, adopting crypto assets into the social structure is the result of interactions among key players (such as users, group of users, community of practices, stakeholders and market) who are social innovators to the extent they impact and change these social interactions. Thus, the social agency is not only an attribute of participants, but also an attribute to the system and distributed across the network of relations within the crypto landscape. Adopting crypto assets is therefore established through the joint actions of multipoint contacts within this ecosystem. Specifically, interactions between innovators provide the relational infrastructure to support a range of social processes, including the adoption and diffusion of innovation (Sousa et al. 2022 ). These social processes represent the actual mechanisms through which the adoption of crypto assets operate among individuals. For this reason, the relational nature of the crypto asset ecosystem may be explained with innovation diffusion theories.
Innovation diffusion theories not only explain the velocity of innovation adoption, but also why some innovations become de facto widely adopted while others might not take off at all. According to Rogers ( 2003 ), diffusion is “the process by which an innovation is communicated through certain channels over time among the members of a social system”; the importance of individuals in this process is evident, but at the same time the vehicle of diffusion and the presence of a structured social systems are key for the success of an innovation’s diffusion.
Individuals’ behavior in the adoption of innovation can be influenced by a variety of psychological and environmental factors, and usually it follows five different stages: knowledge, persuasion, decision, implementation, and confirmation of the innovation adopted (Rogers 2003 ). Moreover, adoption decisions can stem from the indirect influence of those who adopted the innovation in the first place (Chao et al. 2020 ). Social pressures lead to further adoption of the innovation as individuals prefer to conform to social norms, which further reinforce the bandwagon effect (Abrahamson and Bartner 1990 ).
This effect is usually strengthened by a communication channel that can reduce the amount of time necessary for exchanging information between individuals (Vishwanath and Barnett 2011 ). Internet is probably the most powerful tool for knowledge and information exchange that has been created in decades, and digital networks have become fundamental in spreading new ideas and innovation (Sproull and Kiesler 1991 ). Especially when considering novel technologies, internet and IT infrastructures foster the commitment of individuals to social norms and therefore explain the rapid adoption of certain technologies (Sawang et al. 2014 ).
Nevertheless, without a social system where innovation can be spread this process would be unsuccessful. Ashley ( 2009 ) pointed out that it is within social systems that innovation can be spread, and individuals and their relations—as well as organizations and institutions—determine who can be reached by the new information about it. Diffusion processes within social systems can be investigated by adopting the research lens of network theory and using relational approach for empirically evaluating network-related phenomenon. Depending on their structuring, networks can facilitate the access to novel information: relations are created between network actors via physical or online interactions, and the positioning of these actors in the network impact the diffusion process (Burt 1992 ). A study from Ma et al. ( 2014 ) shows that actors’ behavior to share news is influenced by the strength of their relationships. Similarly, Zhang and Peng ( 2015 ) show centrality of individuals in advertising systems are key in the diffusion process. In this vein, referral networks are extremely important in shaping and driving users’ behavior, because it has been demonstrated that individuals make their choices (about a new product and/or innovative system) according to their reference group (Cho et al. 2012 )—individuals accessing the same web pages and websites referring to the same set of information from the same group of websites. Therefore, we argue that relational approach can be used for understanding the crypto assets’ diffusion process.
3.1 data collection.
We collected and triangulated 3 different sets of data to examine the network position that websites come to occupy within the referral network. Specifically, we used a 3-steps approach to scrape crypto assets data. First, we relied on the coinmarketcap API to obtain the top 200 assets by market capitalization. Footnote 2 Coinmarketcap also provides a classification of the assets (Defi, NFT, PTE, currency). For each of the assets, the API provides the official website, the total market capitalization, circulating supply, trading volume as well as the maximum supply if applicable (Coinmarketcap 2021 ). Following prior research on cryptocurrencies (Drobetz et al. 2019 ; van Tonder et al. 2019 ), we use a Web scraping approach from the Coinmarketcap website. This approach was adopted as the API of Coinmarketcap allows us to automate the process of extracting data about the crypto assets such as cryptocurrency name, price, circulating supply, official website of the currency and market capitalization and storing this data in a structured manner as a CSV file (Coinmarketcap 2021 ).
Some assets have more than one official website listed, in which case all the sites are taken into consideration for the analysis. Second, we used the API from SimilarWeb to scrape web analytics data for the websites of the top 200 crypto assets (by market capitalization) obtained from coinbase and coinmarketcap in September—November 2021. This approach of data triangulation has been holistic, however, there was no analytic data available for 54 websites which resulted in a total of 173 websites. This is not a limitation per se as there were multiple websites for some crypto assets and all categories of the assets have been represented in the sample obtained. Also, this period has been chosen as it is characterized by volatility in the market. Furthermore, there were several major events and developments since the mid of the year. First, on the 7th of September, due to deleveraging, over $320 million leveraged Bitcoin was liquidated leading to a 11% market-wipe out of the Total Value Locked (TVL). Second the Chinese Government announced that all cryptocurrencies were illegal. Third, El Paso accepted crypto currency as the legal tender. Finally, several Tweets of Elon Musk led to fluctuations in the market. For instance, one of his tweets caused a 4% drop in bitcoin prices, pushing it below its 20-day moving average at $33,710 in June 2021; driving down Tesla’s (NASDAQ: TSLA) stock quote by a third and Bitcoin (BTC) by more than 40% below its April peak at $64,895.22 (Yahoo News 2021 ). On the other hand, Dogecoin (rolled out as a joke with little stock value Source in NYTimes, 2021), had a multibillion-dollar valuation (DOGE value as at July 2021: 26.244B), mostly as a result of another tweet from him. Indeed, Nick Spanos, the co-founder of ZAP protocol mentioned that “when Elon Musk tweets any crypto-related content, the market … expects a reaction” (Yahoo News 2021 ).
SimilarWeb provides a comprehensive list of engagement metrics for a website including unique users who visited a page, countries where the page has been accessed from, bounce rates, time spent on the site, the sites which have led users to the site in question, and the sites that the current website redirects the users to. User-centric data is collected from a global user panel of 400 million users, website analytics and ISP data to obtain website traffic information. To set up the web mining tool, first, an API key was obtained. Once this was done, there was a 3-step process followed to obtain the necessary information about the websites. First, the end points were constructed by creating a batch API request. Second, this request was sent as an HTTP POST request as a batch. This was particularly useful considering that we could obtain batch jobs and all data from the request can be obtained in one-go as opposed to creating individual requests. Using the website’s data allowed us to obtain information pertaining to both traffic and engagement—including global rank, country rank, bounce rate. Next, the referral traffic data was used to obtain information about visitors who visited a website through clicking links from other pages. Finally, the API response was received in JSON format.
Third, we obtained the network data on the referral sites. For every crypto asset, we looked at the top five sites that refer the user to the website of the assets, and also the top five websites visited by the users of the crypto websites. The information referral network includes sites and referred sites that have been created by using SimilarWeb. An adjacency matrix (A) for the directed network is created such that the rows and columns correspond to a website. The value at position (A_{ij}) is 1 if there is a directed tie from website (i) to website (j), and 0 otherwise. The rows in the matrix represent the outgoing ties and the columns represent the incoming ties. The network comprises 2273 nodes (websites) with 2101 ties representing the information referral process.
To investigate the level of diffusion and the most central websites, we employed a combination of web analytics and SNA. Web analytics helps to understand which are the topical trends, how website users behave, the interests of the users and the most popular sites/pages (Jansen 2009 ). Studies using web analytics concentrate on individuals, websites, and the networks created by online interactions to estimate traffic to websites; interactions are mapped via hyperlinks, which enable individuals to make contacts with “people or groups anywhere in the world” (Park 2003 , p. 50). We accounted for 3 different metrics namely web global ranks (calculated using the total unique pageviews and visitors), total visits and average time spent per unique user (SimilarWeb 2021 ).
Since we can see hyperlinks as connections, it is possible to assume that the hyperlink structure is a communication network among actors operating online (Park 2003 ). Hence, SNA is then applied for assessing how the diffusion of crypto assets spreads across information channels. SNA is a discipline which focuses on the investigation of social structures by using analytical methods derived from graph theory (Wasserman and Faust 1994 ); social structures—or social networks—can be found in both physical and digital environments, where networks can be mapped if we have nodes (individuals, organizations, institutions, or other identifiable actors) connected together via a set of relationships. Relationships can be directed or undirected—if there is a flow from one node to the other—and weighted or unweighted—if the relationship has a value, such as a monetary value, or not (Prell 2012 ). The World Wide Web (WWW) is seen as a medium where information about innovation and innovation itself are connected to individuals; hence, SNA can be used for exploring patterns between individuals and web pages emerging from hyperlinks (Barnett and Park 2014 ; Can and Alatas 2019 ) and ‘understanding the interplay between computer-mediated social processes’ (Park 2003 , p. 50). As highlighted before, networks are made by nodes connected via ties/relationships (Wasserman and Faust 1994 ): in our context, nodes are the web pages, which are connected by the referral relationship; the referral process is based on the idea that when a user leaves a website to go to another a relationship between nodes (websites) is created. The users’ behavioral intention can be captured as they knowingly click on hyperlinks that are created by the site editor to move to other pages within the same or different site. This shapes the social structure which results in the formation of the network, as the web does not have an engineered architecture (Rosen et al. 2011 ). Hyperlink network analysis has become an important research area in SNA, since the seminal works of Park ( 2003 ) and Park and Thelwall ( 2003 ); in the last 20 years, scholars have used this methodological approach to investigate digital network structures in tourism and hospitality (Ying et al. 2016 ), politics (Elgin 2015 ; Lusher and Ackland 2010 ), and manufacturing (Hyun Kim 2012 ), using quantitative methods and statistics from SNA.
In order to assess the diffusion of innovation in our information referral network, we estimated a set of network statistics, similar to what has been done in previous studies on online networks (e.g. Barnett and Park 2005 ). We concentrate on one network-level measure called degree assortativity, and 3 node-level measures, namely in-degree, out-degree, and betweenness centrality. Degree assortativity is the Pearson correlation of the degree of single nodes in the network, and it shows the extent to which nodes with similar degrees are connected to each other; when its value is high, it means that nodes with higher degrees will be connected to each other (Newman 2002 ). This is captured by measuring for each node i in the network with j neighbours the average degree of its neighbors ( \({k}_{\eta n}\left({k}_{i}\right)\) , and is given by the formula:
Once the average degree is measured, the conditional probability (Eq. 2 ) P(k′|k) is used for quantifying the degree correlations (Pearson correlation coefficient given by r—Eq. 3 ) inspecting the dependence of knn(k)- which denotes the average degree of degree-k nodes on k. Thus,
Centrality measures allow us to explore users’ capability to spread information according with the positions that they come to occupy within the network (Wasserman and Faust 1994 ). Specifically, in-degree centrality accounts for the ability of a website to be an influencer based on its number of connections. To calculate the in-degree of a node i , the i th row is summed and is given by the formula:
Influential websites may be seen as opinion leaders since these sites can shape users’ behaviors and decisions. When websites funnel connections to other sites, they basically spread information in the network by connecting to other sites. This networking behavior is captured by the out-degree centrality which is an estimate of the number of connections from one node to others (Wasserman and Faust 1994 ). To calculate the out-degree of a node i , the i th column is summed and is given by the formula:
Finally, websites may play a role of information bridge by connecting different sites within the network. This is captured by the betweenness centrality which accounts for the number of times that one node is in the shortest path between other nodes in the network (Rosen et al. 2011 ) denoted by the formula:
Hubs characterized by nodes with high betweenness centrality are vital for disseminating information in the network, and their presence usually leads to a higher diffusion rate [46] given by the formula:
where L refers to the total number of links.
For the top 200 crypto assets based on market capitalization, a total of 227 websites were identified; no analytic data was available for 54 websites, which resulted in the analysis of 173 crypto assets. Table 1 provides some descriptive statistics. Footnote 3
The top 10 crypto assets based on Web Global Rank, average time visit, and total visits—as listed in coinmarketcap by using data from the web scraping process—are provided in Table 2 .
We look at measures of engagement and user attention by analyzing the Web Global Rank, average visit time and total visits, since these metrics are considered the most important indicators for user activity (SimilarWeb 2021 ). Currency websites such as Binance Coin and Waves have a higher global rank followed by Axie Infinity, which is a PTE. This shows that traffic (determined by unique views globally) to these websites are higher compared the much-sensationalized cryptocurrencies such as Bitcoin or Ethereum. The top asset types by average visits are PTE, and with the exception of Binance Coin and Bitcoin Cash most of the currencies have a lower average visit duration next to NFTs. While it is possible to argue that higher average visit could also be associated with complex sites, Myers et al. ( 2008 ) have shown that when web interfaces are complex, this leads to individuals bouncing off. Similarly, there are no NFTs in the top-10 by total visits, while DeFi tokens overall have higher total visits than other crypto asset types—even if the top website per total visits refers to TerraUSD, a cryptocurrency.
Figure 1 illustrates the information diffusion process across websites, i.e.; how users obtain information and where they are bounced off—after viewing one page—when they leave a website. The dots represent the websites. The size of each dot is proportional to the number of websites receiving connections (the “indegree” of the information referral networks). Coinmarketcap; GitHub; Medium; Coinbase; and Coingecko are the most popular websites, i.e., they are influential as they shape users’ behaviors. The lines represent the referral process.
Information Referral network. The dots represent the website, and the lines represent the referral process —when a user leaves a website to go to another. The size of the node is proportional to the number of incoming connections
The network density (proportion of the total network ties over the total number of possible ties) is 0.001 and the average degree (average number of connections a node has in a network) is 0.927; both these statistics are particularly low, which indicate an environment characterized by lower diffusion of information and therefore innovation (Myers et al. 2008 ). While too much density can be an issue in terms of innovation diffusion, because of the risk of redundancy and tendency towards imitation, it is also true that a moderate level of density is needed to increase the likelihood of being exposed to novel ideas (Shaw-Ching Liu et al. 2005 ). Similarly, a low level of average degree centrality indicates that we are observing a network characterized by several peripheral actors: this is not favoring innovation diffusion, since peripheral actors are not able to influence other nodes and spread innovative ideas.
The score for degree assortativity is 0.019, which is considered almost null (i.e. there is almost no relationship between nodes with similar degrees); this may indicate that there is no redundancy in the network. If we look at the node-level, we see that certain websites are more prone to attract users and send them to the market sites. Social media websites (such as YouTube, Twitter (now X), LinkedIn, and GitHub—which can be considered a social media platform for developers) and informational sites (etherscan.io, which is a blockchain explorer for Ethereum network, or medium.com, which is a publishing platform) can thus be good information sources to observe users’ actions before making choices about adopting crypto assets (Tandon et al. 2021 ). From our analysis, we see that users move from sites social media websites to crypto assets’ website, and from there they reach the market sites as indicated by the scores for out-degree centrality in Table 3 . Websites with higher in-degree centrality act as the initiators of the diffusion process, while websites with higher out-degree centrality enable individuals to conform to (online) social norms via the adoption of crypto assets—because these are the market sites where individuals can purchase assets. Finally, nodes with high betweenness centrality are seen as those influencing the flow of information in the network as they act as bridges to connect to the official websites of the crypto asset.
Our analysis provides novel insights on the crypto asset landscape, and the diffusion process of information related to crypto assets. Two main findings emerge from this research: first, websites of the much-sensationalized cryptocurrencies are not key in information diffusion; second, social media websites are seen as enablers of the diffusion process—but they actually cannot be considered mature hubs for supporting this process because of their low connectivity.
Regarding our first finding, this may be perceived quite surprising. However, it is also true that market-related information about crypto assets is often conveyed by other media—especially news media specialized in finance such as CNBC and Forbes. Major events related to crypto assets have a positive or negative impact on their returns (Hashemi Joo et al. 2020 ), and the larger the media coverage the higher the impact—which is something that a single website cannot do. Official websites like Bitcoin.com can promote or recommend specific financial products, such as open-source wallets, but previous studies found that professional investors sometimes prefer to collect first-hand information via Twitter (now X) (Shen et al. 2019 ). In this vein, our study complements the analysis of Park and Park ( 2020 ), which focused on the websites of top 50 cryptocurrencies and found that, among these websites, those related to the much-sensationalized cryptocurrencies (by the news channels between September and November 2021) are central in the network. However, since they did not concentrate on other crypto assets or media websites, we argue that websites of popular cryptocurrencies are key only when considering this specific asset—the cryptocurrency. Eryiğit and Eryiğit ( 2021 ) pointed out that social media play a relevant role in the diffusion process; their work specifically focused on Bitcoin, but their findings support the idea that word of mouth is particularly effective—and social media strengthen this process. As highlighted by Yang et al. ( 2019 ), self-media users are important sources of diffusion. Compared to official media users, those users creating their own contents on platforms such as Weibo—the microblogging platform examined by these scholars—are more effective in spreading information and ideas compared to those who refer to traditional media or official sources of information. Our results are aligned with this finding: social media platforms are powerful tools for information diffusion, and within these platforms unofficial content creators (e.g. YouTubers) are capable of reaching a larger audience as opposed to official sources (e.g. Bitcoin.com).
The above discussion relates to our second finding: social media websites play a relevant role in the diffusion process. This confirms what has been reported by Moser and Brauneis ( 2023 ): there is a world of professional (but also non-professional) financial advisors that are sharing contents using social media such as YouTube or Twitter (now X), and their channels are rather popular among investors. This finding needs to be interpreted in light of the relational approach we used for understanding innovation diffusion. Interactions between different players can be detected all around the globe: the World Wide Web enables individuals with different expertise to share information via different channels and potentially reaching everyone in the world –with an Internet connection. In a way, the diffusion of crypto assets is supported by the presence of social innovators who communicate using different social media channels. van der Linden and van Beers ( 2017 ) found that some social innovators tend to promote crypto assets in their geographical environment, because of personal interests; however, crypto assets are global by definition, and therefore we also have social innovators who aim to be disruptive—in their approach to innovation—and influence as many individuals as possible globally. Hence, the adoption of this particular type of innovation follows social processes that have been observed also in other contexts. However, we discovered that social media websites are not providing the boost that is needed for initializing a robust diffusion process. Low levels of degree assortativity and connectivity in networks indicate potential issues in knowledge diffusion. As highlighted by Müller and Peres ( 2019 ), high assortativity is important because relevant actors/nodes in the networks can be strongly interconnected and reaching them can effectively boost the diffusion process. If such actors are missing in the network, this may hinder the diffusion process. Low assortativity per se is not a structural problem, from a network perspective, but there should be at least a set of nodes with high betweenness centrality—i.e. nodes that act as brokers in the network and support interconnectivity—in order to facilitate knowledge diffusion. In general, technological networks are considered to be disassortative and particularly sensitive to disruptions such as the removal of key nodes (Newman 2002 ); in this context, it is confirmed that the overall referral network is not dense, and we are missing relevant nodes capable of supporting the diffusion of information related to crypto assets. These nodes (social media websites) have potential, but we are just observing the first stages of such a process.
This study provides an empirical analysis of the referral (online) network describing the diffusion process of information related to crypto assets. Our results show that the most central websites in this network are market sites, which indicates that adopters are exposed to information by chance and through ill-defined exploration. Specifically, crypto market information websites and trading websites provide up to date information and re-direct potential users towards other specialistic websites. Overall, we conclude that the adoption of these assets is at its very beginning. Low assortativity and average degree indicate that crypto assets are in their awareness stage, where the public attention to the existence of these assets is beginning to expand. Awareness can be enhanced when there are information flows about the innovative product (De Bruyn and Lilien 2008 ), and in the crypto assets landscape this is achieved by mass social communications, news outlets, and word-of-mouth communications; this is confirmed by the higher prominence of websites such as YouTube, LinkedIn, and Medium.
This study provides several contributions to research and practice. From a research perspective, we advance our understanding of how information about crypto assets is shared online, and how the diffusion process is currently structured in this context. By using a relational approach, we mapped the key global websites which contribute to the diffusion process, and we analyzed their referral network using advanced analytical network techniques. In this vein, our methodological approach is innovative because it combines web scraping, web analytics, and SNA to empirically detect initiators and influencers. Second, our study does not limit to cryptocurrency websites only (Park and Park 2020 ) or cryptocurrency users only (Bharadwaj and Deka 2021 ), but it explores the entire crypto assets world—and thus it offers a broader overview of the phenomenon. In terms of business implications, there are two main aspects emerging from this work. First, the aforementioned importance of social media channels is something that organizations offering crypto assets might want to capitalize. This does not mean that such organizations are not aware of the potentials of YouTube or LinkedIn: as described by Hua et al. ( 2022 ), cryptocurrencies are often used for donations to YouTube content creators, and a variety of contents about crypto assets can be found in social media channels. Footnote 4 However, this has not been done systematically, or establishing formal partnerships between organizations offering crypto-related products and social media platforms. What has been observed in recent studies (e.g. Moser and Brauneis 2023 ) is that professional financial advisors—which can be called ‘crypto-influencer’—are using social media for promoting crypto assets, but they mainly operate in a clickbait-shaped environment where organizations such as Ethereum are not directly involved in the creation of their contents. The second main practice-related contribution relates to something that is, in a way, diametrically opposed to what highlighted before, i.e. the importance of keeping blockchain-based solutions decentralized. Decentralisation is the foundation of crypto assets: the same concept of blockchain is based on this idea. Our analysis shows that websites of the much-sensationalized cryptocurrencies are not as powerful—in terms of information diffusion—as other websites. At the same time, social media platforms such as Facebook have started introducing their own cryptocurrency (Diem) using a permissioned and private blockchain, which is in contradiction with the whole idea of distributed ledger technology (Ferrari 2020 ). This should emphasize the value of using online and offline advertising systems for raising awareness among global customers, especially for those players who are well-recognized and capitalized—such as Bitcoin and Ethereum.
Our findings produce novel insights on the role of social interactions explaining how global (online) network structures influence the adoption of crypto assets. While this study is able to expand previous research on crypto assets web dynamics (Park and Park 2020 ; Sakas et al. 2022 ), our results are limited by the following constraints. First, we were able to collect web analytics data and map the referral network by using the free version of SimilarWeb. Because of that, we constrained our data collection capacity to no more than five websites that are referred by and referred to. While this does not account for 100% of the referrals, our network still accounts for over 75% of them. Further research can concentrate on using other tools, such as Google Analytics, to collect web analytics data and compare advantages and disadvantages of using different algorithms for the data collection. Second, our study focuses on the most central website and the structure of the referral network, since this is strictly connected to our research objective. We believe that future research should look more in depth into the causal relationship between network centrality and web analytic measures, to test for social influence processes linked to adoption technology. Finally, our study relies on cross-sectional data reflecting individuals’ choices. This has an impact on the possibility to disentangle any sub-process, for instance social selection and social influence, related to innovation adoption. Since it has been recognized that individuals’ choices change over time and only longitudinal research design support this type of analysis, future studies are encouraged to implement a longitudinal research design to investigate how networks evolve in the crypto asset landscape.
The different types of crypto assets are provided in the guidance document ISO/TR 23455:2019. Another work on this topic has been published under the title ISO/TS 23258 “Blockchain and distributed ledger technologies — Taxonomy and Ontology”.
We also triangulate the data obtained from coinmarketcap with the data on coinbase to get the top 200 assets by market capitalization. We find minor discrepancies in the data from coinmarketcap and coinbase in terms of ranking of (some) assets but the top 200 remain the same
Some crypto assets have more than one official registered web address; hence, the number of websites is higher than the number of crypto assets.
Ethereum has even introduced the concept of decentralized social network, a blockchain-based system for social interactions and the sharing of contents (see here: https://ethereum.org/en/social-networks/ ).
Abrahamson, E., Bartner, L.R.: When do bandwagon diffusions roll? How far do they go? and when do they roll backwards? A computer simulation. Acad. Manag. Proc. (1990). https://doi.org/10.5465/ambpp.1990.4978478
Article Google Scholar
Antonakakis, N., Chatziantoniou, I., Gabauer, D.: Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios. J. Int. Finan. Markets. Inst. Money 61 , 37–51 (2019). https://doi.org/10.1016/j.intfin.2019.02.003
Arieli, I., Babichenko, Y., Peretz, R., Young, H.P.: The speed of innovation diffusion in social networks. Econometrica 88 (2), 569–594 (2020). https://doi.org/10.3982/ECTA17007
Ashley, S.R.: Innovation diffusion: Implications for evaluation. In Ottoson, J.M., Hawe, P. (Eds.) Knowledge utilization, diffusion, implementation, transfer, and translation: Implications for evaluation, pp. 35–45. (2009) Jossey-Bass, United States.
Barnett, G.A., Park, H.W.: The structure of international internet hyperlinks and bilateral bandwidth. Ann. Telecommun. 60 , 1115–1132 (2005)
Barnett, G.A., Park, H.W.: Examining the international internet using multiple measures: new methods for measuring the communication base of globalized cyberspace. Qual. Quant. 48 , 563–575 (2014). https://doi.org/10.1007/s11135-012-9787-z
Bartoletti, M., Pompianu, L.: An Empirical analysis of smart contracts: Platforms, applications, and design patterns. In: Brenner, M. et al. (Eds.) Financial Cryptography and Data Security. FC 2017. Lecture Notes in Computer Science(), vol 10323, pp. 494–509. Springer, Cham (2017).
Bharadwaj S, Deka S (2021) Behavioural intention towards investment in cryptocurrency: an integration of Rogers’ diffusion of innovation theory and the technology acceptance model. Forum Scientiae Oeconomia 9(4), 137–159 https://doi.org/10.23762/FSO_VOL9_NO4_7 .
Bizzi, L., Labban, A.: The double-edged impact of social media on online trading: opportunities, threats, and recommendations for organizations. Bus. Horiz. 62 (4), 509–519 (2019). https://doi.org/10.1016/j.bushor.2019.03.003
Bostrom, R.P., Heinen, J.S.: MIS Problems and failures: a socio-technical perspective, Part II: the application of socio-technical theory. MIS Q. 1 (4), 11–28 (1977). https://doi.org/10.2307/249019
Burt, R.S.: Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge (1992)
Book Google Scholar
Can, U., Alatas, B.: A new direction in social network analysis: online social network analysis problems and applications. Physica A 535 , 122372 (2019). https://doi.org/10.1016/j.physa.2019.122372
Chao, C.W., Reid, M., Lai, P.H., Reimers, V.: Strategic recommendations for new product adoption in the Chinese market. J. Strateg. Mark. 28 (2), 176–188 (2020)
Chiu, I. H.-Y.: Regulating crypto-finance: a policy blueprint. ECGI Working Paper Series in Law, no 570/2021 (2021).
Cho, Y., Hwang, J., Lee, D.: Identification of effective opinion leaders in the diffusion of technological innovation: a social network approach. Technol. Forecast. Soc. Chang. 79 , 97–106 (2012). https://doi.org/10.1016/j.techfore.2011.06.003
Coinmarketcap: API Documentation (2021). Accessed from: https://coinmarketcap.com/api/documentation/v1/ # Accessed on 30/09/2021
De Bruyn, A., Lilien, G.L.: A multi-stage model of word-of-mouth influence through viral marketing. Int. J. Res. Mark. 25 (3), 151–163 (2008). https://doi.org/10.1016/j.ijresmar.2008.03.004
Dowling, M.: Is non-fungible token pricing driven by cryptocurrencies? Financ. Res. Lett. 44 , 102097 (2022). https://doi.org/10.1016/j.frl.2021.102097
Drobetz, W., Momtaz, P.P., Schröder, H.: Investor sentiment and initial coin offerings. J. Altern. Invest. 21 (4), 41–55 (2019)
Drummer, D., Neumann, D.: Is code law? Current legal and technical adoption issues and remedies for blockchain-enabled smart contracts. J. Inf. Technol. 35 (4), 337–360 (2020). https://doi.org/10.1177/0268396220924669
Elgin, D.J.: Utilizing hyperlink network analysis to examine climate change supporters and opponents. Rev. Policy Res. 32 (2), 226–245 (2015). https://doi.org/10.1111/ropr.12118
Eryiğit, C., Eryiğit, M.: The diffusion process of Bitcoin. Glob. Local. Econ. Rev. 25 (1), 73–90 (2021)
Google Scholar
Ferrari, V.: The regulation of crypto-assets in the EU—investment and payment tokens under the radar. Maastricht J. Eur. Comp. Law 27 (3), 325–342 (2020). https://doi.org/10.1177/1023263X20911538
Feyen, E.H.B., Kawashima, Y., Mittal, R.: Crypto-assets activity around the world: evolution and macro-financial drivers. https://doi.org/10.1596/1813-9450-9962 (2022). Accessed 15 June 2023.
Forbes: 10 predictions for blockchain, crypto assets, DeFi, And NFTs For 2022. https://www.forbes.com/sites/philippsandner/2022/01/13/10-predictions-for-blockchain-crypto-assets-defi-and-nfts-for-2022/?sh=7db191564911 (2022). Accessed 12 January 2023.
Gandal, N., Hamrick, J.T., Moore, T., Oberman, T.: Price manipulation in the bitcoin ecosystem. J. Monet. Econ. 95 , 86–96 (2018). https://doi.org/10.1016/j.jmoneco.2017.12.004
Griffin, J.M., Sharms, A.: Is bitcoin really untethered? J. Financ. 75 (4), 1913–1964 (2020). https://doi.org/10.1111/jofi.12903
HashemiJoo, M., Nishikawa, Y., Dandapani, K.: Announcement effects in the cryptocurrency market. Appl. Econ. 52 (44), 4794–4808 (2020). https://doi.org/10.1080/00036846.2020.1745747
Hays, D., Kirilenko, A.: The use and adoption of crypto assets. Paper prepared for the Instituto Español de Banca y Finanzas (Spanish Banking and Finance Institute). https://s1.aebanca.es/wp-content/uploads/2019/10/the-use-and-adoption-of-crypto-assets.pdf (2019). Accessed 15 October 2023.
Hedman, J., Beaulieu, T., Karlström, M.: The tales of alphanumerical symbols in media: the case of bitcoin. J. Theor. Appl. Electron. Commer. Res. 16 (7), 2768–2792 (2021). https://doi.org/10.3390/jtaer16070152
Hua, Y., Horta Ribeiro, M., Ristenpart, T., West, R., Naaman, M.: Characterizing Alternative Monetization Strategies on YouTube. In: Proceedings of the ACM on Human-Computer Interaction 6 CSCW2, 283 (2022). https://doi.org/10.1145/3555174 .
Hyun Kim, J.: A hyperlink and semantic network analysis of the triple helix (University-Government-Industry): the interorganizational communication structure of nanotechnology. J. Comput.-Mediat. Commun. 17 (2), 152–170 (2012). https://doi.org/10.1111/j.1083-6101.2011.01564.x
Jansen, B.J.: Understanding user-web interactions via web analytics. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02264-7
Koroma, J., Rongting, Z., Muhideen, S., Akintunde, T.Y., Amosun, T.S., Dauda, S.J., Sawaneh, I.A.: Assessing citizens’ behavior towards blockchain cryptocurrency adoption in the Mano River Union States: Mediation, moderation role of trust and ethical issues. Technol. Soc. 68 , 101885 (2022). https://doi.org/10.1016/j.techsoc.2022.101885
Li, J., Greenwood, D., Kassem, M.: Blockchain in the construction sector: a socio-technical systems framework for the construction industry. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 51–57. Springer, Cham (2019)
Chapter Google Scholar
Lusher, D., Ackland, R.: A Relational hyperlink analysis of an online social movement. J. Social Struct. 12(5) (2010). Available at: https://www.cmu.edu/joss/content/articles/volume12/Lusher/ .
Ma, L., Lee, C.S., Goh, D.H.-L.: Understanding news sharing in social media: an explanation from the diffusion of innovations theory. Online Inf. Rev. 38 (5), 598–615 (2014). https://doi.org/10.1108/OIR-10-2013-0239
McConaghy, T., Marques, R., Müller, A., De Jonghe, D., McConaghy, T., McMullen, G., Henderson, R., Bellemare, S., Granzotto, A.: Bigchaindb: a scalable blockchain database. https://git.berlin/bigchaindb/site/raw/commit/b2d98401b65175f0fe0c169932ddca0b98a456a6/_src/whitepaper/bigchaindb-whitepaper.pdf (2016). Accessed 15 February 2023.
Mohanta, B.K., Panda, S.S., Jena, D.: An overview of smart contract and use cases in blockchain technology. In: Paper presented at the 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (2018). https://doi.org/10.1109/ICCCNT.2018.8494045 .
Moser, S., Brauneis, A.: Should you listen to crypto youtubers? Financ. Res. Lett. 54 , 103782 (2023). https://doi.org/10.1016/j.frl.2023.103782
Müller, E., Peres, R.: The effect of social networks structure on innovation performance: a review and directions for research. Int. J. Res. Mark. 36 (1), 3–19 (2019). https://doi.org/10.1016/j.ijresmar.2018.05.003
Myers, B., Park, S.Y., Nakano, Y., Mueller, G., Ko, A.: How designers design and program interactive behaviors. In: Paper presented at the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing (2008). https://doi.org/10.1109/VLHCC.2008.4639081 .
Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf (2008). Accessed 26 June 2023.
Newman, M.: Assortative mixing in networks. Phys. Rev. Lett. 89 (20), 208701 (2002). https://doi.org/10.1103/PhysRevLett.89.208701
Olney, C.: Crypto-punditry and the media neutrality crisis. Atlantic J. Commun. 30 (4), 379–396 (2022). https://doi.org/10.1080/15456870.2021.1936525
Park, H.W.: Hyperlink network analysis: a new method for the study of social structure on the web. Connections 25 (1), 49–61 (2003)
Park, S., Park, H.W.: Diffusion of cryptocurrencies: web traffic and social network attributes as indicators of cryptocurrency performance. Qual. Quant. 54 (1), 297–314 (2020). https://doi.org/10.1007/s11135-019-00840-6
Park, H.W., Thelwall, M.: Hyperlink analyses of the world wide web: a review. J. Comput. Med. Commun. JCMC843 (2003). https://doi.org/10.1111/j.1083-6101.2003.tb00223.x
Prell, C.: Social network analysis: history, theory, and methodology. SAGE, Thousand Oaks (2012)
Rogers, E.: Diffusion of innovations, 5th edn. Free Press, New York (2003)
Rosen, D., Barnett, G.A., Kim, J.H.: Social networks and online environments: when science and practice co-evolve. Soc. Netw. Anal. Min. 1 (1), 27–42 (2011). https://doi.org/10.1007/s13278-010-0011-7
Rosenfeld, M.: Overview of colored coins. White paper. https://allquantor.at/blockchainbib/pdf/rosenfeld2012overview.pdf (2012). Accessed 14 May 2023.
Sakas, D.P., Giannakopoulos, N.T., Kanellos, N., Migkos, S.P.: Innovative cryptocurrency trade websites’ marketing strategy refinement, via digital behavior. IEEE Access 10 , 63163–63176 (2022). https://doi.org/10.1109/ACCESS.2022.3182396
Sawang, S., Sun, Y., Salim, S.A.: It’s not only what I think but what they think! The moderating effect of social norms. Comput. Educ. 76 , 182–189 (2014). https://doi.org/10.1016/j.compedu.2014.03.017
Shaw-Ching Liu, B., Madhavan, R., Sudharshan, D.: DiffuNET: The impact of network structure on diffusion of innovation. Eur. J. Innov. Manag. 8 (2), 240–262 (2005)
Shen, D., Urquhart, A., Wang, P.: Does twitter predict bitcoin? Econ. Lett. 174 , 118–122 (2019). https://doi.org/10.1016/j.econlet.2018.11.007
Shin, D., Rice, J.: Cryptocurrency: a panacea for economic growth and sustainability? a critical review of crypto innovation. Telematics Inform. 71 , 101830 (2022). https://doi.org/10.1016/j.tele.2022.101830
SimilarWeb. SimilarWeb data methodology. https://support.similarweb.com/hc/en-us/articles/360001631538 (2021). Accessed 11 November 2022.
Sousa, A., Calçada, E., Rodrigues, P., Pinto Borges, A.: Cryptocurrency adoption: a systematic literature review and bibliometric analysis. EuroMed J. Bus. 17 (3), 374–390 (2022). https://doi.org/10.1108/EMJB-01-2022-0003
Sproull, L., Kiesler, S.: Computers, networks and work. Sci. Am. 265 (3), 116–123 (1991)
Tandon, C., Revankar, S., Palivela, H., Parihar, S.S.: How can we predict the impact of the social media messages on the value of cryptocurrency? Insights from big data analytics. Int. J. Inf. Manag. Data Insights 1 (2), 100035 (2021). https://doi.org/10.1016/j.jjimei.2021.100035
van Tonder, R., Trockman, A. and Le Goues, C.: A panel data set of cryptocurrency development activity on GitHub. In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR) (pp. 186–190). IEEE. (2019)
Valente, T.W.: Network interventions. Science 337 (6090), 49–53 (2012). https://doi.org/10.1126/science.121733
Valente, T.W., Rogers, E.M.: The origins and development of the diffusion of innovations paradigm as an example of scientific growth. Sci. Commun. 16 (3), 242–273 (1995). https://doi.org/10.1177/1075547095016003002
van der Linden, M.J., van Beers, C.: Are private (digital) moneys (disruptive) social innovations? an exploration of different designs. J. Soc. Entrep. 8 (3), 302–319 (2017). https://doi.org/10.1080/19420676.2017.1364287
Vishwanath, A., Barnett, G.A.: Advances in the diffusion of innovation. Peter Lang Publishing, New York (2011)
Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994). https://doi.org/10.1017/CBO9780511815478
Yahoo News: Musk breakup tweets bruise bitcoin. (2021). Accessed from: https://uk.news.yahoo.com/finance/news/musk-tweet-dents-bitcoin-weekly-015205776.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAE_NB1aDpwIYAB5JEiMTUxzGDoyvRDHo62lT4EfLD3SdUECnQyLaj_r4zi_dtiVH24w4TKRK9D3QvXZnALMssd8JF8QvHVGA0caiDHw8-JP4-ETpB_-k5KtSbYJHpAjHsGYIhEjE8a-_QJiBOi_5AVph2ECUFFF29qeo-YGeQJ8U . Accessed on 21/08/2023
Yang, X., Dong, M., Chen, X., Ota, K.: Recommender system-based diffusion inferring for open social networks. IEEE Trans. Comput. Soc. Syst. 7 (1), 24–34 (2019). https://doi.org/10.1109/TCSS.2019.2950139
Yi, Y., Zhang, Z., Yang, L.T., Deng, X., Yi, L., Wang, X.: Social interaction and information diffusion in Social Internet of Things: dynamics, cloud-edge, traceability. IEEE Internet Things J. 8 (4), 2177–2192 (2020). https://doi.org/10.1109/JIOT.2020.3026995
Ying, T., Norman, W.C., Zhou, Y.: Online networking in the tourism industry: a webometrics and hyperlink network analysis. J. Travel Res. 55 (1), 16–33 (2016). https://doi.org/10.1177/0047287514532371
Zhang, L., Peng, T.-Q.: Breadth, depth, and speed: diffusion of advertising messages on microblogging sites. Internet Res. 25 (3), 453–470 (2015). https://doi.org/10.1108/IntR-01-2014-0021
Download references
The authors have no relevant financial or non-financial interests to disclose.
Authors and affiliations.
School of Business, Operations and Strategy, University of Greenwich, London, UK
Srinidhi Vasudevan, Anna Piazza & Stefano Ghinoi
Dipartimento di Comunicazione ed Economia, University of Studi Modena e Reggio Emilia, Modena, Italy
Stefano Ghinoi
Department of Economics and Management, University of Helsinki, Helsinki, Finland
Trieste Laboratory on Quantitative Sustainability, Trieste, Trieste, Italy
You can also search for this author in PubMed Google Scholar
Correspondence to Srinidhi Vasudevan .
Conflict of interest.
The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.
Publisher's note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .
Reprints and permissions
Vasudevan, S., Piazza, A. & Ghinoi, S. Information diffusion in referral networks: an empirical investigation of the crypto asset landscape. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01978-8
Download citation
Accepted : 04 September 2024
Published : 11 September 2024
DOI : https://doi.org/10.1007/s11135-024-01978-8
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
/ September 12, 2024
Atlas: Combining TradFi Performance With DeFi Transparency | Eugene & Frankie
In this episode, we’re joined by Frankie from Paradigm, and Eugene from Ellipsis Labs! We discussed the launch of the Atlas L2 private testnet, why Atlas is utilizing the SVM to build an Ethereum L2, and design decisions made along the way. Additionally, we dove into Atlas’ approach to handling MEV. Finally, we covered what the Atlas application set needs to contain on mainnet launch, and first party vs third party apps
Thanks for tuning in!
Atlas Announcement Thread: https://x.com/atlasxyz/status/1833880969251524973
Namada is the shielded asset hub rewarding you to protect the multichain. Enabling data protection for any existing asset, app, or chain, Namada introduces shielded cross-chain actions and rewards for shielding your assets, which strengthens data protection guarantees for everyone.
Namada is currently in its mainnet launch phase — follow along on namada.net
Join us at Permissionless III. Use code BELL10 for a 10% discount: https://blockworks.co/event/permissionless-iii
Follow Frankie: https://x.com/FrankieIsLost
Follow Eugene: https://x.com/0xShitTrader
Follow Mike: https://x.com/MikeIppolito_
Subscribe on YouTube: https://bit.ly/3R1D1D9
Subscribe on Apple: https://apple.co/3pQTfmD
Subscribe on Spotify: https://spoti.fi/3cpKZXH
Timestamps:
(0:00) Introduction
(2:25) The Atlas L2
(4:47) Why Launch An SVM L2?
(10:01) The Differences Between SVM and EVM
(14:48) Opinionated vs Unopinionated Design Philosophy
(19:48) Token Incentivization and L2 Business Models
(27:00) Atlas Design Decisions
(35:11) Namada Ad
(36:10) The Great Rollup Debate
(42:09) Why Choose Ethereum As The Atlas Settlement Layer?
(51:31) Handling MEV On Atlas
(55:52) Should MEV Be Returned To Users?
(1:01:57) Developing More Apps
(1:04:54) First Party Apps vs Third Party Apps
Disclaimer: Nothing said on Bell Curve is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Mike, Jason, Michael, Vance and our guests may hold positions in the companies, funds, or projects discussed.
by admin | Mar 22, 2024 | Crypto Fund Research , Crypto Funds , Crypto Hedge Funds , Research Reports , Top Crypto Funds | 0 Comments
Q4 2023 Crypto Fund Report Download 2023 Q4 Crypto Fund Report as .pdf:
by admin | Dec 29, 2023 | Crypto Fund Research , Crypto Funds , Crypto Hedge Funds , Research Reports , Top Crypto Funds | 0 Comments
Q2 2023 Crypto Fund Report Download 2023 Q2 Crypto Fund Report as .pdf:
Q3 2023 Crypto Fund Report Download 2023 Q3 Crypto Fund Report as .pdf:
by admin | Oct 16, 2023 | Crypto Fund Research , Crypto Hedge Funds , Research Reports | 0 Comments
FOR IMMEDIATE RELEASE Contacts: Crypto Fund Research Joshua Gnaizda, CEO San Francisco, CA [email protected] Pull Assets From Crypto Hedge Funds Even as Funds Outperform Fund flow data from Crypto Fund Research reveals that investors withdrew more...
by admin | Jul 14, 2023 | Crypto Venture , Cryptocurrency Venture Capital Funds | 0 Comments
Introduction to Crypto Venture Capital Funds Cryptocurrency has revolutionized the world of finance and investment, offering new and exciting opportunities for individuals and businesses alike. One such avenue that has emerged is the realm of crypto venture capital...
IMAGES
VIDEO
COMMENTS
Crypto Fund Research leverages more than a decade of alternative investment research experience to conduct its own cutting edge research and collaborates with hundreds of crypto funds - the result is the largest and most comprehensive database of crypto hedge funds and venture capital funds. Our online and downloadable database of crypto ...
Find over 800 crypto funds investing in crypto and blockchain, with information on fund name, city, country, launch year, and type. Compare hedge funds, venture capital, private equity, and more categories of crypto funds.
Crypto Fund Research is the leading provider of data and market intelligence covering crypto hedge funds and venture capital. Based in San Francisco and founded in 2017, Crypto Fund Research is the most trusted source of data, insights, and tools for crypto and blockchain investment professionals around the globe. View Products.
Annually, global venture capital firms in crypto/blockchain raised $5.75b in 2023 across 58 funds, down from 2022's record year of $37.7b across 262 funds. When comparing crypto venture firm's share of global venture capital fundraising, crypto funds tallied roughly 3.53% of global funding down from 2022's high of 12.62%.
San Francisco-based Crypto Fund Research was founded in 2017 to provide insight into the evolution of the crypto funds industry. It maintains a database of crypto funds and venture capital, with ...
July 12, 2023. The number of traditional hedge funds investing in crypto-assets fell to 29% - down from 37% last year - however no traditional hedge fund plans to decrease exposure in 2023. 23% of traditional hedge funds are reassessing their crypto strategy due to the regulatory environment in the US; 12% of crypto hedge funds are ...
Crypto Market Outlook 2024: ETFs Offer Tailwinds for Other Digital Assets. With the expected launch of a spot bitcoin ETFs in Q1 of 2024, broader crypto is coming of age, says CoinDesk Indices ...
The latest report from Crypto Fund Research shows that AUM surpassed $35 million, and reveals that in last summer's Crypto Fund Survey, over 60% expected bitcoin's price to reach $20,000 by ...
Crypto Hedge Funds Traditional Hedge Funds. Big Picture. Growth Individual funds have grown by an 67% who currently invest average of 150% in 2021 (US$23.4m intend to deploy more to US$58.6m) capital by the end of 2022. The percentage of crypto hedge funds with AuM over US$20 million increased in 2021 to 59%, from 46%, which is significant as ...
3. Of those hedge funds who are invested in digital assets, 57% have a toe-hold position with less than 1% of their total hedge fund AuM invested. 4. Two thirds of those hedge funds (67%) who are currently investing in digital assets intend to deploy more capital into the asset class by the end of 2022. 5. 29% of hedge fund managers who are not ...
Explore Full Pro Research Reports with Messari Pro and unlock Enterprise research reports after 90 days. Track & analyze multiple assets on one comprehensive chart. Understand what's happening at a glance with the most important insights. Elevate your insights with our AI-powered Newsfeed summarizing news from hundreds of sources.
The data contained in this report comes from research that was conducted in Q1 2020 across the largest global crypto hedge funds by assets under management (AuM). This ... crypto fund universe, we can see that quant funds are the most prevalent and make up almost half of crypto hedge funds in the market today. The remaining strategies -
Learn how crypto investment funds can give you exposure to digital assets without direct trading. Compare crypto index funds, ETFs, hedge funds and VC funds and their features, advantages and risks.
Crypto index funds offer a simple and convenient way to gain exposure to the cryptocurrency market without having to do extensive research or purchase and manage individual digital assets. This can be particularly appealing for investors who are new to crypto investing or have limited time and resources to dedicate to actively managing a ...
Grayscale Crypto Sectors. ... to send you the prospectus (when available) if you request it by calling (833) 903-2211 or by contacting Foreside Fund Services, LLC, Three Canal Plaza, Suite 100, Portland, Maine 04101. ... This information should not be relied upon as research, investment advice, or a recommendation regarding any products ...
Crypto Fund Research. "Cumulative value of assets under management (AUM) of crypto funds worldwide from 1st quarter 2018 to 2nd quarter 2022 (in million U.S. dollars)." Chart. September 15, 2022.
The global crypto market cap is $2.05T, ... In January 2024 the SEC approved 11 exchange traded funds to invest in Bitcoin. ... Please keep me updated by email with the latest crypto news, research findings, reward programs, event updates, coin listings and more information from CoinMarketCap.
Crypto Fund Performance Awards - The top performing crypto funds for year, quarter, and strategy. In the News - Coverage of Crypto Fund Research in the WSJ, Bloomberg, Forbes, MarketWatch, CoinDesk, and other leading news publications. Research Reports - Crypto fund quarterly reports, surveys, and other industry research. Contact
Crypto Fund Research is the premier resource for timely data and market intelligence on crypto hedge funds and VCs. TRY DEMO. SIGN UP. The Crypto Fund Performance Database is the only database providing performance information on more than 250 crypto hedge funds and VCs. In addition, the database contains detailed fund info including lockups ...
Crypto Theses for 2024. Key trends, people, companies, and projects to watch across the crypto landscape, with predictions for 2024. ... Professional-grade Research Reports covering the latest trends and assets in the crypto space. Fundraising Screener to track trends across 14,000+ crypto funding rounds, 500+ M&A Deals, and 10,000+ investors.
Crypto hedge funds are proliferating at an accelerating pace - estimated to now number more than 300. NEW YORK, 8 June 2022 - Even with the tremendous volatility in the sector, there are many more traditional hedge funds investing in crypto and more specialist crypto funds being created as the digital asset class gains acceptance. Of traditional hedge funds surveyed, 38% are currently ...
Crypto Fund Research's 2020 Crypto Fund Survey was conducted in Q2/Q3 and had over 80 respondents representing a wide variety of crypto funds including crypto hedge funds, venture funds, fund of ...
US News is a recognized leader in college, grad school, hospital, mutual fund, and car rankings. Track elected officials, research health conditions, and find news you can use in politics ...
In the last decades, crypto assets have become particularly popular in financial markets. However, public awareness of the crypto asset landscape is rather limited, and usually associated with sensationalized media coverage of a handful of cryptocurrencies. Moreover, while users of crypto assets primarily collect information on Internet, there is a limited understanding of the relational ...
The percentage of crypto hedge funds using an independent custodian decreased in 2020 from 81% to 76%. The percentage with at least one independent director on their board decreased from 43% to 38% in 2020. The percentage of crypto hedge funds using third party research increased from 38% to 47% in 2020.
US News is a recognized leader in college, grad school, hospital, mutual fund, and car rankings. Track elected officials, research health conditions, and find news you can use in politics ...
Leader in cryptocurrency, Bitcoin, Ethereum, XRP, blockchain, DeFi, digital finance and Web 3.0 news with analysis, video and live price updates.
In this episode, we're joined by Frankie from Paradigm, and Eugene from Ellipsis Labs! We discussed the launch of the Atlas L2 private testnet, why Atlas is utilizing the SVM to build an Ethereum L2, and design decisions made along the way. Additionally, we dove into Atlas' approach to handling MEV. Finally, we covered what the Atlas application set needs to contain on mainnet launch, and ...
Crypto Fund Performance Awards - The top performing crypto funds for year, quarter, and strategy. In the News - Coverage of Crypto Fund Research in the WSJ, Bloomberg, Forbes, MarketWatch, CoinDesk, and other leading news publications. Research Reports - Crypto fund quarterly reports, surveys, and other industry research. Contact
About Crypto Fund Research - Learn more about how Crypto Fund Research delivers the most up-to-date coverage of crypto funds. Who We Help. Blockchain Startups and Founders - We help startups and their founders connect directly with hundreds of VCs actively investing in blockchain and crypto companies,