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The production and consumption of information on Bitcoin and other digital-, or crypto-, currencies have grown, along with their market capitalization. However, a systematic investigation of the relationship between online attention and market dynamics across multiple digital currencies is still lacking. Here, we quantify the interplay between the attention to digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages and their views history from July First, we quantify the evolution of cryptocurrency presence in Wikipedia by analyzing the editorial activity and the network of co-edited pages. We found that a small community of tightly connected editors are responsible for most of the production of information about cryptocurrencies in Wikipedia. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate that it will be of interest for researchers as well as investors. The cryptocurrency market grew super-exponentially for more than 2 years until January , before suffering significant losses in the subsequent months ElBahrawy et al. The consequence and driver of this growth is the attention it has progressively attracted from an increasingly larger public. In this paper, we quantify the evolution of the production and consumption of information concerning the cryptocurrency market as well as its interplay with market behavior. Capitalizing on recent results showing that Wikipedia can be used as a proxy for the overall attention on the web Yoshida et al. The first peer to peer currency system, Bitcoin, was created in as a realization of Satoshi Nakamoto's novel idea Nakamoto, of a digital currency. The system relies on the Blockchain technology and was built to introduce a transparent, anonymous, and decentralized digital currency. In the beginning, Bitcoin attracted technology enthusiasts, open source advocates, and whoever may need fewer restrictions on across country money transfers. In less than 10 years, Bitcoin gained popularity and was joined by more than 2, cryptocurrencies 1. Some of these cryptocurrencies altcoins are replicas of Bitcoin with small changes in terms of protocols and implementation, while others adopted entirely different protocols. Although cryptocurrencies were first introduced as a media of exchange for daily payments Ali et al. Cryptocurrencies can be traded in online exchange platforms and extensive research has looked at the nature and main usages of Bitcoin, specifically in the hope of finding some hints on the price drivers Kristoufek, ; Ciaian et al. Comparisons between cryptocurrencies exchange market and the stock market Ali et al. Social media platforms nowadays provide researchers with a vast amount of data that can signal public opinions or interests. Since stock markets are highly influenced by the rationale of investors and their interests, several studies investigated the link between online social signals and stock market prices. Pioneering studies showed how signals from Google trends and Wikipedia Moat et al. This approach has recently been extended to investigate the relationship between social digital traces and the price of Bitcoin Kristoufek, ; Garcia et al. While these studies showed the importance of relying on different digital sources, a systematic investigation of multiple cryptocurrencies has been lacking so far. Furthermore, only in a few cases Colianni et al. Here, we investigate the interplay between the consumption and production of information in Wikipedia and market indicators. Our analysis focuses on all cryptocurrencies with a page on Wikipedia, from July until January Two main approaches have been suggested to anticipate Bitcoin and cryptocurrency prices. The first relies on market indicators only and uses mostly algorithmic trading and machine learning algorithms to predict prices Chang et al. The second relies instead on users' data generated online, including Google search trends, Wikipedia views and Twitter data, to predict and rationalize price fluctuations. Although the relevance of altcoins has been increasing ElBahrawy et al. Google search trends, Wikipedia views, and Twitter data were found to correlate positively with Bitcoin prices Kristoufek, ; Garcia et al. Comments and replies on Bitcoin 2 , Ethereum 3 , and Ripple forums 4 were found to anticipate their respective prices Kim et al. Similar results were obtained considering data from the social news aggregator Reddit, for Bitcoin, Litecoin, Ethereum, and Monero Phillips and Gorse, , b. In Kristoufek and Phillips and Gorse a , the authors showed a positive correlation between multiple online signals and the prices of Bitcoin, Litecoin, Ethereum, and Monero. The connection between Bitcoin prices and online social signals has allowed the development of successful trading strategies Garcia and Schweitzer, ; Kim et al. In Kim et al. Research focusing on the nature of community discussions and the activity of contributors is very limited. In Jahani et al. Wikipedia data was collected through the Wikipedia API 6 and include the daily number of views and the page edit history of the 38 cryptocurrencies with a page on Wikipedia see Supplementary Material S1. Page-view data range from July 1st, until January 23rd, , since earlier data are not accessible through the API. On the other hand, full editing history is accessible through the API, and includes the content of each edit, the editor, the time of creation and the comments to the edits. We excluded all edits from bots from our analysis. We classified edits into two categories, namely edits with new content and maintenance edits. Maintenance edits aim to keep consensual page content by restoring more accurate old version reverts and fighting malicious edits vandalism. We created an MD5 hash for all edits, and we identified edits sharing the same hash with a previous edit as reverts. We considered all edits, that were neither classified as vandalism nor reverts, as new content. We also collected data on the activity of the most active editors in other Wikipedia pages. To retrieve this data, we used Xtool 7 , a web tool that provides general statistics on the editors and their most edited pages. The price of a cryptocurrency represents its exchange rate with USD or Bitcoin, typically which is determined by the market supply and demand dynamics. The exchange volume is the total trading volume across exchange markets. The market capitalization is calculated as a product of a cryptocurrency's circulating supply the number of coins available to users and its price. The market share is the market capitalization of a cryptocurrency normalized by the total market capitalization of the market. Price and market capitalization data is only available from April 28th, , while volume data is available from December 27th, We compiled a list of 17 such cryptocurrencies from active exchange platforms including Poloniex and Bitfinex see Supplementary Material S2. Note that these are also the most widely traded currencies 1. In our analysis, we consider that cryptocurrencies can be traded once their trading volume exceeds , USD. We excluded days where the reported volume did not lie within 2 standard deviations from the average trading volume, which are likely due to how market exchanges report their exchange volumes 8. In this section, we investigate the connection between a cryptocurrency performance in the market and the attention it attracts on Wikipedia. Wikipedia is the 5th most visited website on the Internet 9 , attractive to a non-expert audience seeking compact and non-technical information. Previous work has shown that Wikipedia traffic can help to predict stock market prices Moat et al. The number of cryptocurrency pages on Wikipedia has grown along with their overall market capitalization. In August , Ripple became the first cryptocurrency with a page. At that point, it was not identified as a cryptocurrency, but as the idea of a monetary system relying on trust. Bitcoin appeared only in March , followed by other 36 currencies see Figure 1. The number of views received daily by a Wikipedia page is a good proxy for the overall attention on the web Yoshida et al. We find that the number of views to cryptocurrency pages has increased overall from until January see Figure 2. In , the sudden drop in cryptocurrency prices impacted the number of views. A second aspect characterizing the evolution in time of Wikipedia pages is their edit history. We find that, on average, pages are more edited than in the past. Figure 1. Cryptocurrencies on Wikipedia. Evolution in time of the cumulative number of cryptocurrencies with a Wikipedia page. Figure 2. Market volume and attention to cryptocurrency pages. The market volume USD for all cryptocurrencies with a page in Wikipedia solid blue line , the total number of views to cryptocurrency pages solid orange line , and the total number of edits to cryptocurrency pages solid green line. Values are aggregated using a time window of 3 months. Interestingly, Bitcoin's share of the total market capitalization declined during the same period ElBahrawy et al. We tested this connection considering all cryptocurrencies see Figure 3B and focused on other market properties. Moreover, these correlations are robust in time see Supplementary Material S3. Figure 3. Overall correlation between attention on Wikipedia and market performance. A The temporal evolution of price blue line and number of Wikipedia views orange line for Bitcoin. Values are computed using a time window of 1 week. B Average market share in USD vs. We also found that the average share of edits of a currency is connected to the overall cryptocurrency performance in the market see Figure 3C. These correlations are robust in time see Supplementary Material S3. Note that the observed correlations suggest only a connection between the relative attention to a given currency and its market properties relative to other currencies. The demonstrated connection between cryptocurrency's success in the market and the overall consumption of information on Wikipedia sheds light on the important role of the latter. In the following sections, we focus on the production of information contained in Wikipedia pages, by analyzing the evolution of cryptocurrency pages and the role played by Wikipedia editors. Frequency of edits and editor diversity is considered reliable indicators of the quality of information included in a Wikipedia page Stvilia et al. Cryptocurrency pages differ with respect to their edit history see Figure 4. Some pages, including those of Bitcoin and Ethereum, experience continuous edits throughout their history, while for other pages, including Dash and Cardano, contributions are intermittent in time, with periods of higher activity followed by calmer ones. For example, the change of the Dash logo in April triggered a spike in the number of edits. Figure 4. Example of edit histories. A Distribution of the inter-event time between two consecutive edits for Bitcoin line with filled circles and Dash line with white circles. Edits are shown as vertical black line as a function of time for Bitcoin B and Dash C. The nature of edits changes over a Wikipedia page life. We find that reverts constitute The fraction of reverts is stable in time see Figure 5A. Only 0. Well-established cryptocurrency pages are less subject to maintenance edits than other pages see Figures 5B,C. Pages of cryptocurrencies forked from Bitcoin such as Bitcoin Cash, Bitcoin Private, and Bitcoin Gold were the source of many debates Caffyn, resulting in a high number of maintenance edits see Figure 5B. Figure 5. Reverts and vandalism revisions. Values are aggregated using a time-window of 1 year. B,C The fraction of reverts B and vandalism C edits for the top 10 cryptocurrencies sorted by number of reverts and vandalism edits, respectively. Interestingly, this growth does not characterize all pages on Wikipedia. For example, in Heilman and West , the authors show that the number of editors in medical related articles has been decreasing. Figure 6. Uneven distribution of contributions of Wikipedia editors. A Distribution of share of edits between and red solid line. B The number of editors contributing to cryptocurrency pages. Values are aggregated using 1-year time window. C Histogram of editors based on the number of Wikipedia pages they have contributed. The editing activity is heterogeneously distributed, as found by ranking the editors according to the number of edits see Figure 6A. This result is in line with what is generally observed in Wikipedia Muchnik et al. We then studied the evolution of editors' activity in time. We found that the higher the cumulative activity of a group, the more recently they started editing the pages see Figure 7 , in contrast to what is generally observed on Wikipedia Kittur et al. Note that the group of most active contributors started editing in August , 3 years after the creation of Bitcoin's page. Furthermore, Figure 8 shows that editors with the largest number of edits are responsible for the most extensive contributions in terms of the number of edited words. Some of their edits, however, may be for maintenance. This value is consistent among different ranking groups. Figure 7. Active editors per group. The number of active editors per group from until Results are computed using a temporal window of 1 year. Editors are divided into four groups based on their total number of edits: More than edits blue line , to edits purple line , 20 to edits green line , less than 20 edits red line. Editors were classified according to their total contributions at January 23rd , then traced back. Figure 8. The activity of editors in different groups. The average number of words per editor. All results are computed over a temporal window of days between August and January The four lines represent four groups of editors: those who contributed more than total edits blue line , to edits purple line , 20 to edits green line , less than 20 edits red line. Figure 9. The focus of editors. Editors are ranked based on the total number of edits in descending order and grouped based on their rank. A The fraction of maintenance edits for each rank group. B The average number of contributed pages for each rank group. Only editors with more than one edit are considered. To understand the general interests and the specialization of the top editors of the cryptocurrency Wikipedia pages, we focused on a subset of 6 editors that have contributed at least edits each. We studied their interests in detail, considering their contribution over the entire Wikipedia. Our results showed that the main interests of these editors are cryptocurrencies and blockchain see Figure Top editors also contribute in other non-cryptocurrency related pages; however, these pages are less homogeneous and include several different interests such as; genetically modified food, musicians, and motor companies see Supplementary Material S4. Figure The activity of the top 6 cryptocurrency pages editors. A The top 10 pages by the number of editors. The x-axis shows the number of top editors who had this page in their top edited pages. Note that here we consider only the top 10 pages per editor. B The top 10 pages by the number of edits. The x-axis shows the total number of edits per page. Results are obtained for the subset of 6 most active editors. We further studied the network of co-edited Wikipedia pages. We constructed an undirected weighted graph, where the nodes are Wikipedia pages; an edge exists between two nodes if they have at least one common editor, and link weights correspond to the number of common editors. Bitcoin has the highest degree of centrality throughout the entire period considered see Supplementary Material S9. Evolution of the network of cryptocurrency pages. Nodes represent Wikipedia pages and edge exist between two nodes if they have at least one common editor. The radius of a node is proportional to the sum of weights of incoming links and the edge thickness is proportional to the edge weight, measured as the number of common editors. The network is aggregated over a different period of times: A from July until July , B from July until July , C from until July , D for the entire period of study. If weekly time windows are considered instead, we find that the network is disconnected see Figure Typically, new pages are created by new editors. On average, new pages connect to the giant component within 5. Short-term dynamics of the Wikipedia network evolution. The cumulative number of new nodes dashed line and the total number of network components solid line. Values are aggregated using a 1 week time window. The demonstrated connection between how successful a cryptocurrency is and the attention it draws on Wikipedia suggests that the latter could help in informing a successful investment strategy. We investigated this possibility by testing a Wikipedia-based strategy similar to the one proposed in Moat et al. This trading position is formally known as a short position. We considered the closing price and the total number of views calculated over the entire day. The intuition behind the strategy is that if attention and information gathering has been rising, prices will drop, and vice-versa Tversky and Kahneman, ; Moat et al. We consider Wikipedia views rather than edits, since the latter do not vary on a daily basis the average time between edits is We also consider that a longer period would overlook the cryptocurrencies' price volatility Brauneis and Mestel, Here, we assume that investor influence is negligible, e. We also considered three baseline strategies. In all other aspects, it is identical to the Wikipedia-based strategy. This will allow us to test which indicator price or Wikipedia page views has better predictive capabilities under the same conditions. The rationale behind the first baseline strategy is that if the price has been rising, a drop will follow, and vice-versa. The performance of the different strategies is assessed by computing the cumulative return R , defined as the summation of log-returns obtained under the proposed strategies. The use of the log return is motivated by the ease of calculation of the short and long positions and since we are considering multi-period returns Hudson and Gregoriou, We tested the Wikipedia-based strategy against the baselines for the 17 cryptocurrencies that have a Wikipedia page and can be marginally traded see list of exchanges with margin trading support in Supplementary Material S2 and list of cryptocurrencies in Supplementary Material S1. Margin trading is a practice of borrowing funds from a broker to trade financial assets, that rely on selling assets one does not yet own. We tested the strategies considering a period from July 1st, until January 23rd, We found that the Wikipedia based strategy outperforms the price based and the random baseline strategies, when one considers the period between July and January see Figure 13A. To evaluate the risk factor in the three strategies, we calculated the Sharpe ratio. The Sharpe ratio is defined as. However, the Sharpe ratio of the Wikipedia strategy does not consistently outperform the baseline strategies along the entire period of study see Supplementary Material S The Wikipedia based investment strategy outperforms the baselines. B The distributions of the daily returns obtained using the Wikipedia-based strategy orange line , the baseline strategy based on prices blue line , and the random strategy gray line. Data is displayed using a kernel density estimate, with a Gaussian kernel and bandwidth calculated using Silverman's rule of thumb. Data for the random strategy is obtained from independent realizations. All results are shown for investments between July and January for all cryptocurrencies which can be traded marginally combined. A closer inspection shows that there are consistent differences between cryptocurrencies, with respect to the cumulative returns see Figure 14 , with some even yielding overall negative returns. Performance of the strategies for different cryptocurrencies. The cumulative returns along the whole period of investment, following the Wikipedia based strategy A the buy hold strategy B , the price-based baseline strategy C , and the random strategy D for the 17 cryptocurrencies considered. The observed differences could be potentially explained by the correlation or causality between changes in daily price and in Wikipedia views see more details on the correlation and Granger causality for each cryptocurrency in Supplementary Material S4. Instead, we observed that, neither the correlation nor the Granger causality explains the results observed, suggesting that other mechanisms could be in play Garcia and Schweitzer, For example, our proposed strategy does not simply map to buying a cryptocurrency when its Wikipedia page views increase. Finally, we investigated the role of the start and end times of the investment period see Figure We found that, for most of the choices, the Wikipedia-based strategy has a higher cumulative return than the random and price baseline strategy. It outperforms both baseline strategies for the majority of the periods ending before January , when the market entered a period of dramatic losses. Comparison between strategies across different periods of time. In this paper, we investigated the interplay between the production and consumption of information about digital currencies in Wikipedia and their market performance. We have shown that there is a positive correlation between a cryptocurrency's overall success in the market, as measured by its price, volume, and market share and the overall attention gained by its Wikipedia page, measured by the number of page views and the number of page edits. This result suggests that the production and consumption of information in Wikipedia is relevant for investment purposes. We have analyzed the edit history of cryptocurrency pages in Wikipedia. We have shown that contributions to cryptocurrency pages are bursty in time, with periods of high activity followed by calmer ones. Also, we have found that the number of cryptocurrency page editors has increased in the period considered, while this is not the case for editors of other topics in Wikipedia. However, very few editors are responsible for most of the edits, consistent with the rest of Wikipedia. Interestingly, this subset of editors have started contributing relatively recently after , which is also in contrast with the rest of Wikipedia. We have shown that the information in Wikipedia is, to a large extent, provided by cryptocurrency and technology enthusiasts. In fact, we have found that editors who are very active on cryptocurrency pages focus their editing activity almost exclusively on cryptocurrencies and blockchain. We have found that the community of cryptocurrency editors is tight: On average, each page is connected to 37 other pages through an average of 7 editors and active contributors tend to edit many pages. New cryptocurrency pages are typically created by new editors, but then also edited by more experienced ones. For this reason, we find that older pages have a higher degree in the co-editing network. Further investigation of the nature of edits which arises as a response to price changes could uncover another interesting dimension of the relationship between Wikipedia editors and the market. Finally, we have proposed a trading strategy relying on Wikipedia page views, similar to the Wikipedia based strategy proposed for the stock market Moat et al. To further enrich the picture, we have discussed the relative performance between different strategies also by considering the effect of the hypothetical starting and ending period of trading, showing that the Wikipedia strategy is a valid option to be considered. Furthermore, our strategy neglects the role played by fees, which could significantly decrease profits in real scenarios. Finally, for the sake of simplicity and as is customary for a study like ours, we have assumed that investor influence is too small to perturb the market; relaxing this assumption could be an interesting aspect to include in future works. Characterizing the production and consumption of information around cryptocurrencies is key to understanding the market dynamics and in informing investment decisions De Domenico and Baronchelli, Although our study was limited to the analysis of Wikipedia data, other sources of information including traditional news outlets such as Twitter, Reddit, or bitcointalk 2 could reveal important information about cryptocurrency market dynamics. The datasets generated and analyzed for this study along with the code to regenerate the figures can be found in ElBahrawy AE: data acquisition, pre-processing, and analysis. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We would like to thank Miriam Redi from the Wikimedia Foundation for her valuable discussion on the Wikipedia structure. AE acknowledge the support of the Alan Turing Institute. Available online at: coinmarketcap. Bitcoin forum accessed February 19, Ethereum forum accessed February 19, Rippl chat accessed February 19, Available online at: www. Available online at: xtools. Available online at: alexa. Alessandretti, L. Anticipating cryptocurrency prices using machine learning. Complexity , 1— Ali, R. The economics of digital currencies. Bank Engl. Google Scholar. Bollen, J. Twitter mood as a stock market predictor. Computer 44, 91— Brauneis, A. Price discovery of cryptocurrencies: Bitcoin and beyond. Caffyn, G. Ceruleo, P. Bitcoin: a rival to fiat money or a speculative financial asset? Master's thesis. Chang, P. Huang, K. Jo, H. Lee, H. Kang, and V. Bevilacqua Berlin; Heidelberg: Springer , 1— Ciaian, P. The economics of bitcoin price formation. Colianni, S. CS Project. Curme, C. Quantifying the semantics of search behavior before stock market moves. De Domenico, M. The fragility of decentralised trustless socio-technical systems. EPJ Data Sci. Dickerson, A. ElBahrawy, A. Evolutionary dynamics of the cryptocurrency market. Open Sci. Elendner, H. Technical report, Humboldt University, Berlin. Fama, E. Perfect competition and optimal production decisions under uncertainty. Bell J. Gajardo, G. Does bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and djia as the euro, great british pound and yen? Chaos Solitons Fract. Gandal, N. Can we predict the winner in a market with network effects? Games Garcia, D. Social signals and algorithmic trading of bitcoin. The digital traces of bubbles: feedback cycles between socio-economic signals in the bitcoin economy. Interface Glaser, F. Bitcoin-Asset or Currency? Revealing Users' Hidden Intentions. Guo, T. Predicting short-term bitcoin price fluctuations from buy and sell orders. Heilman, J. Wikipedia and medicine: quantifying readership, editors, and the significance of natural language. Internet Res. Hudson, R. Calculating and comparing security returns is harder than you think: a comparison between logarithmic and simple returns. Jahani, E. ACM Hum. Jang, H. An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access 6, — Kaminski, J. Nowcasting the bitcoin market with twitter signals. Kim, Y. Predicting fluctuations in cryptocurrency transactions based on user comments and replies. When bitcoin encounters information in an online forum: using text mining to analyse user opinions and predict value fluctuation. Kittur, A. Kristoufek, L. Bitcoin meets google trends and wikipedia: quantifying the relationship between phenomena of the internet era. What are the main drivers of the bitcoin price? Madan, I. Matta, M. Moat, H. Quantifying wikipedia usage patterns before stock market moves. Muchnik, L. Jr, Havlin, S. Origins of power-law degree distribution in the heterogeneity of human activity in social networks. Nakamoto, S. Panciera, K. Phillips, R. Cryptocurrency price drivers: wavelet coherence analysis revisited. Preis, T. Quantifying trading behavior in financial markets using google trends. Rivest, R. Stenqvist, E. Accessed: 19 February Stvilia, B. Tversky, A. Loss aversion in riskless choice: a reference-dependent model. Wang, S. Buzz factor or innovation potential: what explains cryptocurrencies returns? Yermack, D. Is Bitcoin a Real Currency? An Economic Appraisal. Technical report, National Bureau of Economic Research. Yoshida, M. Blockchain The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. Top bar navigation. About us About us. Sections Sections. About journal About journal. Article types Author guidelines Editor guidelines Publishing fees Submission checklist Contact editorial office. Blockchain , 09 October Blockchain for Web3 and the Metaverse. Introduction The cryptocurrency market grew super-exponentially for more than 2 years until January , before suffering significant losses in the subsequent months ElBahrawy et al. State of the Art Two main approaches have been suggested to anticipate Bitcoin and cryptocurrency prices. Data Collection and Preparation Wikipedia data was collected through the Wikipedia API 6 and include the daily number of views and the page edit history of the 38 cryptocurrencies with a page on Wikipedia see Supplementary Material S1. Results 4. Wikipedia Pages and Market Properties In this section, we investigate the connection between a cryptocurrency performance in the market and the attention it attracts on Wikipedia. Edited by: Claudio J. Tessone , University of Zurich, Switzerland.

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