{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:20:12Z","timestamp":1776086412879,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Centre for Blockchain Technologies, University College London"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPJ Data Sci."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how \u201chealthy\u201d and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it.<\/jats:p><jats:p>In this paper, we investigate whether developers\u2019 emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developers\u2019 comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developers\u2019 comments over time, and test the corresponding time series (i.e. the <jats:italic>affect time series<\/jats:italic>) for correlations and causality with the Bitcoin\/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developers\u2019 emotions and Bitcoin\/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)\u2014associated with the prediction of the prices of cryptocurrencies\u2014significantly decreases when including the affect time series.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-020-00239-6","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:04:52Z","timestamp":1595502292000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["The Butterfly \u201cAffect\u201d: impact of development practices on cryptocurrency prices"],"prefix":"10.1140","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1127-5600","authenticated-orcid":false,"given":"Silvia","family":"Bartolucci","sequence":"first","affiliation":[]},{"given":"Giuseppe","family":"Destefanis","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Ortu","sequence":"additional","affiliation":[]},{"given":"Nicola","family":"Uras","sequence":"additional","affiliation":[]},{"given":"Michele","family":"Marchesi","sequence":"additional","affiliation":[]},{"given":"Roberto","family":"Tonelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"issue":"4","key":"239_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0195200","volume":"13","author":"RC Phillips","year":"2018","unstructured":"Phillips RC, Gorse D (2018) Cryptocurrency price drivers: wavelet coherence analysis revisited. PLoS ONE 13(4):0195200","journal-title":"PLoS ONE"},{"issue":"19","key":"239_CR2","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1080\/00036846.2015.1109038","volume":"48","author":"P Ciaian","year":"2016","unstructured":"Ciaian P, Rajcaniova M, Kancs DA (2016) The economics of Bitcoin price formation. Appl Econ 48(19):1799\u20131815","journal-title":"Appl Econ"},{"key":"239_CR3","doi-asserted-by":"crossref","unstructured":"Cong LW, Ye L, Neng W (2018) Tokenomics: Dynamic adoption and valuation. Becker Friedman Institute for Research in Economics Working Paper (2018-49)","DOI":"10.2139\/ssrn.3222802"},{"key":"239_CR4","doi-asserted-by":"crossref","unstructured":"Bartolucci S, Kirilenko A (2019) A model of the optimal selection of crypto assets. Preprint. arXiv:1906.09632","DOI":"10.2139\/ssrn.3578450"},{"key":"239_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8983590","author":"L Alessandretti","year":"2018","unstructured":"Alessandretti L, ElBahrawy A, Aiello LM, Baronchelli A (2018) Anticipating cryptocurrency prices using machine learning. Complexity. https:\/\/doi.org\/10.1155\/2018\/8983590","journal-title":"Complexity"},{"key":"239_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfds.2018.10.001","author":"H Jing-Zhi","year":"2018","unstructured":"Jing-Zhi H, William H, Jun N (2018) Predicting Bitcoin returns using high-dimensional technical indicators. J Finance and Data Sci. https:\/\/doi.org\/10.1016\/j.jfds.2018.10.001","journal-title":"J Finance and Data Sci"},{"key":"239_CR7","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.chaos.2018.11.014","volume":"118","author":"S Lahmiri","year":"2019","unstructured":"Lahmiri S, Bekiros S (2019) Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos Solitons Fractals 118:35\u201340","journal-title":"Chaos Solitons Fractals"},{"issue":"7","key":"239_CR8","doi-asserted-by":"publisher","DOI":"10.1063\/1.5036517","volume":"28","author":"S Drozdz","year":"2018","unstructured":"Drozdz S, Gabarowski R, Minati L, Oswiecimka P, Watorek M (2018) Bitcoin market route to maturity? Evidence from return fluctuations, temporal correlations and multiscaling effects. Chaos, Interdiscip J Nonlinear Sci 28(7):071101. https:\/\/doi.org\/10.1063\/1.5036517","journal-title":"Chaos, Interdiscip J Nonlinear Sci"},{"issue":"7","key":"239_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/fi11070154","volume":"11","author":"S Drozdz","year":"2019","unstructured":"Drozdz S, Minati L, Oswiecimka P, Stanuszek M, Watorek M (2019) Signatures of crypto-currency market decoupling from the forex. Future Internet 11(7):154. https:\/\/doi.org\/10.3390\/fi11070154","journal-title":"Future Internet"},{"key":"239_CR10","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.econlet.2016.09.019","volume":"148","author":"A Urquhart","year":"2016","unstructured":"Urquhart A (2016) The inefficiency of Bitcoin. Econ Lett 148:80\u201382","journal-title":"Econ Lett"},{"key":"239_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.econlet.2017.06.023","volume":"158","author":"P Katsiampa","year":"2017","unstructured":"Katsiampa P (2017) Volatility estimation for Bitcoin: a comparison of GARCH models. Econ Lett 158:3\u20136","journal-title":"Econ Lett"},{"key":"239_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.chaos.2017.12.018","volume":"107","author":"S Lahmiri","year":"2018","unstructured":"Lahmiri S, Bekiros S, Salvi A (2018) Long-range memory, distributional variation and randomness of Bitcoin volatility. Chaos Solitons Fractals 107:43\u201348","journal-title":"Chaos Solitons Fractals"},{"issue":"2","key":"239_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm11020023","volume":"11","author":"C Conrad","year":"2018","unstructured":"Conrad C, Custovic A, Ghysels E (2018) Long-and short-term cryptocurrency volatility components: a GARCH-MIDAS analysis. J Financ Risk Manag 11(2):23","journal-title":"J Financ Risk Manag"},{"key":"239_CR14","doi-asserted-by":"crossref","unstructured":"Walther T, Klein T, Bouri E (2019) Exogenous drivers of Bitcoin and cryptocurrency volatility\u2014a mixed data sampling approach to forecasting. University of St. Gallen. Research Paper (2018\/19)","DOI":"10.2139\/ssrn.3192474"},{"key":"239_CR15","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.frl.2018.08.015","volume":"29","author":"E Bouri","year":"2019","unstructured":"Bouri E, Lau CKM, Lucey B, Roubaud D (2019) Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Res Lett 29:340\u2013346","journal-title":"Finance Res Lett"},{"issue":"8","key":"239_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0161197","volume":"11","author":"YB Kim","year":"2016","unstructured":"Kim YB, Kim JG, Kim W, Im JH, Kim TH, Kang SJ, Kim CH (2016) Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS ONE 11(8):1\u201317. https:\/\/doi.org\/10.1371\/journal.pone.0161197","journal-title":"PLoS ONE"},{"key":"239_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2019.00098","volume":"7","author":"TR Li","year":"2019","unstructured":"Li TR, Chamrajnagar AS, Fong XR, Rizik NR, Fu F (2019) Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model. Front Phys 7:98. https:\/\/doi.org\/10.3389\/fphy.2019.00098","journal-title":"Front Phys"},{"key":"239_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s42521-019-00008-9","volume":"1","author":"T Aste","year":"2019","unstructured":"Aste T (2019) Cryptocurrency market structure: connecting emotions and economics. Digital Finance 1:5\u201321","journal-title":"Digital Finance"},{"key":"239_CR19","doi-asserted-by":"crossref","unstructured":"Keskin Z, Aste T (2019) Information-theoretic measures for non-linear causality detection: application to social media sentiment and cryptocurrency prices. arXiv:1906.05740","DOI":"10.1098\/rsos.200863"},{"issue":"2","key":"239_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm12020053","volume":"12","author":"CY-H Chen","year":"2019","unstructured":"Chen CY-H, Hafner CM (2019) Sentiment-induced bubbles in the cryptocurrency market. J Financ Risk Manag 12(2):53","journal-title":"J Financ Risk Manag"},{"issue":"13","key":"239_CR21","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1080\/13504851.2014.995359","volume":"22","author":"A Yelowitz","year":"2015","unstructured":"Yelowitz A, Wilson M (2015) Characteristics of Bitcoin users: an analysis of Google search data. Appl Econ Lett 22(13):1030\u20131036","journal-title":"Appl Econ Lett"},{"key":"239_CR22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/3233347.3233370","volume-title":"Proceedings of the 4th international conference on frontiers of educational technologies","author":"RC Phillips","year":"2018","unstructured":"Phillips RC, Gorse D (2018) Mutual-excitation of cryptocurrency market returns and social media topics. In: Proceedings of the 4th international conference on frontiers of educational technologies. ACM, New York, pp\u00a080\u201386"},{"key":"239_CR23","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1145\/2804381.2804386","volume-title":"Proceedings of the 7th international workshop on social software engineering\u2014SSE 2015","author":"D Graziotin","year":"2015","unstructured":"Graziotin D, Wang X, Abrahamsson P (2015) Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering. In: Proceedings of the 7th international workshop on social software engineering\u2014SSE 2015. ACM Press, New York, pp\u00a025\u201332. https:\/\/doi.org\/10.1145\/2804381.2804386. http:\/\/dl.acm.org\/citation.cfm?doid=2804381.2804386"},{"key":"239_CR24","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.73","volume":"2","author":"G Destefanis","year":"2016","unstructured":"Destefanis G, Ortu M, Counsell S, Swift S, Marchesi M, Tonelli R (2016) Software development: do good manners matter? PeerJ 2:73","journal-title":"PeerJ"},{"issue":"1","key":"239_CR25","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1007\/s10664-017-9526-0","volume":"23","author":"A Murgia","year":"2018","unstructured":"Murgia A, Ortu M, Tourani P, Adams B, Demeyer S (2018) An exploratory qualitative and quantitative analysis of emotions in issue report comments of open source systems. Empir Softw Eng 23(1):521\u2013564. https:\/\/doi.org\/10.1007\/s10664-017-9526-0","journal-title":"Empir Softw Eng"},{"key":"239_CR26","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.289","volume":"2","author":"D Graziotin","year":"2014","unstructured":"Graziotin D, Wang X, Abrahamsson P (2014) Happy software developers solve problems better: psychological measurements in empirical software engineering. PeerJ 2:289","journal-title":"PeerJ"},{"issue":"4","key":"239_CR27","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10111-010-0164-1","volume":"13","author":"IA Khan","year":"2011","unstructured":"Khan IA, Brinkman W-P, Hierons RM (2011) Do moods affect programmers\u2019 debug performance? Cogn Technol Work 13(4):245\u2013258","journal-title":"Cogn Technol Work"},{"key":"239_CR28","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/B978-0-12-802117-0.00005-9","volume-title":"Handbook of digital currency","author":"B Ong","year":"2015","unstructured":"Ong B, Lee TM, Li G, Chuen DLK (2015) Evaluating the potential of alternative cryptocurrencies. In: Handbook of digital currency. Elsevier, Amsterdam, pp\u00a081\u2013135. https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780128021170000059"},{"key":"239_CR29","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/SERA.2016.7516145","volume-title":"2016 IEEE 14th international conference on Software Engineering Research, Management and Applications (SERA)","author":"ZMFMR Islam","year":"2016","unstructured":"Islam ZMFMR (2016) Towards understanding and exploiting developers\u2019 emotional variations in software engineering. In: 2016 IEEE 14th international conference on Software Engineering Research, Management and Applications (SERA), pp\u00a0185\u2013192. https:\/\/doi.org\/10.1109\/SERA.2016.7516145"},{"key":"239_CR30","first-page":"3562","volume-title":"LREC","author":"JC de Albornoz","year":"2012","unstructured":"de Albornoz JC, Plaza L, Gerv\u00e1s P (2012) Sentisense: an easily scalable concept-based affective lexicon for sentiment analysis. In: LREC, pp\u00a03562\u20133567"},{"key":"239_CR31","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1145\/2901739.2901752","volume-title":"Proceedings of the 13th international conference on mining software repositories","author":"M Mantyla","year":"2016","unstructured":"Mantyla M, Adams B, Destefanis G, Graziotin D, Ortu M (2016) Mining valence, arousal, and dominance: possibilities for detecting burnout and productivity? In: Proceedings of the 13th international conference on mining software repositories, pp\u00a0247\u2013258"},{"issue":"7","key":"239_CR32","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1080\/02699930902809375","volume":"23","author":"JA Russell","year":"2009","unstructured":"Russell JA (2009) Emotion, core affect, and psychological construction. Cogn Emot 23(7):1259\u20131283. https:\/\/doi.org\/10.1080\/02699930902809375","journal-title":"Cogn Emot"},{"key":"239_CR33","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.18","volume":"1","author":"D Graziotin","year":"2015","unstructured":"Graziotin D, Wang X, Abrahamsson P (2015) How do you feel, developer? An explanatory theory of the impact of affects on programming performance. PeerJ 1:18","journal-title":"PeerJ"},{"key":"239_CR34","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1145\/3273934.3273943","volume-title":"Proceedings of the 14th international conference on predictive models and data analytics in software engineering","author":"M Ortu","year":"2018","unstructured":"Ortu M, Hall T, Marchesi M, Tonelli R, Bowes D, Destefanis G (2018) Mining communication patterns in software development: a Github analysis. In: Proceedings of the 14th international conference on predictive models and data analytics in software engineering, pp\u00a070\u201379"},{"key":"239_CR35","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1145\/2597073.2597086","volume-title":"Proceedings of the 11th working conference on mining software repositories","author":"A Murgia","year":"2014","unstructured":"Murgia A, Tourani P, Adams B, Ortu M (2014) Do developers feel emotions? An exploratory analysis of emotions in software artifacts. In: Proceedings of the 11th working conference on mining software repositories, pp\u00a0262\u2013271"},{"issue":"4","key":"239_CR36","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.3758\/s13428-012-0314-x","volume":"45","author":"AB Warriner","year":"2013","unstructured":"Warriner AB, Kuperman V, Brysbaert M (2013) Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav Res Methods 45(4):1191\u20131207. https:\/\/doi.org\/10.3758\/s13428-012-0314-x","journal-title":"Behav Res Methods"},{"key":"239_CR37","volume-title":"Proceedings of ACL","author":"C Danescu-Niculescu-Mizil","year":"2013","unstructured":"Danescu-Niculescu-Mizil C, Sudhof M, Jurafsky D, Potts C (2013) A computational approach to politeness with application to social factors. In: Proceedings of ACL"},{"issue":"3","key":"239_CR38","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1007\/s10664-017-9546-9","volume":"23","author":"F Calefato","year":"2018","unstructured":"Calefato F, Lanubile F, Maiorano F, Novielli N (2018) Sentiment polarity detection for software development. Empir Softw Eng 23(3):1352\u20131382","journal-title":"Empir Softw Eng"},{"key":"239_CR39","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","volume":"37","author":"CW Granger","year":"1969","unstructured":"Granger CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424\u2013438","journal-title":"Econometrica"},{"issue":"2","key":"239_CR40","doi-asserted-by":"publisher","first-page":"164","DOI":"10.2307\/1992331","volume":"17","author":"DL Thornton","year":"1985","unstructured":"Thornton DL, Batten DS (1985) Lag-length selection and tests of Granger causality between money and income. J Money Credit Bank 17(2):164\u2013178","journal-title":"J Money Credit Bank"},{"issue":"33","key":"239_CR41","first-page":"1","volume":"3","author":"VK-S Liew","year":"2004","unstructured":"Liew VK-S (2004) Which lag length selection criteria should we employ? Econ Bull 3(33):1\u20139","journal-title":"Econ Bull"},{"key":"239_CR42","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.econmod.2017.03.019","volume":"64","author":"M Balcilar","year":"2017","unstructured":"Balcilar M, Bouri E, Gupta R, Roubaud D (2017) Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Econ Model 64:74\u201381","journal-title":"Econ Model"},{"issue":"2","key":"239_CR43","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461\u2013464","journal-title":"Ann Stat"},{"issue":"4","key":"239_CR44","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1111\/1467-9892.00270","volume":"23","author":"J Gonzalo","year":"2002","unstructured":"Gonzalo J, Pitarakis J-Y (2002) Lag length estimation in large dimensional systems. J Time Ser Anal 23(4):401\u2013423","journal-title":"J Time Ser Anal"},{"issue":"6","key":"239_CR45","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1080\/758520275","volume":"21","author":"JD Jones","year":"1989","unstructured":"Jones JD (1989) A comparison of lag\u2013length selection techniques in tests of Granger causality between money growth and inflation: evidence for the US, 1959\u201386. Appl Econ 21(6):809\u2013822","journal-title":"Appl Econ"},{"key":"239_CR46","volume-title":"9th Python in science conference","author":"S Seabold","year":"2010","unstructured":"Seabold S, Perktold J (2010) Statsmodels: econometric and statistical modeling with Python. In: 9th Python in science conference"},{"key":"239_CR47","volume-title":"Regression","author":"L Fahrmeir","year":"2007","unstructured":"Fahrmeir L, Kneib T, Lang S, Marx B (2007) Regression. Springer, Berlin"},{"key":"239_CR48","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.aml.2017.05.005","volume":"74","author":"HT Banks","year":"2017","unstructured":"Banks HT, Joyner ML (2017) AIC under the framework of least squares estimation. Appl Math Lett 74:33\u201345","journal-title":"Appl Math Lett"},{"key":"239_CR49","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1109\/HICSS.1996.495431","volume-title":"Proceedings of HICSS-29: 29th Hawaii international conference on system sciences","author":"J Roman","year":"1996","unstructured":"Roman J, Jameel A (1996) Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns. In: Proceedings of HICSS-29: 29th Hawaii international conference on system sciences, vol\u00a02. IEEE, Los Alamitos, pp\u00a0454\u2013460"},{"issue":"2","key":"239_CR50","doi-asserted-by":"publisher","first-page":"14","DOI":"10.9735\/0975-2927.2.2.14-17","volume":"2","author":"RK Dase","year":"2010","unstructured":"Dase RK, Pawar DD (2010) Application of artificial neural network for stock market predictions: a review of literature. Int J Mach Intell 2(2):14\u201317","journal-title":"Int J Mach Intell"},{"key":"239_CR51","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/PDP2018.2018.00060","volume-title":"2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP)","author":"S McNally","year":"2018","unstructured":"McNally S, Roche J, Caton S (2018) Predicting the price of Bitcoin using machine learning. In: 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP). IEEE, Los Alamitos, pp\u00a0339\u2013343"},{"key":"239_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2019.112395","volume":"365","author":"Z Chen","year":"2020","unstructured":"Chen Z, Li C, Sun W (2020) Bitcoin price prediction using machine learning: an approach to sample dimension engineering. J Comput Appl Math 365:112395","journal-title":"J Comput Appl Math"},{"key":"239_CR53","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. Preprint. arXiv:1412.6980"},{"key":"239_CR54","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.econlet.2018.10.011","volume":"173","author":"P Chaim","year":"2018","unstructured":"Chaim P, Laurini MP (2018) Volatility and return jumps in Bitcoin. Econ Lett 173:158\u2013163","journal-title":"Econ Lett"},{"key":"239_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/09-SS051","volume":"4","author":"MP Fay","year":"2010","unstructured":"Fay MP, Proschan MA (2010) Wilcoxon\u2013Mann\u2013Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv 4:1","journal-title":"Stat Surv"},{"issue":"1\u20132","key":"239_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/1500000011","volume":"2","author":"B Pang","year":"2008","unstructured":"Pang B, Lee L (2008) Opinion Mining and Sentiment Analysis. Found Trends Inf Retr 2(1\u20132):1\u2013135","journal-title":"Found Trends Inf Retr"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-020-00239-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-020-00239-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-020-00239-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T23:07:01Z","timestamp":1626995221000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-020-00239-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["239"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-020-00239-6","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]},"assertion":[{"value":"7 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}