{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T08:29:22Z","timestamp":1778833762440,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPJ Data Sci."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Context<\/jats:title>\n                <jats:p>The lockdown orders established in multiple countries in response to the Covid-19 pandemic are arguably one of the most widespread and deepest shock experienced by societies in recent years. Studying their impact trough the lens of social media offers an unprecedented opportunity to understand the susceptibility and the resilience of human activity patterns to large-scale exogenous shocks. Firstly, we investigate the changes that this upheaval has caused in online activity in terms of time spent online, themes and emotion shared on the platforms, and rhythms of content consumption. Secondly, we examine the resilience of certain platform characteristics, such as the daily rhythms of emotion expression.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Data<\/jats:title>\n                <jats:p>Two independent datasets about the French cyberspace: a fine-grained temporal record of almost 100 thousand YouTube videos and a collection of 8 million Tweets between February 17 and April 14, 2020.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Findings<\/jats:title>\n                <jats:p>In both datasets we observe a reshaping of the circadian rhythms with an increase of night activity during the lockdown. The analysis of the videos and tweets published during lockdown shows a general decrease in emotional contents and a shift from themes like work and money to themes like death and safety. However, the daily patterns of emotions remain mostly unchanged, thereby suggesting that emotional cycles are resilient to exogenous shocks.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1140\/epjds\/s13688-021-00262-1","type":"journal-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T12:08:18Z","timestamp":1612181298000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["The rhythms of the night: increase in online night activity and emotional resilience during the spring 2020 Covid-19 lockdown"],"prefix":"10.1140","volume":"10","author":[{"given":"Maria","family":"Castaldo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommaso","family":"Venturini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Frasca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9813-1815","authenticated-orcid":false,"given":"Floriana","family":"Gargiulo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"issue":"6051","key":"262_CR1","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1126\/science.1202775","volume":"333","author":"SA Golder","year":"2011","unstructured":"Golder SA, Macy MW (2011) Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051):1878\u20131881","journal-title":"Science"},{"key":"262_CR2","doi-asserted-by":"publisher","DOI":"10.1177\/2398212817744501","volume":"1","author":"F Dzogang","year":"2017","unstructured":"Dzogang F, Lightman S, Cristianini N (2017) Circadian mood variations in Twitter content. Brain Neurosci Adv 1:2398212817744501","journal-title":"Brain Neurosci Adv"},{"key":"262_CR3","unstructured":"Lampos V, Lansdall-Welfare T, Araya R, Cristianini N (2013) Analysing mood patterns in the United Kingdom through Twitter content. arXiv preprint. arXiv:1304.5507"},{"issue":"8","key":"262_CR4","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/1787234.1787254","volume":"53","author":"G Szabo","year":"2010","unstructured":"Szabo G, Huberman BA (2010) Predicting the popularity of online content. Commun ACM 53(8):80\u201388","journal-title":"Commun ACM"},{"key":"262_CR5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1145\/1298306.1298310","volume-title":"Proceedings of the 7th ACM SIGCOMM conference on Internet measurement","author":"P Gill","year":"2007","unstructured":"Gill P, Arlitt M, Li Z, Mahanti A (2007) Youtube traffic characterization: a view from the edge. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pp\u00a015\u201328"},{"key":"262_CR6","first-page":"12","volume-title":"Data analytics","author":"M Ten Thij","year":"2014","unstructured":"Ten Thij M, Bhulai S, Kampstra P (2014) Circadian patterns in Twitter. In: Data analytics, pp\u00a012\u201317"},{"key":"262_CR7","volume-title":"Fifth international AAAI conference on weblogs and social media","author":"A Noulas","year":"2011","unstructured":"Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in Foursquare. In: Fifth international AAAI conference on weblogs and social media"},{"key":"262_CR8","volume-title":"Seventh international AAAI conference on weblogs and social media","author":"N Grinberg","year":"2013","unstructured":"Grinberg N, Naaman M, Shaw B, Lotan G (2013) Extracting diurnal patterns of real world activity from social media. In: Seventh international AAAI conference on weblogs and social media"},{"issue":"1","key":"262_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0030091","volume":"7","author":"T Yasseri","year":"2012","unstructured":"Yasseri T, Sumi R, Kert\u00e9sz J (2012) Circadian patterns of Wikipedia editorial activity: a demographic analysis. PLoS ONE 7(1):e30091","journal-title":"PLoS ONE"},{"key":"262_CR10","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1145\/1878500.1878512","volume-title":"Proceedings of the ACM SIGSPATIAL international workshop on GeoStreaming","author":"A Pozdnoukhov","year":"2010","unstructured":"Pozdnoukhov A, Walsh F (2010) Exploratory novelty identification in human activity data streams. In: Proceedings of the ACM SIGSPATIAL international workshop on GeoStreaming, pp\u00a059\u201362"},{"key":"262_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/14\/1\/013055","volume":"14","author":"H-H Jo","year":"2012","unstructured":"Jo H-H, Karsai M, Kert\u00e9sz J, Kaski K (2012) Circadian pattern and burstiness in human communication activity. New J Phys 14:013055","journal-title":"New J Phys"},{"key":"262_CR12","unstructured":"Beck F, L\u00e9ger D, Fressard L, Peretti-Watel P, Verger P (2020) The Coconel Group: Covid-19 health crisis and lockdown associated with high level of sleep complaints and hypnotic uptake at the population level. J Sleep Res 13119"},{"key":"262_CR13","doi-asserted-by":"publisher","DOI":"10.1145\/2433396.2433443","volume-title":"WSDM 2013 - proceedings of the 6th ACM international conference on web search and data mining","author":"H Pinto","year":"2013","unstructured":"Pinto H, Almeida J, Gon\u00e7alves M (2013) Using early view patterns to predict the popularity of YouTube videos. In: WSDM 2013 - proceedings of the 6th ACM international conference on web search and data mining. https:\/\/doi.org\/10.1145\/2433396.2433443"},{"key":"262_CR14","doi-asserted-by":"publisher","unstructured":"Nguyen M-T, Nakajima T, Yoshimi M, Thoai N (2019) Analyzing and predicting the popularity of online contents. https:\/\/doi.org\/10.1145\/3366030.3366047","DOI":"10.1145\/3366030.3366047"},{"key":"262_CR15","unstructured":"Google: how engagement metrics are counted - YouTube Help. Accessed: 2020-11-24. https:\/\/support.google.com\/youtube\/answer\/2991785?hl=en%5C%E2%5C%80%5C%8B"},{"key":"262_CR16","unstructured":"MediaLab: Gazouilloire. https:\/\/github.com\/medialab\/gazouilloire. Accessed: 2020-11-24"},{"issue":"1","key":"262_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/0261927X09351676","volume":"29","author":"YR Tausczik","year":"2010","unstructured":"Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol 29(1):24\u201354","journal-title":"J Lang Soc Psychol"},{"issue":"3","key":"262_CR18","first-page":"145","volume":"56","author":"A Piolat","year":"2011","unstructured":"Piolat A, Booth RJ, Chung CK, Davids M, Pennebaker JW (2011) La version fran\u00e7aise du dictionnaire pour le LIWC: modalit\u00e9s de construction et exemples d\u2019utilisation. Psychol Fr 56(3):145\u2013159","journal-title":"Psychol Fr"},{"key":"262_CR19","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1177\/0002764211429368","volume":"56","author":"G Ritzer","year":"2012","unstructured":"Ritzer G, Dean P, Jurgenson N (2012) The coming of age of the prosumer. Am Behav Sci 56:379\u2013398","journal-title":"Am Behav Sci"},{"key":"262_CR20","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.chb.2016.09.024","volume":"66","author":"ML Khan","year":"2017","unstructured":"Khan ML (2017) Social media engagement: what motivates user participation and consumption on youtube? Comput Hum Behav 66:236\u2013247","journal-title":"Comput Hum Behav"},{"issue":"1","key":"262_CR21","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-018-0174-4","volume":"7","author":"T Aledavood","year":"2018","unstructured":"Aledavood T, Lehmann S, Saram\u00e4ki J (2018) Social network differences of chronotypes identified from mobile phone data. EPJ Data Sci 7(1):46","journal-title":"EPJ Data Sci"},{"issue":"9","key":"262_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0138098","volume":"10","author":"T Aledavood","year":"2015","unstructured":"Aledavood T, L\u00f3pez E, Roberts SG, Reed-Tsochas F, Moro E, Dunbar RI, Saram\u00e4ki J (2015) Daily rhythms in mobile telephone communication. PLoS ONE 10(9):0138098","journal-title":"PLoS ONE"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-021-00262-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-021-00262-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-021-00262-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T12:08:54Z","timestamp":1612181334000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-021-00262-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,1]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["262"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-021-00262-1","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,1]]},"assertion":[{"value":"17 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7"}}