{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T08:12:09Z","timestamp":1774685529718,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010666","name":"H2020 Research Infrastructures","doi-asserted-by":"publisher","award":["871042"],"award-info":[{"award-number":["871042"]}],"id":[{"id":"10.13039\/100010666","id-type":"DOI","asserted-by":"publisher"}]}],"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:p>Understanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30\u201370% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-021-00263-0","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T11:40:08Z","timestamp":1612352408000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic"],"prefix":"10.1140","volume":"10","author":[{"given":"Hao","family":"Cui","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4957-5406","authenticated-orcid":false,"given":"J\u00e1nos","family":"Kert\u00e9sz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"263_CR1","doi-asserted-by":"publisher","first-page":"17599","DOI":"10.1073\/pnas.0704916104","volume":"104","author":"F Wu","year":"2007","unstructured":"Wu F, Huberman BA (2007) Novelty and collective attention. Proc Natl Acad Sci USA 104:17599\u201317601","journal-title":"Proc Natl Acad Sci USA"},{"key":"263_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1111\/jcom.12088","volume":"64","author":"W Russell Neuman","year":"2014","unstructured":"Russell Neuman W, Guggenheim L, Mo Jang S, Bae SY (2014) The dynamics of public attention: agenda-setting theory meets big data. J Commun 64:193\u2013214","journal-title":"J Commun"},{"key":"263_CR3","unstructured":"Twitter micoroblog and social network service. https:\/\/about.twitter.com\/. Accessed 2 Dec 2020"},{"key":"263_CR4","unstructured":"Twitter: research and experiments. https:\/\/help.twitter.com\/en\/rules-and-policies#research-and-experiments. Accessed 2 Dec 2020"},{"key":"263_CR5","first-page":"251","volume-title":"Proceedings of the 21st international conference on world wide web (WWW)","author":"J Lehmann","year":"2007","unstructured":"Lehmann J, Gon\u00e7alves B, Ramasco JJ, Cattuto C (2007) Dynamical classes of collective attention in Twitter. In: Proceedings of the 21st international conference on world wide web (WWW), pp\u00a0251\u2013260"},{"key":"263_CR6","volume":"10","author":"Y-H Eom","year":"2015","unstructured":"Eom Y-H, Puliga M, Smailovi\u010d J, Mozeti\u010d I, Caldarelli G (2015) Twitter-based analysis of the dynamics of collective attention to political parties. PLoS ONE 10:0131184","journal-title":"PLoS ONE"},{"key":"263_CR7","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.physa.2014.02.034","volume":"404","author":"J Ko","year":"2014","unstructured":"Ko J, Kwon HW, Kim HS, Lee K, Choi MY (2014) Model for Twitter dynamics: public attention and time series of tweeting. Physica A 404:141\u2013149","journal-title":"Physica A"},{"key":"263_CR8","volume":"12","author":"T-Q Pen","year":"2017","unstructured":"Pen T-Q, Sun G, Wu Y (2017) Interplay between public attention and public emotion toward multiple social issues on Twitter. PLoS ONE 12:0167896","journal-title":"PLoS ONE"},{"key":"263_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0014118","volume":"5","author":"C Chew","year":"2010","unstructured":"Chew C, Eysenbach G (2010) Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE 5:14118","journal-title":"PLoS ONE"},{"key":"263_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0019467","volume":"6","author":"A Signorini","year":"2011","unstructured":"Signorini A, Segre AM, Polgreen PM (2011) The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS ONE 6:19467","journal-title":"PLoS ONE"},{"key":"263_CR11","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7219","volume":"19","author":"LGG van Lent","year":"2017","unstructured":"van Lent LGG, Sungur H, Kunneman FA, van de Velde B, Das E (2017) Too far to care? Measuring public attention and fear for ebola using Twitter. J Med Internet Res 19:193","journal-title":"J Med Internet Res"},{"key":"263_CR12","unstructured":"Zavarrone E, Grassia MG, Marino M, Cataldo R, Mazza R, Canestrari N CO.ME.T.A.\u2014COVID-19 media textual analysis. A\u00a0dashboard for media monitoring. https:\/\/arxiv.org\/pdf\/2004.07742.pdf. Accessed 2 Dec 2020"},{"key":"263_CR13","unstructured":"Lopez CE, Vasu1 M, Gallemore C Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset. https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2003\/2003.10359.pdf. Accessed 2 Dec 2020"},{"issue":"7","key":"263_CR14","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1177\/0956797620939054","volume":"31","author":"G Pennycook","year":"2020","unstructured":"Pennycook G, McPhetres J, Zhang Y, Lu JG, Rand DG (2020) Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci 31(7):770\u2013780","journal-title":"Psychol Sci"},{"issue":"4","key":"263_CR15","volume":"15","author":"J Gao","year":"2020","unstructured":"Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, Wang Y, Fu H, Dai J (2020) Mental health problems and social media exposure during COVID-19 outbreak. PLoS ONE 15(4):0231924","journal-title":"PLoS ONE"},{"issue":"5","key":"263_CR16","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.dsx.2020.05.035","volume":"14","author":"S Dubey","year":"2020","unstructured":"Dubey S, Biswas P, Ghosh R, Chatterjee S, Dubey MJ, Chatterjee S, Lahiri D, Lavie CJ (2020) Psychosocial impact of COVID-19. Diabetes Metab Syndr Clin Res Rev 14(5):779\u2013788","journal-title":"Diabetes Metab Syndr Clin Res Rev"},{"key":"263_CR17","unstructured":"An introduction to Sina Weibo: background and status quo. https:\/\/www.whatsonweibo.com\/sinaweibo\/. Accessed 2 Dec 2020"},{"key":"263_CR18","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1080\/1369118X.2013.839730","volume":"17","author":"J Tong","year":"2014","unstructured":"Tong J, Zuo L (2014) Weibo communication and government legitimacy in China: a computer-assisted analysis of Weibo messages on two \u2018mass incidents\u2019. Inf Commun Soc 17:66\u201385","journal-title":"Inf Commun Soc"},{"key":"263_CR19","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1080\/1369118X.2015.1104372","volume":"19","author":"JYM Nip","year":"2016","unstructured":"Nip JYM, Fu K-W (2016) Networked framing between source posts and their reposts: an analysis of public opinion on China\u2019s microblogs. Inf Commun Soc 19:1127\u20131149","journal-title":"Inf Commun Soc"},{"issue":"5","key":"263_CR20","doi-asserted-by":"publisher","first-page":"E24","DOI":"10.1017\/dmp.2020.68","volume":"14","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Fu K, Gr\u00e9pin K, Liang H, Fung I (2020) Limited early warnings and public attention to coronavirus disease 2019 in China, January\u2013February, 2020: a longitudinal cohort of randomly sampled weibo users. Disaster Med Public Health Prep 14(5):E24\u2013E27","journal-title":"Disaster Med Public Health Prep"},{"key":"263_CR21","doi-asserted-by":"publisher","DOI":"10.2196\/18825","volume":"22","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Cheng S, Yu X, Xu H (2020) Chinese public\u2019s attention to the COVID-19 epidemic on social media: observational descriptive study. J Med Internet Res 22:18825","journal-title":"J Med Internet Res"},{"key":"263_CR22","unstructured":"Li X, Zhou M, Wu J, Yuan A, Wu F, Li J Analyzing COVID-19 on online social media: trends, sentiments and emotions. https:\/\/arxiv.org\/pdf\/2005.14464.pdf. Accessed 2 Dec 2020"},{"issue":"4","key":"263_CR23","doi-asserted-by":"publisher","first-page":"343","DOI":"10.4258\/hir.2017.23.4.343","volume":"23","author":"D-W Seo","year":"2017","unstructured":"Seo D-W, Shin S-Y (2017) Methods using social media and search queries to predict infectious disease outbreaks. Healthc Inf Res 23(4):343\u2013348","journal-title":"Healthc Inf Res"},{"issue":"1","key":"263_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12976-017-0074-5","volume":"15","author":"A Alessa","year":"2018","unstructured":"Alessa A, Faezipour M (2018) A review of influenza detection and prediction through social networking sites. Theor Biol Med Model 15(1):2","journal-title":"Theor Biol Med Model"},{"issue":"2","key":"263_CR25","doi-asserted-by":"publisher","DOI":"10.2196\/19702","volume":"6","author":"TS Higgins","year":"2020","unstructured":"Higgins TS, Wu AW, Sharma D, Illing EA, Rubel K, Ting JY (2020) Correlations of online search engine trends with coronavirus disease (COVID-19) incidence: infodemiology study. JMIR Public Health Surveill 6(2):19702","journal-title":"JMIR Public Health Surveill"},{"issue":"7","key":"263_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17072365","volume":"17","author":"L Qin","year":"2020","unstructured":"Qin L, Sun Q, Wang Y, Wu K-F, Chen M, Shia B-C, Wu S-Y (2020) Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index. Int J Environ Res Public Health 17(7):2365","journal-title":"Int J Environ Res Public Health"},{"issue":"10","key":"263_CR27","doi-asserted-by":"publisher","DOI":"10.2807\/1560-7917.ES.2020.25.10.2000199","volume":"25","author":"C Li","year":"2020","unstructured":"Li C, Chen LJ, Chen X, Zhang M, Pang CP, Chen H (2020) Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Euro Surveill 25(10):2000199","journal-title":"Euro Surveill"},{"issue":"1","key":"263_CR28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.4269\/ajtmh.2012.11-0597","volume":"86","author":"R Chunara","year":"2012","unstructured":"Chunara R, Andrews JR, Brownstein JS (2012) Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am Soc Trop Med Hyg 86(1):39\u201345","journal-title":"Am Soc Trop Med Hyg"},{"issue":"8","key":"263_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph13080780","volume":"13","author":"K Liu","year":"2016","unstructured":"Liu K, Li L, Jiang T, Chen B, Jiang Z, Wang Z, Chen Y, Jiang J, Gu H (2016) Chinese public attention to the outbreak of ebola in West Africa: evidence from the online big data platform. Int J Environ Res Public Health 13(8):780","journal-title":"Int J Environ Res Public Health"},{"key":"263_CR30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.109.128701","volume":"109","author":"N Blumm","year":"2012","unstructured":"Blumm N, Ghoshal G, Forr\u00f3 Z, Schich M, Bianconi G, Bouchaud J-P, Barab\u00e1si A-L (2012) Dynamics of ranking processes in complex systems. Phys Rev Lett 109:128701","journal-title":"Phys Rev Lett"},{"key":"263_CR31","doi-asserted-by":"publisher","DOI":"10.1063\/1.4826446","volume":"23","author":"R Criado","year":"2013","unstructured":"Criado R, Garcia E, Pedroche F, Romance M (2013) A new method for comparing rankings through complex networks: model and analysis of competitiveness of major European soccer leagues. Chaos 23:043114","journal-title":"Chaos"},{"key":"263_CR32","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-016-0096-y","volume":"5","author":"JA Morales","year":"2016","unstructured":"Morales JA, S\u00e1nchez S, Flores J, Pineda C, Gershenson C, Cocho G, Zizumbo J, Rodr\u00edguez RF, I\u00f1iguez G (2016) Generic temporal features of performance rankings in sports and games. EPJ Data Sci 5:33","journal-title":"EPJ Data Sci"},{"key":"263_CR33","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2018.00045","volume":"6","author":"JA Morales","year":"2018","unstructured":"Morales JA, Colman E, S\u00e1nchez S, S\u00e1nchez-Puig F, Pineda C, I\u00f1iguez G, Cocho G, Flores J, Gershenson C (2018) Rank dynamics of word usage at multiple scales. Front Phys 6:45","journal-title":"Front Phys"},{"key":"263_CR34","doi-asserted-by":"crossref","unstructured":"Alshaabi T, Minot JR, Arnold MV, Adams JL, Dewhurst DR, Reagan AJ, Muhamad R, Danforth CM, Dodds PS How the world\u2019s collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter. https:\/\/arxiv.org\/pdf\/2003.12614.pdf. Accessed 2 Dec 2020","DOI":"10.1371\/journal.pone.0244476"},{"key":"263_CR35","unstructured":"Dewhurst DR, Alshaabi T, Arnold MV, Minot JR, Danforth CM, Dodds PS Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance. https:\/\/arxiv.org\/pdf\/2004.03516.pdf. Accessed 2 Dec 2020"},{"key":"263_CR36","unstructured":"Weibo reports first quarter 2020 unaudited financial results. http:\/\/ir.weibo.com\/news-releases\/news-release-details\/weibo-reports-first-quarter-2020-unaudited-financial-results\/. Accessed 2 Dec 2020"},{"key":"263_CR37","unstructured":"Wang Y An introduction to Sina Weibo for journalists. https:\/\/www.interhacktives.com\/2018\/02\/22\/how-to-use-sina-weibo-as-a-journalist\/. Accessed 2 Dec 2020"},{"key":"263_CR38","unstructured":"Service WC Common questions on the rules of real-time hot-search-list, hot-message-list and hot-topic-list. https:\/\/www.weibo.com\/ttarticle\/p\/show?id=2309404007731978739654. Accessed 2 Dec 2020"},{"key":"263_CR39","unstructured":"Weibo Advertising. https:\/\/www.marketingtochina.com\/weibo-advertising\/. Accessed 2 Dec 2020"},{"key":"263_CR40","unstructured":"National Health Commission of People\u2019s Republic of China. http:\/\/www.nhc.gov.cn\/xcs\/xxgzbd\/gzbd_index.shtml. Accessed 2 Dec 2020"},{"key":"263_CR41","unstructured":"China confirms 15,152 new coronavirus cases, 254 additional deaths. https:\/\/www.cnbc.com\/2020\/02\/13\/coronavirus-latest-updates-china-hubei.html. Accessed 2 Dec 2020"},{"key":"263_CR42","unstructured":"Sajid I China reports 99 new virus cases majority imported. https:\/\/www.aa.com.tr\/en\/asia-pacific\/china-reports-99-new-virus-cases-majority-imported\/1801667. Accessed 2 Dec 2020"},{"key":"263_CR43","unstructured":"Savitzky\u2013Golay filter. https:\/\/en.wikipedia.org\/wiki\/Savitzky-Golay_filter. Accessed 2 Dec 2020"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-021-00263-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-021-00263-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-021-00263-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T11:41:53Z","timestamp":1612352513000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-021-00263-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,3]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["263"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-021-00263-0","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,3]]},"assertion":[{"value":"12 August 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":"3 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":"8"}}