What social media told us in the time of COVID-19: a scoping review

Pérez-Escoda, 2020, Social networks' engagement during the COVID-19 pandemic in Spain: health media vs. healthcare professionals, Int J Environ Res Public Health, 17, 10.3390/ijerph17145261 Jordan, 2019, Using Twitter for public health surveillance from monitoring and prediction to public response, Data (Basel), 4, 6 Shah, 2019, Automatically appraising the credibility of vaccine-related web pages shared on social media: a Twitter surveillance study, J Med Internet Res, 21, 10.2196/14007 Sinnenberg, 2017, Twitter as a tool for health research: a systematic review, Am J Public Health, 107, e1, 10.2105/AJPH.2016.303512 Steffens, 2019, How organisations promoting vaccination respond to misinformation on social media: a qualitative investigation, BMC Public Health, 19, 10.1186/s12889-019-7659-3 Abd-Alrazaq, 2020, Top concerns of tweeters during the COVID-19 pandemic: infoveillance study, J Med Internet Res, 22, 10.2196/19016 Freberg, 2013, Managing and sharing H1N1 crisis information using social media bookmarking services, Public Relat Rev, 39, 178, 10.1016/j.pubrev.2013.02.007 Giustini, 2018, Effective uses of social media in public health and medicine: a systematic review of systematic reviews, Online J Public Health Inform, 10, e215, 10.5210/ojphi.v10i2.8270 Al-Dmour, 2020, Influence of social media platforms on public health protection against the COVID-19 pandemic via the mediating effects of public health awareness and behavioral changes: integrated model, J Med Internet Res, 22, 10.2196/19996 Bridgman Tang, 2018, Social media and outbreaks of emerging infectious diseases: a systematic review of literature, Am J Infect Control, 46, 962, 10.1016/j.ajic.2018.02.010 Arksey, 2005, Scoping studies: towards a methodological framework, Int J Soc Res Methodol, 8, 19, 10.1080/1364557032000119616 Levac, 2010, Scoping studies: advancing the methodology, Implement Sci, 5, 69, 10.1186/1748-5908-5-69 Ose, 2016, Using Excel and Word to structure qualitative data, J Appl Soc Sci (Boulder), 10, 147, 10.1177/1936724416664948 Braun, 2006, Using thematic analysis in psychology, Qual Res Psychol, 3, 77, 10.1191/1478088706qp063oa Li, 2020, Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020, Euro Surveill, 25, 10.2807/1560-7917.ES.2020.25.10.2000199 Liu, 2020, Characteristics and outcomes of a sample of patients with COVID-19 identified through social media in Wuhan, China: observational study, J Med Internet Res, 22, 10.2196/20108 O'Leary, 2020, A Google–Wikipedia–Twitter model as a leading indicator of the numbers of coronavirus deaths, Intell Syst Account Finance Manag, 27, 151, 10.1002/isaf.1482 Peng, 2020, Exploring urban spatial features of COVID-19 transmission in Wuhan based on social media data, ISPRS Int J Geoinf, 9, 402, 10.3390/ijgi9060402 Qin, 2020, Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index, Int J Environ Res Public Health, 17, 10.3390/ijerph17072365 Zhu, 2020, Limited early warnings and public attention to coronavirus disease 2019 in China, January–February, 2020: a longitudinal cohort of randomly sampled Weibo users, Disaster Med Public Health Prep, 10.1017/dmp.2020.68 Basch, 2020, Preventive behaviors conveyed on YouTube to mitigate transmission of COVID-19: cross-sectional study, JMIR Public Health Surveill, 6 Basch, 2020, The role of YouTube and the entertainment industry in saving lives by educating and mobilizing the public to adopt behaviors for community mitigation of COVID-19: successive sampling design study, JMIR Public Health Surveill, 6, 10.2196/19145 Khatri, 2020, YouTube as a source of information on 2019 novel coronavirus outbreak: a cross-sectional study of English and Mandarin content, Travel Med Infect Dis, 35, 10.1016/j.tmaid.2020.101636 Li, 2020, Characterizing the propagation of situational information in social media during covid-19 epidemic: a case study on Weibo, IEEE Trans Comput Soc Syst, 7, 556, 10.1109/TCSS.2020.2980007 Merkley, 2020, A rare moment of cross-partisan consensus: elite and public response to the COVID-19 pandemic in Canada, Can J Polit Sci, 10.1017/S0008423920000311 Rufai, 2020, World leaders' usage of Twitter in response to the COVID-19 pandemic: a content analysis, J Public Health (Oxf), 42, 510, 10.1093/pubmed/fdaa049 Sutton, 2020, COVID-19: Retransmission of official communications in an emerging pandemic, PLoS One, 15, 10.1371/journal.pone.0238491 Wang, 2021, Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter, Comput Human Behav, 114, 10.1016/j.chb.2020.106568 Ahmed, 2020, COVID-19 and the “Film Your Hospital” conspiracy theory: social network analysis of Twitter data, J Med Internet Res, 22 Ahmed, 2020, COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data, J Med Internet Res, 22 Brennen, 2020, Beyond (mis)representation: visuals in COVID-19 misinformation, Int J Press/Polit Bruns, 2020, ‘Corona? 5G? or both?’: the dynamics of COVID-19/5G conspiracy theories on Facebook, Media Int Aust, 177, 12, 10.1177/1329878X20946113 Galhardi, 2020, Fact or fake? An analysis of disinformation regarding the COVID-19 pandemic in Brazil, Cien Saude Colet, 25, 4201, 10.1590/1413-812320202510.2.28922020 Gallotti, 2020, Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics, Nat Hum Behav, 4, 1285, 10.1038/s41562-020-00994-6 Islam, 2020, COVID-19-related infodemic and its impact on public health: a global social media analysis, Am J Trop Med Hyg, 103, 1621, 10.4269/ajtmh.20-0812 Kouzy, 2020, Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter, Cureus, 12 Moscadelli, 2020, Fake news and COVID-19 in Italy: results of a quantitative observational study, Int J Environ Res Public Health, 17, 10.3390/ijerph17165850 Pulido, 2020, COVID-19 infodemic: more retweets for science-based information on coronavirus than for false information, Int Sociol, 35, 377, 10.1177/0268580920914755 Rovetta, 2020, Global infodemiology of COVID-19: analysis of Google web searches and Instagram hashtags, J Med Internet Res, 22, 10.2196/20673 Uyheng, 2020, Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines, J Comput Soc Sc, 10.1007/s42001-020-00087-4 Gao, 2020, Mental health problems and social media exposure during COVID-19 outbreak, PLoS One, 15 Li, 2020, The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users, Int J Environ Res Public Health, 17 Hakimi, 2020, Hand sanitizer in a pandemic: wrong formulations in the wrong hands, J Emerg Med, 59, 668, 10.1016/j.jemermed.2020.07.018 Hernández-García, 2020, Characteristics of YouTube videos in Spanish on how to prevent COVID-19, Int J Environ Res Public Health, 17, 10.3390/ijerph17134671 Moon, 2020, Evaluation of Korean-language COVID-19-related medical information on YouTube: cross-sectional infodemiology study, J Med Internet Res, 22, 10.2196/20775 Ozdede, 2020, Analysis of dentistry YouTube videos related to COVID-19, Braz Dent J, 31, 392, 10.1590/0103-6440202003767 Yüce, 2020, An analysis of YouTube videos as educational resources for dental practitioners to prevent the spread of COVID-19, Ir J Med Sci Al-Rawi, 2020, COVID-19 and the gendered use of emojis on Twitter: infodemiology study, J Med Internet Res, 22, 10.2196/21646 Arpaci, 2020, Analysis of twitter data using evolutionary clustering during the COVID-19 pandemic, Comput Mater Contin, 65, 193, 10.32604/cmc.2020.011489 Barrett, 2020, Calculated ageism: generational sacrifice as a response to the COVID-19 pandemic, J Gerontol Psychol Sci Soc Sci, 10.1093/geronb/gbaa132 Boon-Itt, 2020, Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modelling study, JMIR Public Health Surveill, 6, 10.2196/21978 Budhwani, 2020, Creating COVID-19 stigma by referencing the novel coronavirus as the “Chinese virus” on Twitter: quantitative analysis of social media data, J Med Internet Res, 22, 10.2196/19301 Chang, 2020, Blaming devices in online communication of the COVID-19 pandemic: stigmatizing cues and negative sentiment gauged with automated analytic techniques, J Med Internet Res, 22, 10.2196/21504 Chehal, 2020, COVID-19 pandemic lockdown: an emotional health perspective of Indians on Twitter, Int J Soc Psychiatry, 10.1177/0020764020940741 Chen, 2020, Unpacking the black box: how to promote citizen engagement through government social media during the COVID-19 crisis, Comput Hum Behav, 110, 10.1016/j.chb.2020.106380 Damiano, 2020, A content analysis of coronavirus tweets in the United States just prior to the pandemic declaration, Cyberpsychol Behav Soc Netw, 10.1089/cyber.2020.0425 Darling-Hammond, 2020, After “The China Virus” went viral: racially charged coronavirus coverage and trends in bias against Asian Americans, Health Educ Behav, 47, 870, 10.1177/1090198120957949 Das, 2020, Characterizing public emotions and sentiments in COVID-19 environment: a case study of India, J Hum Behav Soc Environ, 10.1080/10911359.2020.1781015 De Santis, 2020, An infoveillance system for detecting and tracking relevant topics from Italian tweets during the COVID-19 event, IEEE Access, 8, 132527, 10.1109/ACCESS.2020.3010033 Dheeraj, 2020, Analysing COVID-19 news impact on social media aggregation, Int J Adv Trends Comput Sci Eng, 9, 2848, 10.30534/ijatcse/2020/56932020 Essam, 2020, How do Arab tweeters perceive the COVID-19 pandemic?, J Psycholinguist Res, 10.1007/s10936-020-09715-6 Yin, 2020, COVID-19 information propagation dynamics in the Chinese Sina-microblog, Math Biosci Eng, 17, 2676, 10.3934/mbe.2020146 Gozzi, 2020, Collective response to media coverage of the COVID-19 pandemic on Reddit and Wikipedia: mixed-methods analysis, J Med Internet Res, 22, 10.2196/21597 Green, 2020, Elusive consensus: polarization in elite communication on the COVID-19 pandemic, Sci Adv, 6, 10.1126/sciadv.abc2717 Han, 2020, Using social media to mine and analyze public opinion related to COVID-19 in China, Int J Environ Res Public Health, 17 Jelodar, 2020, Deep Sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach, IEEE J Biomed Health Inform, 24, 2733, 10.1109/JBHI.2020.3001216 Jimenez-Sotomayor, 2020, Coronavirus, ageism, and Twitter: an evaluation of tweets about older adults and COVID-19, J Am Geriatr Soc, 68, 1661, 10.1111/jgs.16508 Kim, 2020, Effects of social grooming on incivility in COVID-19, Cyberpsychol Behav Soc Netw, 23, 519, 10.1089/cyber.2020.0201 Kurten, 2020, #Coronavirus: monitoring the Belgian Twitter discourse on the severe acute respiratory syndrome coronavirus 2 pandemic, Cyberpsychol Behav Soc Netw Kwon, 2020, Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis, J Biomed Inform, 111, 10.1016/j.jbi.2020.103601 Lai, 2020, Addressing immediate public coronavirus (COVID-19) concerns through social media: utilizing Reddit's AMA as a framework for public engagement with science, PLoS One, 15, 10.1371/journal.pone.0240326 Li, 2020, Data mining and content analysis of the Chinese social media platform Weibo during the early COVID-19 outbreak: retrospective observational infoveillance study, JMIR Public Health Surveill, 6, 10.2196/18700 Li, 2020, Constructing and communicating COVID-19 stigma on Twitter: a content analysis of tweets during the early stage of the COVID-19 outbreak, Int J Environ Res Public Health, 17 Lwin, 2020, Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends, JMIR Public Health Surveill, 6, 10.2196/19447 Ma, 2020, Effects of health information dissemination on user follows and likes during COVID-19 outbreak in China: data and content analysis, Int J Environ Res Public Health, 17, 10.3390/ijerph17145081 Medford, 2020, An “infodemic”: leveraging high-volume Twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak, Open Forum Infect Dis, 7, 10.1093/ofid/ofaa258 Mohamad, 2020, Creative production of ‘COVID-19 social distancing’ narratives on social media, Tijdsschr Econ Soc Geog, 111, 347, 10.1111/tesg.12430 Nguyen, 2020, Exploring U.S. shifts in anti-Asian sentiment with the emergence of COVID-19, Int J Environ Res Public Health, 17, 10.3390/ijerph17197032 Odlum, 2020, Application of topic modeling to tweets as the foundation for health disparity research for COVID-19, Stud Health Technol Inform, 272, 24 Park, 2020, Conversations and medical news frames on twitter: infodemiological study on COVID-19 in South Korea, J Med Internet Res, 22, 10.2196/18897 Pastor, 2020, Sentiment analysis of filipinos and effects of extreme community quarantine due to coronavirus (COVID-19) pandemic, SSRN, 10.2139/ssrn.3574385 Samuel, 2020, COVID-19 public sentiment insights and machine learning for tweets classification, Information (Basel), 11, 314, 10.3390/info11060314 Samuel, 2020, Feeling positive about reopening? New normal scenarios from COVID-19 US reopen sentiment analytics, IEEE Access, 8, 142173, 10.1109/ACCESS.2020.3013933 Su, 2020, Examining the impact of COVID-19 lockdown in Wuhan and Lombardy: a psycholinguistic analysis on Weibo and Twitter, Int J Environ Res Public Health, 17, 10.3390/ijerph17124552 Thelwall, 2020, COVID-19 tweeting in English: gender differences, Relaciones Públicas, 29 Wang, 2020, COVID-19 sensing: negative sentiment analysis on social media in China via BERT model, IEEE Access, 8, 138162, 10.1109/ACCESS.2020.3012595 Wicke, 2020, Framing COVID-19: how we conceptualize and discuss the pandemic on Twitter, PLoS One, 15, 10.1371/journal.pone.0240010 Xi, 2020, A thematic analysis of Weibo topics (Chinese twitter hashtags) regarding older adults during the COVID-19 outbreak, J Gerontol Psychol Sci Soc Sci, 10.1093/geronb/gbaa148 Xie, 2020, An extensive search trends-based analysis of public attention on social media in the early outbreak of COVID-19 in China, Risk Manag Healthc Policy, 13, 1353, 10.2147/RMHP.S257473 Xue, 2020, The hidden pandemic of family violence during COVID-19: unsupervised learning of tweets, J Med Internet Res, 22, 10.2196/24361 Yigitcanlar, 2020, How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories, Health Inf Sci Syst, 8, 37, 10.1007/s13755-020-00121-9 Yu, 2020, Analyzing Spanish news frames on Twitter during COVID-19–a network study of El País and El Mundo, Int J Environ Res Public Health, 17, 10.3390/ijerph17155414 Zhao, 2020, Chinese public's attention to the COVID-19 epidemic on social media: observational descriptive study, J Med Internet Res, 22, 10.2196/18825 Zhu, 2020, Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics, Chaos Solitons Fractals, 140, 10.1016/j.chaos.2020.110123 Schillinger, 2020, From “infodemics” to health promotion: a novel framework for the role of social media in public health, Am J Public Health, 110, 1393, 10.2105/AJPH.2020.305746 Daft, 1986, Organizational information requirements, media richness and structural design, Manag Sci, 32, 554, 10.1287/mnsc.32.5.554 de Vreese, 2016, Measuring media exposure in a changing communications environment, J Commun Methods Meas, 10, 69, 10.1080/19312458.2016.1150441 Schmunk