Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak

By Cynthia Chew, Gunther Eysenbach. PLoS ONE, 2010

1,579 citations recorded by the source corpus.

Background Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary “infoveillance” approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms “H1N1” versus “swine flu” over time; 2) conduct a content analysis of “tweets”; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool. Methodology/Principal Findings Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords “swine flu,” “swineflu,” and/or “H1N1.” using Infovigil, an infoveillance system. Tweets using “H1N1” increased from 8.8% to 40.5% (R 2 = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data. Conclusions This study illustrates the potential of using social media to conduct “infodemiology” studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.

Subfields: Epidemiology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1cf1c903962a2296c90f6c6483abd4d5e00f605b

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Citations in the corpus

Effects of Sampling on Twitter Trend Detection (2016, background): “(Achrekar et al., 2011; Paul et al., 2014; Chew and Eysen-bach, 2010; Gesualdo et al., 2013; Aramaki et al., 2011; Ji et al., 2012; Culotta, 2010; Achrekar et al., 2012; Sadilek et al., 2012; Lamb et al., 2013; Yates et al., 2014; Parker et al., 2015; Nagel et al., 2013; de Quincey and Kostkova,…”
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach (2020, background): “Chew and Eysenbach’s study [6] showed that Twitter could be used for real-time “infodemiology” studies, providing a source of opinions for health authorities to respond to public concerns.”
Rapid assessment of disaster damage using social media activity (2016, background): “Opportunities provided by social media are used in various domains, including the economic (12), political (13–16), and social (14, 17–21) sciences, as well as in public health (22, 23).”
Measuring Post Traumatic Stress Disorder in Twitter (2014, neutral): “Freifeld, and Madoff 2009; Chew and Eysenbach 2010; Lamb, Paul, and Dredze 2013) and modeling (Sadilek, Kautz, and Silenzio 2012), rely on explicit mentions of illness or health issues; if people are sick, they say so.”
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media (2019, background): “This is perhaps surprising, as health promotion and public health researchers now pay considerable attention to the potential of the internet as a tool to diffuse health-related information (Chew and Eysenbach, 2010; Ritterband and Tate, 2009; Murray et al., 2009; Scanfeld et al., 2010; Signorini et”
Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines (2015, background): “For example: ‘‘news and information were the most commonly tweeted H1N1-related material’’ (Chew & Eysenbach, 2010, p. 10).”
A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication (2013, background): “Results The 98 selected studies are summarized by study design, social media tool/application, study purpose, participants/sample and sample size, measurement tools, results, conclusion, and use of social media in Multimedia Appendix 2 [24-121].”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, background): “Chew et al. [26] analyze the content of tweets shared during the H1N1 (i.e., swine ﬂu) outbreak, containing keywords and hash-tags related to the H1N1 event to determine the kind of information exchanged by social media users.”
Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (2020, background): “More importantly during the current COVID-19 pandemic, Twitter provides researchers the ability to study the role social media plays in the global health crisis [15-19].”
Prevalence of Health Misinformation on Social Media: Systematic Review (2021, background): “4% Twitter Pandemics 2010 Chew et al [47]”
Social Media: A Review and Tutorial of Applications in Medicine and Health Care (2014, background): “Examples of social media applications in health include (but are not limited to) access to educational resources by clinicians and patients [1-3], generation of content rich reference resources (eg, Wikipedia) [4], evaluation and reporting of real-time flu trends [5], catalyzing outreach during (pub”
Digital Response During the COVID-19 Pandemic in Saudi Arabia (2020, background): “The global experiences with the H1N1 influenza pandemic in 2009 [4] and Ebola virus in 2014 clearly indicated that timely and appropriate technology usage played a considerable role in controlling these pandemics [5-7].”
COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data (2020, background): “Social media is an important information source for a subset of the population, and previous seminal research has noted the potential of Twitter for providing real time content analysis, allowing public health authorities to rapidly respond to concerns raised by the public [14].”