Pandemics in the Age of Twitter
Content Analysis of Tweets during the 2009 H1N1 Outbreak
Bibliographic Data
| ID | 23344372 |
|---|---|
| Authors | Cynthia Chew (0000-0002-3548-8064, University Health Network), Gunther Eysenbach (0000-0001-6479-5330, University Health Network, corresponding author) |
| Editors | Margaret Sampson (0000-0003-3497-8878) |
| Year | 2010 |
| Volume | 5 |
| Issue | 11 |
| Pages | e14118 |
| Publication date | 2010-11-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PLoS ONE (JOURNAL) |
| Journal identifiers | ISSN: 1932-6203 • E-ISSN: 1932-6203 |
| Publisher | Public Library of Science (PLoS) (PUBLISHER) |
| DOI | 10.1371/journal.pone.0014118 |
| PMID | 21124761 |
| OpenAlex | W1551484630 |
| Language | EN |
| Citations received | 237 |
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.
Computer security · Content analysis · Coronavirus disease 2019 (COVID-19) · Data science · Descriptive statistics · Exploratory analysis · Government (linguistics) · Internet privacy · Microblogging · Misinformation · Pandemic · Public health · Sentiment analysis · Social media · Sociology · Statistics · Upload · World Wide Web · Computer Science · Data-Driven Disease Surveillance · Medicine · Misinformation and Its Impacts
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| Unique citing works | 237 |
|---|---|
| Citations per year | 15,8 |
| Citation span | 2011 - 2026 (16) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 234 |