Pandemics in the Age of Twitter: Content Analysis of “Tweets†During the H1N1 Outbreak
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Abstract
Background: Twitter is an instant micro-blogging service that allows users to post, read, and exchange information and thoughts easily with masses across the globe. In response to the 2009 Influenza A virus subtype H1N1 outbreak (aka "swine flu"), users produced thousands of posts on the subject. Media outlets have claimed that Twitter and other forms of social media have led to the viral distribution of mass misinformation and may be a threat to public health and government initiatives. However, quantifiable evidence of these claims has not been substantiated.
Objective: This exploratory project aims to analyze the content of Twitter posts or “tweets†shared during the H1N1 outbreak to determine the types and quality of information that social media users are exchanging in pandemics.
Methods: Using the Infovigil system, an emerging infoveillance system, we are continuously identifying and archiving health-related tweets. Between April 28 and May 11, 2009, we archived over 300,000 tweets containing the keywords or hashtags “swine fluâ€, “swinefluâ€, or “H1N1â€. A random selection of tweets from each hour of each day were coded for content by two raters. A multi-axial coding scheme was created using an iterative process to reflect the range of data. Data analysis consisted of descriptive statistics and univariate analysis of content between days. Non-English posts and reposts (“retweetsâ€) were excluded from the analysis.
Results: Preliminary analysis of 400 tweets indicates that news posts were the most common type of information shared (46%) followed by public health education (19.18%) and H1N1-related humour (18.25%). 36.75% of all posts quoted news articles verbatim and provided URLs to the source. Only 7 cases could be identified as possible sources of misinformation.
Conclusions: Contrary to anecdotal evidence, misinformation is not rampantly spread via Twitter. Instead, the service is being utilized to distribute news and information from credible sources and almost one of five tweets are of humorous nature. Contrary to some media reports of Twitter fueling an epidemic of misinformation, Twitter can and is already used to quickly disseminate pandemic information to the public. Further analysis of tweets collected during an epidemic will allow us to refine the Infovigil system for twitter-based syndromic surveillance
Objective: This exploratory project aims to analyze the content of Twitter posts or “tweets†shared during the H1N1 outbreak to determine the types and quality of information that social media users are exchanging in pandemics.
Methods: Using the Infovigil system, an emerging infoveillance system, we are continuously identifying and archiving health-related tweets. Between April 28 and May 11, 2009, we archived over 300,000 tweets containing the keywords or hashtags “swine fluâ€, “swinefluâ€, or “H1N1â€. A random selection of tweets from each hour of each day were coded for content by two raters. A multi-axial coding scheme was created using an iterative process to reflect the range of data. Data analysis consisted of descriptive statistics and univariate analysis of content between days. Non-English posts and reposts (“retweetsâ€) were excluded from the analysis.
Results: Preliminary analysis of 400 tweets indicates that news posts were the most common type of information shared (46%) followed by public health education (19.18%) and H1N1-related humour (18.25%). 36.75% of all posts quoted news articles verbatim and provided URLs to the source. Only 7 cases could be identified as possible sources of misinformation.
Conclusions: Contrary to anecdotal evidence, misinformation is not rampantly spread via Twitter. Instead, the service is being utilized to distribute news and information from credible sources and almost one of five tweets are of humorous nature. Contrary to some media reports of Twitter fueling an epidemic of misinformation, Twitter can and is already used to quickly disseminate pandemic information to the public. Further analysis of tweets collected during an epidemic will allow us to refine the Infovigil system for twitter-based syndromic surveillance
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