Isaac Chun-Hai Fung1, Ashley M Jackson2, Jennifer O Ahweyevu2, Jordan H Grizzle2, Jingjing Yin3, Zion Tsz Ho Tse4, Hai Liang5, Juliet N Sekandi6, King-Wa Fu7. 1. Department of Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA. Electronic address: cfung@georgiasouthern.edu. 2. Department of Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA. 3. Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA. 4. School of Electrical and Computer Engineering, The University of Georgia, Athens, GA. 5. School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong. 6. Global Health Institute, The University of Georgia, Athens, GA; Department of Epidemiology and Biostatistics, The University of Georgia, Athens, GA. 7. Journalism and Media Studies Centre, The University of Hong Kong, Hong Kong; MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA.
Abstract
BACKGROUND: Advocates use the hashtag #GlobalHealth on Twitter to draw users' attention to prominent themes on global health, to harness their support, and to advocate for change. OBJECTIVES: We aimed to describe #GlobalHealth tweets pertinent to given major health issues. METHODS: Tweets containing the hashtag #GlobalHealth (N = 157,951) from January 1, 2014, to April 30, 2015, were purchased from GNIP Inc. We extracted 5 subcorpora of tweets, each with 1 of 5 co-occurring disease-specific hashtags (#Malaria, #HIV, #TB, #NCDS, and #NTDS) for further analysis. Unsupervised machine learning was applied to each subcorpus to categorize the tweets by their underlying topics and obtain the representative tweets of each topic. The topics were grouped into 1 of 4 themes (advocacy; epidemiological information; prevention, control, and treatment; societal impact) or miscellaneous. Manual categorization of most frequent users was performed. Time zones of users were analyzed. FINDINGS: In the entire #GlobalHealth corpus (N = 157,951), there were 40,266 unique users, 85,168 retweets, and 13,107 unique co-occurring hashtags. Of the 13,087 tweets across the 5 subcorpora with co-occurring hashtag #malaria (n = 3640), #HIV (n = 3557), #NCDS (noncommunicable diseases; n = 2373), #TB (tuberculosis; n = 1781), and #NTDS (neglected tropical diseases; n = 1736), the most prevalent theme was prevention, control, and treatment (4339, 33.16%), followed by advocacy (3706, 28.32%), epidemiological information (1803, 13.78%), and societal impact (1617, 12.36%). Among the top 10 users who tweeted the highest number of tweets in the #GlobalHealth corpus, 5 were individual professionals, 3 were news media, and 2 were organizations advocating for global health. The most common users' time zone was Eastern Time (United States and Canada). CONCLUSIONS: This study highlighted the specific #GlobalHealth Twitter conversations pertinent to malaria, HIV, tuberculosis, noncommunicable diseases, and neglected tropical diseases. These conversations reflect the priorities of advocates, funders, policymakers, and practitioners of global health on these high-burden diseases as they presented their views and information on Twitter to their followers.
BACKGROUND: Advocates use the hashtag #GlobalHealth on Twitter to draw users' attention to prominent themes on global health, to harness their support, and to advocate for change. OBJECTIVES: We aimed to describe #GlobalHealth tweets pertinent to given major health issues. METHODS:Tweets containing the hashtag #GlobalHealth (N = 157,951) from January 1, 2014, to April 30, 2015, were purchased from GNIP Inc. We extracted 5 subcorpora of tweets, each with 1 of 5 co-occurring disease-specific hashtags (#Malaria, #HIV, #TB, #NCDS, and #NTDS) for further analysis. Unsupervised machine learning was applied to each subcorpus to categorize the tweets by their underlying topics and obtain the representative tweets of each topic. The topics were grouped into 1 of 4 themes (advocacy; epidemiological information; prevention, control, and treatment; societal impact) or miscellaneous. Manual categorization of most frequent users was performed. Time zones of users were analyzed. FINDINGS: In the entire #GlobalHealth corpus (N = 157,951), there were 40,266 unique users, 85,168 retweets, and 13,107 unique co-occurring hashtags. Of the 13,087 tweets across the 5 subcorpora with co-occurring hashtag #malaria (n = 3640), #HIV (n = 3557), #NCDS (noncommunicable diseases; n = 2373), #TB (tuberculosis; n = 1781), and #NTDS (neglected tropical diseases; n = 1736), the most prevalent theme was prevention, control, and treatment (4339, 33.16%), followed by advocacy (3706, 28.32%), epidemiological information (1803, 13.78%), and societal impact (1617, 12.36%). Among the top 10 users who tweeted the highest number of tweets in the #GlobalHealth corpus, 5 were individual professionals, 3 were news media, and 2 were organizations advocating for global health. The most common users' time zone was Eastern Time (United States and Canada). CONCLUSIONS: This study highlighted the specific #GlobalHealth Twitter conversations pertinent to malaria, HIV, tuberculosis, noncommunicable diseases, and neglected tropical diseases. These conversations reflect the priorities of advocates, funders, policymakers, and practitioners of global health on these high-burden diseases as they presented their views and information on Twitter to their followers.
Authors: Braydon J Schaible; Kassandra R Snook; Jingjing Yin; Ashley M Jackson; Jennifer O Ahweyevu; Muhling Chong; Zion Tsz Ho Tse; Hai Liang; King-Wa Fu; Isaac Chun-Hai Fung Journal: Perm J Date: 2019-07-08
Authors: Ashley M Jackson; Lindsay A Mullican; Jingjing Yin; Zion Tsz Ho Tse; Hai Liang; King-Wa Fu; Jennifer O Ahweyevu; Jimmy J Jenkins Iii; Nitin Saroha; Isaac Chun-Hai Fung Journal: Ann Glob Health Date: 2018-11-05 Impact factor: 2.462