Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014

Isaac Chun-Hai Fung, Jingjing Yin, Keisha D. Pressley, Carmen H. Duke, Chen Mo, Hai Liang, King-Wa Fu, Zion Tsz Ho Tse and Su-I Hou
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Isaac Chun-Hai Fung: Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
Jingjing Yin: Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
Keisha D. Pressley: Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
Carmen H. Duke: Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
Chen Mo: Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
Hai Liang: School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
King-Wa Fu: Journalism and Media Studies Centre, The University of Hong Kong, HongKong, China
Zion Tsz Ho Tse: School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
Su-I Hou: College of Community Innovation and Education, The University of Central Florida, Orlando, FL 32816, USA

Data, 2019, vol. 4, issue 2, 1-12

Abstract: As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models.

Keywords: global health; health promotion; HIV/AIDS; social media; supervised machine learning; Twitter (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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