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Infodemiology &
Social Contagion

Social Listening Green.png

My research applies computational methods to track and study the spread of narratives on social media discourse. Using a hybrid methodology that combines machine learning with human-led content analysis, I investigate how misinformation and conspiratorial narratives emerge and propagate through online networks.  I also conduct social network analysis to examine online political mobilization, which can influence coordinated actions in the real world. 

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My work can be described as infodemiology ("information epidemiology"), but instead of examining sequences of amino acids to track the spread of viruses, I use sequences of letters to track narratives.

 

See the Journal of Medical Internet Research (JMIR) Infodemiology for further description on the field, where I also serve as an associate editor.

Related Publications

Haupt, M. R., Chiu, M., Chang, J., Li, Z., Cuomo, R., & Mackey, T. K. (2023). Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding. Plos one, 18(12), e0295414. [LINK] [FILE]

Haupt, M. R., Li, J., & Mackey, T. K. (2021). Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning. Big Data & Society, 8(1) [LINK] [FILE]

Haupt, M. R., Jinich-Diamant, A., Li, J., Nali, M., & Mackey, T. K. (2021). Characterizing Twitter User Topics and Communication Network Dynamics of the “Liberate” Movement during COVID-19 using Unsupervised Machine Learning and Social Network Analysis. Online Social Networks and Media, 21, 100114. [LINK] [FILE]

Haupt, M. R., Xu, Q., Yang, J., Cai, M., & Mackey, T. K. (2021). Characterizing Vaping Industry Political Influence and Mobilization on Facebook: Social Network Analysis. Journal of Medical Internet Research, 23(10), e28069. [LINK] [FILE]

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