2016/03/09 by Yu Wang, Jiebo Luo, Wang, Yu +8 · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Electoral Systems and Political Participation #FOS: Computer and information sciences #Opinion Dynamics and Social Influence #Social Media and Politics #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.1603.03099
4 pages, to appear in the 10th International AAAI Conference on Web and Social Media
arxiv created 2016/03/09 · openalex publication_date 2016/03/09 · arxiv updated 2016/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians.