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Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter

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.

preprint2016arXivOpen access
Yu WangJiebo LuoRichard NiemiYuncheng LiTianran Hu
Social and Information Networks
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Open access5 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Yu WangJiebo LuoRichard NiemiYuncheng LiTianran Hu

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