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

TL;DR

This paper introduces a framework combining LDA and negative binomial regression to infer the topic preferences of Trump's Twitter followers based on their 'likes', revealing which topics garner the most engagement.

Contribution

The study presents a novel approach integrating topic modeling and regression analysis to quantify political followers' preferences on social media.

Findings

Attacking Democrats yields the most 'likes' for Trump.

The framework effectively links tweet topics to follower engagement.

Method is applicable to other politicians' social media analysis.

Abstract

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.