How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

TL;DR

This paper investigates political polarization on Twitter during the 2016 US presidential election by classifying followers of Trump and Clinton using multimodal features from tweets and profile images, revealing measurable polarization.

Contribution

It introduces a multimodal classification approach combining text and visual features to quantify political polarization on social media for the first time.

Findings

Achieved 69% classification accuracy distinguishing Trump and Clinton followers.

Demonstrated that social media polarization reflects real-world political divides.

Showed that integrating text and visual features improves classification performance.

Abstract

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.