Top Gear or Black Mirror: Inferring Political Leaning From Non-Political Content

TL;DR

This paper presents a machine learning approach to infer political leaning from non-political social media content, revealing a clear left-right divide in news sharing behaviors without relying on political text.

Contribution

It introduces a novel classifier that predicts political orientation from non-political tweets and social media activity, enabling polarization studies beyond political content.

Findings

High classification accuracy with F1 scores up to 0.85.

No correlation between political activity level and classification accuracy.

Distinctive left-right divide in news sharing observed in UK case study.

Abstract

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.