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Social media platforms are often blamed for exacerbating politicalpolarization and worsening public dialogue. Many claim hyperpartisan users postpernicious content, slanted to their political views, inciting contentious andtoxic conversations. However, what factors, actually contribute to increasedonline toxicity and negative interactions? In this work, we explore the rolethat political ideology plays in contributing to toxicity both on an individualuser level and a topic level on Twitter. To do this, we train and open-source aDeBERTa-based toxicity detector with a contrastive objective that outperformsthe Google Jigsaw Persective Toxicity detector on the Civil Comments testdataset. Then, after collecting 187 million tweets from 55,415 Twitter users,we determine how several account-level characteristics, including politicalideology and account age, predict how often each user posts toxic content.Running a linear regression, we find that the diversity of views and thetoxicity of the other accounts with which that user engages has a more markedeffect on their own toxicity. Namely, toxic comments are correlated with userswho engage with a wider array of political views. Performing topic analysis onthe toxic content posted by these accounts using the large language model MPNetand a version of the DP-Means clustering algorithm, we find similar behavioracross 6,592 individual topics, with conversations on each topic becoming moretoxic as a wider diversity of users become involved.

Twits, Toxic Tweets, and Tribal Tendencies: Trends in Politically Polarized Posts on Twitter