"F*** You Biden": Cross-Partisan Electoral Toxicity on X
Danishjeet Singh, Anindya Mondal, Filippo Menczer

TL;DR
This study analyzes toxicity in political discourse on X during the 2024 U.S. election, revealing asymmetries in toxicity levels and reply patterns between Democrats and Republicans.
Contribution
It uncovers the asymmetry where Republican posts are more toxic but attract fewer toxic replies, highlighting the role of reply volume in toxicity dynamics.
Findings
Republican posts are more toxic than Democratic posts.
Democratic posts attract more toxic replies despite lower toxicity.
Most replies to Democratic posts come from Republicans, explaining higher toxicity.
Abstract
Political discourse on social media has grown increasingly toxic, with electoral periods amplifying partisan hostility and cross-group attacks. Yet it remains unclear whether toxicity in online political speech reflects how partisans communicate within their own circles, or how aggressively they engage with the opposition. Disentangling these dynamics is critical for understanding online political hostility and for designing effective content moderation. We examine this question at scale using a large collection of original posts and replies from X (formerly Twitter), collected during the 2024 U.S. presidential election. Using a human-validated large language model to classify the political alignment of posts and users, and the Perspective API for toxicity scoring, we uncover a striking asymmetry: Republican-leaning posts are significantly more toxic than Democratic-leaning posts, yet…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
