How Growing Toxicity Manifests: A Topic Trajectory Analysis of U.S. Immigration Discourse on Social Media

TL;DR

This study analyzes how toxicity in U.S. immigration discussions on social media evolves over time, revealing that increasing toxicity correlates with alarmist topics while decreasing toxicity aligns with policy discussions.

Contribution

The paper introduces a novel pipeline combining hierarchical topic discovery and trajectory analysis to study toxicity dynamics in large-scale social media discourse.

Findings

Users with increasing toxicity shift toward alarmist, fear-based topics.

Users with decreasing toxicity focus more on legal and policy issues.

Both groups' trajectories significantly differ from their reference groups.

Abstract

In the online public sphere, discussions about immigration often become increasingly fractious, marked by toxic language and polarization. Drawing on 4 million X posts over six months, we combine a user- and topic-centric approach to study how shifts in toxicity manifest as topical shifts. Our topic discovery method, which leverages instruction-based embeddings and recursive HDBSCAN, uncovers 157 fine-grained subtopics within the U.S. immigration discourse. We focus on users in four groups: (1) those with increasing toxicity, (2) those with decreasing toxicity, and two reference groups with no significant toxicity trend but matched toxicity levels. Treating each posting history as a trajectory through a five-dimensional topic space, we compare average group trajectories using permutational MANOVA. Our findings show that users with increasing toxicity drift toward alarmist, fear-based frames, whereas those with decreasing toxicity pivot toward legal and policy-focused themes. Both patterns diverge statistically significantly from their reference groups. This pipeline, which combines hierarchical topic discovery with trajectory analysis, offers a replicable method for studying dynamic conversations around social issues at scale.