How Growing Toxicity Manifests: A Topic Trajectory Analysis of U.S. Immigration Discourse on Social Media
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915416681480192 |
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| author | Joh, Una Li, Yiqi Hemsley, Jeff |
| author_facet | Joh, Una Li, Yiqi Hemsley, Jeff |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Growing Toxicity Manifests: A Topic Trajectory Analysis of U.S. Immigration Discourse on Social Media Joh, Una Li, Yiqi Hemsley, Jeff Social and Information Networks J.4; K.4.2; I.2.7 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. |
| title | How Growing Toxicity Manifests: A Topic Trajectory Analysis of U.S. Immigration Discourse on Social Media |
| topic | Social and Information Networks J.4; K.4.2; I.2.7 |
| url | https://arxiv.org/abs/2507.21418 |