Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions
2026-04-21T08:52:15Z•0363a338a4adadab11b1c0a7516c2637eaceebba85cc74625ba31306611df0a4
CCSVIHawai'iIDP-DSNLLMMICETopoSimX/Twitterclaim extractionclimate hazardscommunity detectiondynamic signed networksfact-checkinggeospatial visualizationlarge language modelsminority communitiesmoral framingmultimodalpolitical toxicityprivacy anxiety","terms of service"psycholinguisticssocial mediasocial simulationspectral methodsstochastic block modeltoxicity detection
What happened
This arXiv feed (multiple CS/social-impact papers) covers empirical and methodological advances across social media analysis, LLM applications, network science, and geospatial visualization. Key contributions: (1) a large-scale analysis of political toxicity on X around the 2024 U.S. election, showing issue-dependent toxicity (highest on identity issues), partisan asymmetries, and psycholinguistic drivers; (2) CCSVI, a web-based geospatial platform integrating climate hazard and social vulnerability data for Hawai‘i; (3) TopoSim, a topology-aware LLM-driven social simulation framework that enc
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 0363a338a4adadab11b1c0a7516c2637eaceebba85cc74625ba31306611df0a4
- Enrichment time
- 2026-04-21T08:52:15Z
- AI-assisted enrichment
- Yes
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