Quantifying Political Partisanship for Cross-Platform Analyses

2026-07-27T08:52:08Zf554644083f29413276c8b35632de17f0c5505630a9407a95fa662092799aa95
AI safetyLLMsacademic researchalgorithmic biasconspiracy theoriescontent moderationdata ethicsgraph neural networksonline harmspolitical polarizationprivacyservice ecosystemssexual healthsocial networksurban segregation

What happened

This arXiv feed contains multidisciplinary studies on social networks, platform moderation, political polarization, conspiracy propagation, urban segregation, service ecosystems, graph neural networks, sexual-contact networks, and large language model behavior. The material is primarily academic and descriptive, with no disclosed software vulnerability, exploit, breach, or direct cybersecurity incident. Some findings have trust-and-safety relevance, particularly content moderation, conspiracy propagation, algorithmic bias, privacy, and societal harms from AI-mediated information systems.

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_si
Record identifier
f554644083f29413276c8b35632de17f0c5505630a9407a95fa662092799aa95
Enrichment time
2026-07-27T08:52:08Z
AI-assisted enrichment
Yes

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