Comparing Sentiment Contagion in AI-Agent and Human Social Networks: Evidence from MOLTBOOK
2026-06-08T08:52:16Z•74abcc48e971553bc589de1f6372a5c394533f94dc68fd6b12e8173ead8f1306
adversarial-datasetai-generated-contentbot-detectioncarbon-footprintcultural-marketsdaoevent-driven-simulatorsfinancial-networksgovernanceimpersonationinteroperability-standardsllmsmarLmisinformationmulti-agent-systemsopinion-dynamicspatentsrecommender-systemssentiment-analysissocial-botssustainability
What happened
This arXiv feed (2026-06-08) collects papers on AI/social intelligence, multi-agent systems, and socio-technical governance. Notable security-relevant items: “Adversarial Creation and Detection of AI-Generated Social Bot Content” describes an adversarial methodology to synthesize paired human/AI messages and a multilingual cross-platform dataset that substantially improves detection of AI-generated social media content while modelling impersonation strategies (dual-use: strengthens detectors but also documents impersonation tactics). Several papers explore opinion-manipulation risk surfaces —
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 74abcc48e971553bc589de1f6372a5c394533f94dc68fd6b12e8173ead8f1306
- Enrichment time
- 2026-06-08T08:52:16Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.