Agentic detection of online conspiracies shows context improves accuracy and reveals limits
A study tests an interactive approach that uses social context to judge conspiracy posts, with mixed gains and clear limits.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Lior Biton, Oren Tsur
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
The study used an agentic system to classify Hebrew conspiracy tweets, sampling user history and ego-network context. It found contextual retrieval improved accuracy over text-only methods but also caused more false positives in some cases and required careful tool use.
Why it matters
In practice, adding context can help separate sarcasm or mockery from true endorsement, guiding safer moderation and research use.
Contextual tools improved detection accuracy in many cases, showing the value of looking at user history and social ties rather than text alone.
However, adding context increased complexity and could misinterpret satire or critical discussion as endorsement, highlighting trade-offs in real-world use.
Original sources · 1
- Agentic Detection of Online Conspiracies ↗arXiv · 2026-09-24
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Results are dataset- and model-specific; hyperparameters were not tuned and findings may differ with other languages, posts, or model choices.
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