Agentic Detection of Online Conspiracies
An agentic tool-using framework detects conspiratorial intent in Hebrew tweets better than text-only models.
The paper treats conspiracy detection as inferring a speaker's intent rather than matching explicit claims, using an agent with tools for social-context queries. The corpus covers about 80–90% of public Hebrew tweets from late 2018 to early 2023, including election cycles and COVID vaccination campaigns. On a manually annotated adversarial set, context-aware workflows beat text-only classification, and the agent outperforms a non-agentic model given the same contexts. The authors also analyze errors and token-cost tradeoffs of selective tool use.
- Detection targets speaker intent rather than explicit conspiracy keywords.
- Corpus covers roughly 80 to 90 percent of public Hebrew tweets, 2018–2023.
- Agentic tool use beats text-only classifiers and non-agentic context models.
- Authors analyze error modes and token-economy tradeoffs.
From the original article
1 min read at arxiv.orgConspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism, satire, or mockery. The main challenge is therefore not only recognizing conspiracy-related claims, but inferring the speaker's intent -- the utterance's illocutionary force.
By Lior Biton · arxiv.org
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