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# Agentic Detection of Online Conspiracies

**Authors:** Lior Biton; Oren Tsur · **Year:** 2026 · **Venue:** arXiv:2609.30250 **Link:** [arxiv.org/abs/2609.30250](https://arxiv.org/abs/2609.30250) · library `langstream-2026-biton-agentic-detection-online-conspiracies` · **Licence:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) — summary only

[**0** citations](https://openalex.org/works?filter=cites:W7214363184) openalex · 2026-10-18[arXiv:2609.30250](https://openalex.org/S4306400194)[licence](http://creativecommons.org/licenses/by-nc-sa/4.0/)

- [Publisher's page](https://arxiv.org/abs/2609.30250)
- [PDF](https://arxiv.org/pdf/2609.30250)
- [arXiv 2609.30250](https://arxiv.org/abs/2609.30250)
- [OpenAlex](https://openalex.org/W7214363184)

**Authors (2)**

1. [Lior Biton](https://openalex.org/A5152855567) h 0 · 0 citations · 1 works
2. [Oren Tsur](https://openalex.org/A5011919726) [ORCID](https://orcid.org/0000-0002-6809-2234) · h 16 · 2691 citations · 56 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The authors ask whether a Hebrew tweet endorses a conspiracy theory, or only mocks, quotes or reports it. They argue that this is a question about the speaker's intent, so the surrounding context matters. Their data is a Hebrew tweet archive of about 270 million tweets from 2018 to 2023. From it they build an adversarial set of 33,121 candidates, of which 504 were labelled by two experts (331 non-conspiracy, 173 conspiracy). An LLM agent can call four tools: tweet metadata, author profile, the user's recent posts, and the retweet ego network. Text-only classification reaches F1 0.535. The agent with all tools reaches accuracy 0.790 and F1 0.730, and it beats the same context pasted into the prompt (F1 0.67). False positives fall from 142 to 76 and false negatives from 58 to 30. The agent reads about 1.5 times more input characters than the prompt-only version. A two-agent debate is weaker, at F1 0.558.

## Ideas

- I1. A speaker-commitment flag in `prag`: when a sentence quotes a claim and then ridicules it ("the storm is Pfizer's plot, there is no hope for you"), mark the claim as reported or mocked, not as asserted. The paper's own error classes give the test cases: strong criticism of a government versus a conspiracy claim, and a quote that is mocked.
- I2. A tool-economy gate in the world reader: consult outside context only when the sentence's reading is unresolved. The paper finds that 10.9% of cases use no tool, and in those the agent agrees with text-only classification 94.5% of the time. This is a measurable rule for when the world needs more evidence.
- I3. Use the paper's error categories as a checklist of hard pragmatic cases for `prag`, written in our own examples. The labelled set is non-commercial, so it stays a measurement, never part of the build.

## For Mova

I1 goes into the pragmatics rules of `prag` (the speaker-commitment part of the frame). I2 goes into `world`, as a rule for when to read further context. I3 is a test list for `prag`, written in our own words and examples. Nothing from the dataset is carried into a release.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2609.30250](https://arxiv.org/abs/2609.30250)

```
@misc{biton2026agentic,
  title = {{Agentic Detection of Online Conspiracies}},
  author = {Biton, Lior and Tsur, Oren},
  year = {2026},
  eprint = {2609.30250},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.30250}
}
```

- [← previous NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2025-shmidman-neodictabert-pushing-frontier-bert-models/)
- [next → HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2026-kayzer-hebatron-hebrew-specialized-open-weight/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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50 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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