ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912557677150208 |
|---|---|
| author | Bates, Luke Glockner, Max Nakov, Preslav Gurevych, Iryna |
| author_facet | Bates, Luke Glockner, Max Nakov, Preslav Gurevych, Iryna |
| contents | Conspiracy theories erode public trust in science and institutions while resisting debunking by evolving and absorbing counter-evidence. As AI-generated misinformation becomes increasingly sophisticated, understanding rhetorical patterns in conspiratorial content is important for developing interventions such as targeted prebunking and assessing AI vulnerabilities. We introduce ConspirED (CONSPIR Evaluation Dataset), which captures the cognitive traits of conspiratorial ideation in multi-sentence excerpts (80--120 words) from online conspiracy articles, annotated using the CONSPIR cognitive framework (Lewandowsky and Cook, 2020). ConspirED is the first dataset of conspiratorial content annotated for general cognitive traits. Using ConspirED, we (i) develop computational models that identify conspiratorial traits and determine dominant traits in text excerpts, and (ii) evaluate large language/reasoning model (LLM/LRM) robustness to conspiratorial inputs. We find that both are misaligned by conspiratorial content, producing output that mirrors input reasoning patterns, even when successfully deflecting comparable fact-checked misinformation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety Bates, Luke Glockner, Max Nakov, Preslav Gurevych, Iryna Computation and Language Conspiracy theories erode public trust in science and institutions while resisting debunking by evolving and absorbing counter-evidence. As AI-generated misinformation becomes increasingly sophisticated, understanding rhetorical patterns in conspiratorial content is important for developing interventions such as targeted prebunking and assessing AI vulnerabilities. We introduce ConspirED (CONSPIR Evaluation Dataset), which captures the cognitive traits of conspiratorial ideation in multi-sentence excerpts (80--120 words) from online conspiracy articles, annotated using the CONSPIR cognitive framework (Lewandowsky and Cook, 2020). ConspirED is the first dataset of conspiratorial content annotated for general cognitive traits. Using ConspirED, we (i) develop computational models that identify conspiratorial traits and determine dominant traits in text excerpts, and (ii) evaluate large language/reasoning model (LLM/LRM) robustness to conspiratorial inputs. We find that both are misaligned by conspiratorial content, producing output that mirrors input reasoning patterns, even when successfully deflecting comparable fact-checked misinformation. |
| title | ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.20468 |