ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety
This addresses the issue of AI safety and misinformation for researchers and developers, though it is incremental as it builds on existing cognitive frameworks.
The paper tackles the problem of AI-generated misinformation by introducing ConspirED, a dataset annotated for cognitive traits of conspiracy theories, and finds that large language models are misaligned by conspiratorial content, producing outputs that mirror input reasoning patterns.
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.