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Misinformation is often viewed as an information problem, attributed to people’s poor understanding of consensus-based facts. While this is a contributing factor, we propose that misinformation and conspiracy are also communicated differently to their audiences. Can we better reach these audiences by matching the style...
Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and decisions that use it. Most data-poisoning studies add a trigger to the training data and activate it in the prompt. False documents can also change factual responses wi...
What enters an LLM's context can influence its decision even when it should not determine it, and exposure systematically shifts decisions, with effects reaching nearly 50 percentage points in closed-weight models.
CheckThat! 2026 Task 3 asks systems to generate fact-checking articles, graded by an unweighted mean of four sub-metrics (M4). Our UTS submission placed 2nd of 11 teams (M4 = 0.484). The shipped system is a deterministic stub drafter wrapped by two single-lever interventions: a domain-attribution cite frame (HostCite)...
Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems often suffer from poor cross-domain generalization, weak rationale grounding, and reliance on costly prompting-based large language models (LLMs). We introduce CHARM, a MAC...
The results show that grievance is measured more faithfully by reading the surrounding context, and more honestly when tested on text the lexicon did not select, as evaluated on a non-circular benchmark.
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