129 citations · 848 across the 73 of their papers we have counts for
78 papers
Misinformation Without Triggers: From Factual Answers to Downstream Decisions
Lin Tian, Marian-Andrei Rizoiu
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 d…
Reading Too Much into Context: Passive Exposure Can Steer LLM Decisions
Yuxiang Zheng, Lin Tian, Marian-Andrei Rizoiu
Large language model (LLM) assistants can now search the web and consult external sources while completing user requests. These sources can provide useful evidence, but they can al…
Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour
Huixiang Fu, Marian-Andrei Rizoiu
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-dom…
From a Word-Level Dictionary to Sentence-Level Semantics: Multilingual Grievance Labelling with Contextual Models
Lin Tian, Marian-Andrei Rizoiu
Grievance is one of the warning signs analysts look for when assessing threats of violence. It is increasingly measured at scale from online text, most often with word-level lexico…
UTS at CheckThat! 2026: Cite-Frame Engineering for Generated Fact-Checking Articles
Dima Galat, Marian-Andrei Rizoiu
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…
UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors
Dima Galat, Marian-Andrei Rizoiu
We investigate which language model evasion attacks survive state-of-the-art adversarial fine-tuning, developing strategies that sweep the top 5 positions on the ELOQUENT 2026 Voig…