6 papers
Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts
Kevin Thomas, Milosz Kasprzyk, Reuel C Igbokwe Onuigbo +5
Detecting machine-generated disinformation on social media is increasingly difficult as large language models (LLMs) make it easier to generate and rewrite misleading content at sc…
LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems
João A. Leite, João A. Leite, Olesya Razuvayevskaya +2
Automated fact-checking (AFC) systems are susceptible to adversarial attacks, enabling false claims to evade detection. Existing adversarial frameworks typically rely on injecting…
Tailored untruths: How personalisation challenges LLM safeguards
João A. Leite, Arnav Arora, Silvia Gargova +5
Large Language Models (LLMs) can generate highly persuasive disinformation, yet little is known about how effectively they personalise it across languages and demographic groups. W…
A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models
Ivan Srba, Olesya Razuvayevskaya, João A. Leite +10
In the age of social media and generative AI, the ability to automatically assess the credibility of online content has become increasingly critical, complementing traditional appr…
A Cross-Domain Study of the Use of Persuasion Techniques in Online Disinformation
João A. Leite, Olesya Razuvayevskaya, Carolina Scarton +1
Disinformation, irrespective of domain or language, aims to deceive or manipulate public opinion, typically through employing advanced persuasion techniques. Qualitative and quanti…
Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals
João A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva +1
Credibility signals represent a wide range of heuristics typically used by journalists and fact-checkers to assess the veracity of online content. Automating the extraction of cred…