43 citations · 102 across the 11 of their papers we have counts for
11 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, Olesya Razuvayevskaya, Kalina Bontcheva +1
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, João Luz, 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 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…
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…
EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles
João A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva +1
This work introduces EUvsDisinfo, a multilingual dataset of disinformation articles originating from pro-Kremlin outlets, along with trustworthy articles from credible / less biase…
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…
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Olesya Razuvayevskaya, Ben Wu, Joao A. Leite +5
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrate…
Noisy Self-Training with Data Augmentations for Offensive and Hate Speech Detection Tasks
João A. Leite, Carolina Scarton, Diego F. Silva
Online social media is rife with offensive and hateful comments, prompting the need for their automatic detection given the sheer amount of posts created every second. Creating hig…
SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
Ben Wu, Olesya Razuvayevskaya, Freddy Heppell +4
This paper describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multi-lingual setup. For Subtask 1…
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
João A. Leite, Diego F. Silva, Kalina Bontcheva +1
Hate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capabl…