23 citations · 124 across the 35 of their papers we have counts for
26 papers · 1 filter
A Browser-based Open Source Assistant for Multimodal Content Verification
Rosanna Milner, Michael Foster, Twin Karmakharm +5
Disinformation and false content produced by generative AI pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media information. While t…
Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks
Olesya Razuvayevskaya, Kalina Bontcheva
This study presents the first large-scale comparison of persuasion techniques present in crowd- versus professionally-written debunks. Using extensive datasets from Community Notes…
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…
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative Classification
Iknoor Singh, Carolina Scarton, Kalina Bontcheva
The proliferation of online news and the increasing spread of misinformation necessitate robust methods for automatic data analysis. Narrative classification is emerging as a impor…
UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections
Fatima Haouari, Carolina Scarton, Nicolò Faggiani +5
Misleading narratives play a crucial role in shaping public opinion during elections, as they can influence how voters perceive candidates and political parties. This entails the n…
A Dataset for Analysing News Framing in Chinese Media
Owen Cook, Yida Mu, Xinye Yang +2
Framing is an essential device in news reporting, allowing the writer to influence public perceptions of current affairs. While there are existing automatic news framing detection…
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…
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Yida Mu, Peizhen Bai, Kalina Bontcheva +1
Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification app…
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling
Yida Mu, Chun Dong, Kalina Bontcheva +1
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic…
Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claims
Freddy Heppell, Mehmet E. Bakir, Kalina Bontcheva
As Large Language Models become more proficient, their misuse in coordinated disinformation campaigns is a growing concern. This study explores the capability of ChatGPT with GPT-3…
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Ben Wu, Yue Li, Yida Mu +3
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks…
Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study
Freddy Heppell, Kalina Bontcheva, Carolina Scarton
This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn.world) and WarOnFakes (waronfakes.com), which publish content in Ara…
Examining Temporal Bias in Abusive Language Detection
Mali Jin, Yida Mu, Diana Maynard +1
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right throug…
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets
Yida Mu, Xingyi Song, Kalina Bontcheva +1
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that c…
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…
Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking
Iknoor Singh, Carolina Scarton, Xingyi Song +1
Finding previously debunked narratives involves identifying claims that have already undergone fact-checking. The issue intensifies when similar false claims persist in multiple la…
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
Yida Mu, Ye Jiang, Freddy Heppell +4
The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challe…
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Yida Mu, Ben P. Wu, William Thorne +5
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts. However, due to…
Examining Temporalities on Stance Detection towards COVID-19 Vaccination
Yida Mu, Mali Jin, Kalina Bontcheva +1
Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a…
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…
It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits
Yida Mu, Kalina Bontcheva, Nikolaos Aletras
New events emerge over time influencing the topics of rumors in social media. Current rumor detection benchmarks use random splits as training, development and test sets which typi…