activity
20242026
collaborators

17 papers

cs.CV2026

MigrationNarrate: A Dataset for Detection of Migration Narratives in YouTube Videos

Fatima Haouari, Carolina Scarton, Kalina Bontcheva

Narratives are central to how social communication is framed, making their detection critical for understanding and analysing public discourse. Prior work has explored narrative de…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

CACARA: Cross-Modal Alignment Leveraging a Text-Centric Approach for Cost-Effective Multimodal and Multilingual Learning

Diego A. B. Moreira, Alef I. Ferreira, Jhessica Silva +10

As deep learning models evolve, new applications and challenges are rapidly emerging. Tasks that once relied on a single modality, such as text, images, or audio, are now enriched…

cs.CL2025

SCRum-9: Multilingual Stance Classification over Rumours on Social Media

Yue Li, Jake Vasilakes, Zhixue Zhao +1

We introduce SCRum-9, the largest multilingual Stance Classification dataset for Rumour analysis in 9 languages, containing 7,516 tweets from X. SCRum-9 goes beyond existing stance…

cs.CL2025

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…