6 papers · 1 filter
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
Leonardo Castro-Gonzalez, Yi-Ling Chung, Hannak Rose Kirk +4
The field of machine learning has recently made significant progress in reducing the requirements for labelled training data when building new models. These `cheaper' learning tech…
Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
Mikel K. Ngueajio, Flor Miriam Plaza-del-Arco, Yi-Ling Chung +2
Automated counter-narratives (CN) offer a promising strategy for mitigating online hate speech, yet concerns about their affective tone, accessibility, and ethical risks remain. We…
Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking
Yi-Ling Chung, Aurora Cobo, Pablo Serna
Robust automatic fact-checking systems have the potential to combat online misinformation at scale. However, most existing research primarily focuses on English. In this paper, we…
DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures
Angus R. Williams, Hannah Rose Kirk, Liam Burke +6
Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but la…
NLP for Counterspeech against Hate: A Survey and How-To Guide
Helena Bonaldi, Yi-Ling Chung, Gavin Abercrombie +1
In recent years, counterspeech has emerged as one of the most promising strategies to fight online hate. These non-escalatory responses tackle online abuse while preserving the fre…
Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation
Jaione Bengoetxea, Yi-Ling Chung, Marco Guerini +1
Counter Narratives (CNs) are non-negative textual responses to Hate Speech (HS) aiming at defusing online hatred and mitigating its spreading across media. Despite the recent incre…