Christine de Kock — accepted AI-conference papers

2025

BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages

Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif +42

ACL 2025long

People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition–an umbrella term for several NLP tasks–impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

Detecting Sockpuppetry on Wikipedia Using Meta-Learning

Luc Raszewski, Christine de Kock

ACL 2025long

Malicious sockpuppet detection on Wikipedia is critical to preserving access to reliable information on the internet and preventing the spread of disinformation. Prior machine learning approaches rely on stylistic and meta-data features, but do not prioritise adaptability to author-specific behaviou…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

Human Interest Framing across Cultures: A Case Study on Climate Change

Gisela Vallejo, Christine de Kock, Timothy Baldwin, Lea Frermann

COLING 2025main

Human Interest (HI) framing is a narrative strategy that injects news stories with a relatable, emotional angle and a human face to engage the audience. In this study we investigate the use of HI framing across different English-speaking cultures in news articles about climate change. Despite its de…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

Inducing lexicons of in-group language with socio-temporal context

Christine de Kock

ACL 2025long

In-group language is an important signifier of group dynamics. This paper proposes a novel method for inducing lexicons of in-group language, which incorporates its socio-temporal context. Existing methods for lexicon induction do not capture the evolving nature of in-group language, nor the social…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information

Zhiwei Liu, Kailai Yang, Qianqian Xie, Christine de Kock, Sophia Ananiadou, Eduard Hovy

ACL 2025long

Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformation detection rely on effort- and resource-intensive fine-tuning and complex model structures. With the outstanding perfo…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2022

How to disagree well: Investigating the dispute tactics used on Wikipedia

Christine De Kock, Tom Stafford, Andreas Vlachos

EMNLP 2022main

Disagreements are frequently studied from the perspective of either detecting toxicity or analysing argument structure. We propose a framework of dispute tactics which unifies these two perspectives, as well as other dialogue acts which play a role in resolving disputes, such as asking questions and…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗

2022

Leveraging Wikipedia article evolution for promotional tone detection

Christine De Kock, Andreas Vlachos

ACL 2022long

Detecting biased language is useful for a variety of applications, such as identifying hyperpartisan news sources or flagging one-sided rhetoric. In this work we introduce WikiEvolve, a dataset for document-level promotional tone detection. Unlike previously proposed datasets, WikiEvolve contains se…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗