Papers by Christine de Kock
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)
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Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
| Challenge: | Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages. |
| Approach: | They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets. |
| Outcome: | The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. |
Inducing lexicons of in-group language with socio-temporal context (2025.acl-long)
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| Challenge: | Existing methods for lexicon induction do not capture the evolving nature of in-group language, nor the social structure of the community. |
| Approach: | They propose a method for inducing lexicons of in-group language which incorporates its socio-temporal context. |
| Outcome: | The proposed method outperforms existing methods for lexicon induction . it quantifies relevance of each term to a specific sub-community at a given point in time . |
Detecting Sockpuppetry on Wikipedia Using Meta-Learning (2025.acl-long)
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| Challenge: | Existing approaches to model author-specific sockpuppet detection on Wikipedia are limited in data-scarce settings. |
| Approach: | They propose to use meta-learning to improve model adaptation to a new sockpuppet-group by training models across multiple tasks. |
| Outcome: | The proposed technique improves performance in data-scarce settings by training models across multiple tasks. |
Human Interest Framing across Cultures: A Case Study on Climate Change (2025.coling-main)
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| Challenge: | 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. |
| Approach: | They perform a systematic analysis of HI stories to understand its role in climate change reporting in English-speaking countries from four continents. |
| Outcome: | The proposed approach has shown to capture and retain readership and enhance political engagement of the population. |
IYKYK: Using language models to decode extremist cryptolects (2026.eacl-long)
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Christine de Kock, Arij Riabi, Zeerak Talat, Michael Sejr Schlichtkrull, Pranava Madhyastha, Eduard Hovy
| Challenge: | Extremist groups develop complex in-group language to exclude or mislead outsiders . general purpose LLMs cannot consistently detect or decode extremist language . |
| Approach: | They evaluate the ability of current language technologies to detect and interpret the cryptolects of two online extremist platforms. |
| Outcome: | The proposed models can detect and interpret extremist language better than current models. |
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information (2025.acl-long)
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| Challenge: | Current methods for cross-domain misinformation detection focus on in-domain tasks and do not incorporate significant sentiment and emotion features. |
| Approach: | They propose a retrieval augmented (RAG) LLM framework that incorporates affective information into retrieval databases. |
| Outcome: | The proposed framework improves on three misinformation benchmarks. |