Papers by Javier Parapar
Semantic Similarity Models for Depression Severity Estimation (2023.emnlp-main)
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| Challenge: | Public health systems have limited capacity for case detection and diagnosis due to the widespread use of social media. |
| Approach: | They propose to use social media content to generate semantic rankings for depressive symptoms and severity levels and use them to predict symptoms severity. |
| Outcome: | The proposed pipeline improves on two Reddit-based benchmarks and shows that it is more efficient than state-of-the-art in terms of measuring depression level. |
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media (2026.eacl-long)
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Michele Joshua Maggini, Paloma Piot, Anxo Pérez, Erik Bran Marino, Lúa Santamaría Montesinos, Ana Lisboa Cotovio, Marta Vázquez Abuín, Javier Parapar, Pablo Gamallo
| Challenge: | Existing methods for detecting hyperpartisan narratives and PRCTs are limited . hyperpartisan content promotes extreme views through one-sided, emotional language . |
| Approach: | They propose a multilingual dataset of 1617 hyperpartisan news headlines in Spanish, Italian, and Portuguese annotated in multiple political discourse aspects. |
| Outcome: | The proposed dataset is the first multilingual dataset of 1617 hyperpartisan headlines in Spanish, Italian, and Portuguese. |
Decoding Hate: Exploring Language Models’ Reactions to Hate Speech (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) are trained on vast amounts of unmoderated internet data, enabling them to generate text autonomously. |
| Approach: | They investigate the responses of seven state-of-the-art Large Language Models (LLMs) to hate speech by qualitative analysis. |
| Outcome: | The proposed models can handle hate speech inputs and mitigate it through fine-tuning and guideline guardrailing. |
Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
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| Challenge: | Existing discourse parsers do not generalize well across genres and text types. |
| Approach: | They propose to integrate large language models into RST discourse parsers to improve parser performance in a social media context. |
| Outcome: | The proposed model improves parser performance in a social media context without pre-identified discourse units. |