20 citations · 27 across the 9 of their papers we have counts for
12 papers
From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation
Paloma Piot, Javier Parapar
Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough annotations from multiple dem…
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media
Michele Joshua Maggini, Paloma Piot, Anxo Pérez +6
Detecting hyperpartisan narratives and Population Replacement Conspiracy Theories (PRCT) is essential to addressing the spread of misinformation. These complex narratives pose a si…
Can LLMs Evaluate What They Cannot Annotate? Revisiting LLM Reliability in Hate Speech Detection
Paloma Piot, David Otero, Patricia Martín-Rodilla +1
Hate speech spreads widely online, harming individuals and communities, making automatic detection essential for large-scale moderation, yet detecting it remains difficult. Part of…
Who Is the Story About? Protagonist Entity Recognition in News
Jorge Gabín, M. Eduardo Ares, Javier Parapar
News articles often reference numerous organizations, but traditional Named Entity Recognition (NER) treats all mentions equally, obscuring which entities genuinely drive the narra…
Bridging Gaps in Hate Speech Detection: Meta-Collections and Benchmarks for Low-Resource Iberian Languages
Paloma Piot, José Ramom Pichel Campos, Javier Parapar
Hate speech poses a serious threat to social cohesion and individual well-being, particularly on social media, where it spreads rapidly. While research on hate speech detection has…
WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data
Paloma Piot, Diego Sánchez, Javier Parapar
Online harms are a growing problem in digital spaces, putting user safety at risk and reducing trust in social media platforms. One of the most persistent forms of harm is hate spe…
Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection using Debias Tuning in Large Language Models
Paloma Piot, Patricia Martín-Rodilla, Javier Parapar
Commercial Large Language Models (LLMs) have recently incorporated memory features to deliver personalised responses. This memory retains details such as user demographics and indi…
Towards Efficient and Explainable Hate Speech Detection via Model Distillation
Paloma Piot, Javier Parapar
Automatic detection of hate and abusive language is essential to combat its online spread. Moreover, recognising and explaining hate speech serves to educate people about its negat…
Beyond Questions: Leveraging ColBERT for Keyphrase Search
Jorge Gabín, Javier Parapar, Craig Macdonald
While question-like queries are gaining popularity and search engines' users increasingly adopt them, keyphrase search has traditionally been the cornerstone of web search. This qu…
Decoding Hate: Exploring Language Models' Reactions to Hate Speech
Paloma Piot, Javier Parapar
Hate speech is a harmful form of online expression, often manifesting as derogatory posts. It is a significant risk in digital environments. With the rise of Large Language Models…
Enhancing Automatic Keyphrase Labelling with Text-to-Text Transfer Transformer (T5) Architecture: A Framework for Keyphrase Generation and Filtering
Jorge Gabín, M. Eduardo Ares, Javier Parapar
Automatic keyphrase labelling stands for the ability of models to retrieve words or short phrases that adequately describe documents' content. Previous work has put much effort int…
MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection
Paloma Piot, Patricia Martín-Rodilla, Javier Parapar
Hate speech represents a pervasive and detrimental form of online discourse, often manifested through an array of slurs, from hateful tweets to defamatory posts. As such speech pro…