2 citations · 3 across the 6 of their papers we have counts for
6 papers
Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection
Myrthe Reuver, Indira Sen, Matteo Melis +1
This paper investigates hybrid intelligence and collaboration between researchers of sexism and Large Language Models (LLMs), with a four-component pipeline. First, nine sexism res…
A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debates
Paulina Garcia Corral, Avishai Green, Hendrik Meyer +3
The climate crisis is a salient issue in online discussions, and hypocrisy accusations are a central rhetorical element in these debates. However, for large-scale text analysis, hy…
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
Myrthe Reuver, Suzan Verberne, Antske Fokkens
For a viewpoint-diverse news recommender, identifying whether two news articles express the same viewpoint is essential. One way to determine "same or different" viewpoint is stanc…
Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains
Alessandra Polimeno, Myrthe Reuver, Sanne Vrijenhoek +1
News recommender systems play an increasingly influential role in shaping information access within democratic societies. However, tailoring recommendations to users' specific inte…
Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction
Michiel van der Meer, Myrthe Reuver, Urja Khurana +2
This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022). Our approach uses Large Language Models for the task of Argument Quality Pr…
Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study
Myrthe Reuver, Suzan Verberne, Roser Morante +1
Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance…