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Found 8 papers, 1 papers with code
Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study
Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context.
Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation.
No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems
Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions.
Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)
Diversity in news recommendation is important for democratic debate.
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics.
Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains
News recommender systems play an increasingly influential role in shaping information access within democratic societies.
Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction
This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022).
Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study
Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics.
