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Found 4 papers, 2 papers with code
HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
HearHere facilitates the key processes of news information consumption through two visualizations.
CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem.
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2).
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance.
