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Found 6 papers, 1 papers with code
SoMeR: Multi-View User Representation Learning for Social Media
User representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations.
Whose Emotions and Moral Sentiments Do Language Models Reflect?
We define the problem of affective alignment, which measures how LMs' emotional and moral tone represents those of different groups.
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities.
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Social media platforms are rife with politically charged discussions.
A Data Fusion Framework for Multi-Domain Morality Learning
Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life.
Inherent Trade-offs in the Fair Allocation of Treatments
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups.
