Value Alignment of Social Media Ranking Algorithms
This addresses the problem of value misalignment in social media algorithms for users, offering a method to personalize feeds based on human values, though it is incremental as it builds on existing value theory and ranking techniques.
The paper tackles the problem of social media feed rankings being driven by engagement signals that may prioritize individualistic values, by presenting an approach for value alignment using Schwartz's theory of Basic Human Values. The result shows that users can architect feeds reflecting their desired values, with value-ranked feeds aligning with personal values and diverging substantially from engagement-driven feeds in controlled experiments with N=141 and N=250 participants.
While social media feed rankings are primarily driven by engagement signals rather than any explicit value system, the resulting algorithmic feeds are not value-neutral: engagement may prioritize specific individualistic values. This paper presents an approach for social media feed value alignment. We adopt Schwartz's theory of Basic Human Values -- a broad set of human values that articulates complementary and opposing values forming the building blocks of many cultures -- and we implement an algorithmic approach that models and then ranks feeds by expressions of Schwartz's values in social media posts. Our approach enables controls where users can express weights on their desired values, combining these weights and post value expressions into a ranking that respects users' articulated trade-offs. Through controlled experiments (N=141 and N=250), we demonstrate that users can use these controls to architect feeds reflecting their desired values. Across users, value-ranked feeds align with personal values, diverging substantially from existing engagement-driven feeds.