Alexandria: A Library of Pluralistic Values for Realtime Re-Ranking of Social Media Feeds
Akaash Kolluri, Renn Su, Farnaz Jahanbakhsh, Dora Zhao, Tiziano Piccardi, Michael S. Bernstein

TL;DR
This paper introduces Alexandria, a library of 78 pluralistic values for social media feed re-ranking, implemented via LLM classifiers, enabling real-time, user-controlled feed customization to better align with diverse user needs.
Contribution
The paper presents a comprehensive library of social media values and a practical tool for real-time, user-driven feed re-ranking using LLM classifiers, addressing limitations of engagement-focused algorithms.
Findings
Diverse user needs benefit from a large, nuanced value library.
User control improves satisfaction and alignment with individual preferences.
The tool effectively operationalizes pluralistic values in real-time feed re-ranking.
Abstract
Social media feed ranking algorithms fail when they too narrowly focus on engagement as their objective. The literature has asserted a wide variety of values that these algorithms should account for as well -- ranging from well-being to productive discourse -- far more than can be encapsulated by a single topic or theory. In response, we present a for social media algorithms: a pluralistic set of 78 values as articulated across the literature, implemented into LLM-powered content classifiers that can be installed individually or in combination for real-time re-ranking of social media feeds. We investigate this approach by developing a browser extension, , that re-ranks the X/Twitter feed in real time based on the user's desired values. Through two user studies, both qualitative (N=12) and quantitative (N=257), we found that diverse user…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Complex Network Analysis Techniques · Social Media and Politics
