RTs != Endorsements: Rethinking Exposure Fairness on Social Media Platforms

TL;DR

This paper challenges traditional exposure fairness notions in social media recommender systems, emphasizing the importance of social context and interactions over simple content exposure metrics.

Contribution

It introduces a new perspective on exposure fairness that incorporates social environment factors, highlighting the complexity of social media platforms.

Findings

Exposure frequency to others is as crucial as content exposure.

Social context significantly impacts fairness considerations.

Traditional fairness metrics may be insufficient for social media environments.

Abstract

Recommender systems underpin many of the personalized services in the online information & social media ecosystem. However, the assumptions in the research on content recommendations in domains like search, video, and music are often applied wholesale to domains that require a better understanding of why and how users interact with the systems. In this position paper we focus on social media and argue that personalized timelines have an added layer of complexity that is derived from the social nature of the platform itself. In particular, definitions of exposure fairness should be expanded to consider the social environment each user is situated in: how often a user is exposed to others is as important as who they get exposed to.