Individual Fairness for Social Media Influencers

TL;DR

This paper examines how different recommendation strategies impact fairness among social media content creators, showing that exploration can promote fairness but has limitations, especially for mid-quality creators.

Contribution

It extends prior work by analyzing fairness beyond averages and highlighting the role of exploration in achieving fair outcomes in social media networks.

Findings

Non-exploratory recommendations lead to quick but unfair convergence.

Exploration can improve fairness for top and bottom quality creators.

Fair outcomes are not guaranteed for mid-quality creators even with exploration.

Abstract

Nowadays, many social media platforms are centered around content creators (CC). On these platforms, the tie formation process depends on two factors: (a) the exposure of users to CCs (decided by, e.g., a recommender system), and (b) the following decision-making process of users. Recent research studies underlined the importance of content quality by showing that under exploratory recommendation strategies, the network eventually converges to a state where the higher the quality of the CC, the higher their expected number of followers. In this paper, we extend prior work by (a) looking beyond averages to assess the fairness of the process and (b) investigating the importance of exploratory recommendations for achieving fair outcomes. Using an analytical approach, we show that non-exploratory recommendations converge fast but usually lead to unfair outcomes. Moreover, even with exploration, we are only guaranteed fair outcomes for the highest (and lowest) quality CCs.