Evaluating Content Exposure Bias in Social Networks
Pages 379 - 383
Abstract
Online social platforms employ personalized feed algorithms to gather and prioritize messages from accounts followed by users, which distorts content's perceived popularity prior to personalization. We call this "exposure bias," and our research focuses on quantifying it using diverse exposure bias metrics, and we evaluate recommendation algorithms through various content ranking heuristics. Similarly we simulate activity in a network to assess the influence of such ranking heuristics on exposure bias. Furthermore, we are working on agent-based model simulations to comprehend the impact of ranking schemes, with the ultimate goal of exploring intervention effects over time. Our empirical findings reveal that users exposed to popularity-based feeds experience significantly lower exposure bias compared to chronologically-ordered feeds.
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Index Terms
- Evaluating Content Exposure Bias in Social Networks
Index terms have been assigned to the content through auto-classification.
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Published: 15 March 2024
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ASONAM '23
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ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining
November 6 - 9, 2023
Kusadasi, Turkiye
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ASONAM '23 Paper Acceptance Rate 53 of 145 submissions, 37%;
Overall Acceptance Rate 116 of 549 submissions, 21%
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