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Individual Fairness for Social Media Influencers


Ionescu, Stefania; Pagan, Nicolo; Hannak, Aniko (2023). Individual Fairness for Social Media Influencers. In: Hocine, Cherifi; Rosario Nunzio, Mantegna; Luis M, Rocha; Chantal, Cherifi; Salvatore, Miccichè. Complex Networks and Their Applications XI. Cham: Springer, 162-175.

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.

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.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Scope:Discipline-based scholarship (basic research)
Language:English
Date:4 January 2023
Deposited On:16 Nov 2023 14:29
Last Modified:29 Apr 2024 01:41
Publisher:Springer
Series Name:Studies in Computational Intelligence
Number:1077
ISSN:1860-949X
ISBN:9783031211263
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-031-21127-0_14
Other Identification Number:merlin-id:24165
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