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A computational reinforcement learning account of social media engagement


Lindström, Björn; Bellander, Martin; Chang, Allen; Tobler, Philippe N; Amodio, David M (2019). A computational reinforcement learning account of social media engagement. PsyArXiv Preprints 78mh5, Society for the Improvement of Psychological Science.

Abstract

Social media has become the modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (“likes”), which turns the online world into a “Skinner Box” for the modern human. Yet despite such common portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. We applied a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyzed over one million posts from over 4,000 individuals on several social media platforms, using computational models based on reward reinforcement learning theory. Our results consistently show that human behavior on social media qualitatively and quantitatively conforms to the principles of reward learning. Results further reveal meaningful individual differences in social reward learning on social media, explained in part by variability in users’ tendency for social comparison. Together, these findings support the social reinforcement learning view of social media engagement and offer key new insights into this emergent mode of modern human behavior on an unprecedented scale.

Abstract

Social media has become the modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (“likes”), which turns the online world into a “Skinner Box” for the modern human. Yet despite such common portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. We applied a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyzed over one million posts from over 4,000 individuals on several social media platforms, using computational models based on reward reinforcement learning theory. Our results consistently show that human behavior on social media qualitatively and quantitatively conforms to the principles of reward learning. Results further reveal meaningful individual differences in social reward learning on social media, explained in part by variability in users’ tendency for social comparison. Together, these findings support the social reinforcement learning view of social media engagement and offer key new insights into this emergent mode of modern human behavior on an unprecedented scale.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Computational modeling, reinforcement learning, reward learning, social learning, social media
Language:English
Date:11 July 2019
Deposited On:10 Feb 2020 10:48
Last Modified:27 Mar 2020 14:55
Series Name:PsyArXiv Preprints
Number of Pages:26
OA Status:Green
Publisher DOI:https://doi.org/10.31234/osf.io/78mh5
Official URL:https://psyarxiv.com/78mh5/

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