A computational reward learning account of social media engagement

Björn Lindström (), Martin Bellander, David T. Schultner, Allen Chang, Philippe N. Tobler and David M. Amodio
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Björn Lindström: University of Amsterdam
Martin Bellander: Karolinska Institutet
David T. Schultner: University of Amsterdam
Allen Chang: Boston University
Philippe N. Tobler: University of Zürich
David M. Amodio: University of Amsterdam

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Social media has become a 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), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

Date: 2021
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DOI: 10.1038/s41467-020-19607-x

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