Analyzed over 1 million posts from over 4,000 individuals, several social media platforms: Human behavior qualitatively and quantitatively conforms to the principles of reward learning (like rats)

Lindström, Björn, Martin Bellander, Allen Chang, Philippe N. Tobler, and David M. Amodio. 2019. “A Computational Reinforcement Learning Account of Social Media Engagement.” PsyArXiv. July 11. doi:10.31234/osf.io/78mh5

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.