Imagine you’re at a party where everyone shares interesting stories and juicy gossip. However, there’s a DJ controlling the playlist. They instinctively know your preferences and play the songs that amplify your excitement, making you tap your feet and sing along. This is similar to algorithm-mediated social learning on online platforms like Twitter and TikTok. Algorithms use our inherent biases to curate content that grabs our attention and keeps us engaged. But this comes with consequences. By promoting prestigious, ingroup, moral, and emotional information (PRIME), algorithms inadvertently distort our social perceptions and lead to conflicts and spreading of misinformation. Researchers propose solutions like diversifying algorithmic recommendations and increasing transparency to address these issues. It’s time to dive deeper into the fascinating world of algorithm-mediated social learning!

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or ‘PRIME’ information) to sustain users’ attention and maximize engagement. Here, we synthesize emerging insights into ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human–algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

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