Affect and prediction in short-video social media recommendation algorithm: TikTok and the missing half-second

This article examines the relationship between prediction and serendipity in the short-video social media platform TikTok, analyzing its recommendation algorithm through the lenses of affective and pragmatic turns in cognitive science. By looking at TikTok’s user experience, I demonstrate that while predictive models are crucial for user engagement, elements of surprise and unpredictability are equally essential for maintaining user interest. The study draws on theories of perception, emotion processing, and affect to provide a comprehensive understanding of the cognitive and embodied dimensions of digital social media experiences. I argue that TikTok’s success lies in its unique integration of both predictive accuracy and serendipitous discovery, creating an “indeterminacy center” that keeps users engaged. This research contributes to the broader understanding of social media dynamics, offering insights into the balance between prediction and serendipity in digital platforms.