#TulsaFlop: A Case Study of Algorithmically-Influenced Collective Action on TikTok

TL;DR

This study investigates how TikTok's recommender algorithm amplifies sociopolitical call-to-action videos, influencing collective behavior and highlighting algorithmic impacts on political mobilization.

Contribution

It provides empirical evidence of algorithmic amplification of sociopolitical content on TikTok and analyzes its implications for collective action and political mobilization.

Findings

Call-to-action videos received significantly more views than other videos by the same users.

Amplification is driven by increased engagement, not systematic algorithm bias.

Some videos achieved over 2 million plays despite users having fewer followers.

Abstract

When a re-election rally for the U.S. president drew smaller crowds than expected in Tulsa, Oklahoma, many people attributed the low turnout to collective action organized by TikTok users. Motivated by TikTok's surge in popularity and its growing sociopolitical implications, this work explores the role of TikTok's recommender algorithm in amplifying call-to-action videos that promoted collective action against the Tulsa rally. We analyze call-to-action videos from more than 600 TikTok users and compare the visibility (i.e. play count) of these videos with other videos published by the same users. Evidence suggests that Tulsa-related videos generally received more plays, and in some cases the amplification was dramatic. For example, one user's call-to-action video was played over 2 million times, but no other video by the user exceeded 100,000 plays, and the user had fewer than 20,000 followers. Statistical modeling suggests that the increased play count is explained by increased engagement rather than any systematic amplification of call-to-action videos. We conclude by discussing the implications of recommender algorithms amplifying sociopolitical messages, and motivate several promising areas for future work.