New research digs into how TikTok’s algorithm is personalized and how users engage with TikTok based on its recommendations.
TikTok’s swift ascension to the upper echelons of social media is often attributed to its recommendation algorithm, which predicts viewer preferences so acutely it’s spawned a maxim: “The TikTok algorithm knows me better than I know myself.”
The platform’s success was so pronounced it’s seemed to spur other social media platforms to shift their designs. When users scroll through X or Instagram, they now see more recommended posts from accounts they don’t follow.
Yet for all that influence, the public knows little about how TikTok’s algorithm functions. So Franziska Roesner, a University of Washington associate professor in the Paul G. Allen School of Computer Science & Engineering, set about researching both how that algorithm is personalized and how TikTok users engage with the platform based on those recommendations.
Roesner and collaborators will present two papers this month that mine real-world data to help understand the “black box” of TikTok’s recommendation algorithm and its impact.
Researchers first recruited 347 TikTok users, who downloaded their data from the app and donated 9.2 million video recommendations. Using that data, the team initially looked at how TikTok personalized its recommendations. In the first 1,000 videos TikTok showed users, the team found that a third to half of the videos were shown based on TikTok’s predictions of what those users like. The researchers will publish the first paper in the Proceedings of the ACM Web Conference 2024.
The second study, which the team will present at the ACM CHI Conference on Human Factors in Computing Systems in Honolulu, explored engagement trends. Researchers discovered that over the users’ first 120 days, average daily time on the platform increased from about 29 minutes on the first day to 50 minutes on the last.
Here, Roesner explains how TikTok recommends videos; the impact that has on users; and the ways tech companies, regulators, and the public might mitigate unwanted effects:
Additional coauthors on the papers are from Boston University; the Max Planck Institute for Software Systems; the University of Haifa; TU Delft; and Georgetown University.
Source: University of Washington