Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok

In this talk, I will present my recent work at WebSci Companion'24 and ICWSM'25. We aim to study if one can effectively diagnose behavioral addiction using digital data traces from social media platforms. Focusing on the TikTok short-format video platform as a case study, we employ a novel mixed methodology of combining survey responses with data donations of behavioral traces. We survey 1590 TikTok users and stratify them into three addiction groups (i.e., less/moderately/highly likely addicted). Then, we obtain data donations from 107 surveyed participants. By analyzing users' data we find that, among others, highly likely addicted users spend more time watching TikTok videos and keep coming back to TikTok throughout the day, indicating a compulsion to use the platform. Finally, by using basic user engagement features, we train classifier models to identify highly likely addicted users with F1 >= 0.55. The performance of the classifier models suggests predicting addictive users solely based on their usage is rather difficult.