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

TL;DR

This study combines surveys and digital traces from TikTok to diagnose behavioral addiction, revealing usage patterns and developing classifiers with moderate success, thus advancing understanding of social media addiction risks.

Contribution

It introduces a novel mixed methodology combining survey data with behavioral traces to identify TikTok addiction, which is underexplored in current research.

Findings

Highly addicted users spend more time on TikTok and return frequently.

Classifiers can identify addicted users with moderate accuracy (F1 ≥ 0.55).

Predicting addiction solely from usage data remains challenging.

Abstract

Opaque algorithms disseminate and mediate the content that users consume on online social media platforms. This algorithmic mediation serves users with contents of their liking, on the other hand, it may cause several inadvertent risks to society at scale. While some of these risks, e.g., filter bubbles or dissemination of hateful content, are well studied in the community, behavioral addiction, designated by the Digital Services Act (DSA) as a potential systemic risk, has been understudied. In this work, 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 $F_1 \geq 0.55$. The performance of the classifier models suggests predicting addictive users solely based on their usage is rather difficult.