Will You Dance To The Challenge? Predicting User Participation of TikTok Challenges
It addresses the challenge of social contagion prediction on TikTok, an incremental improvement for social media analytics.
This paper tackles the problem of predicting user participation in TikTok challenges by proposing a deep learning model, deepChallenger, which learns latent user and challenge representations from past videos; it achieves an F1 score of 0.494, significantly outperforming baselines with an F1 of 0.188.
TikTok is a popular new social media, where users express themselves through short video clips. A common form of interaction on the platform is participating in "challenges", which are songs and dances for users to iterate upon. Challenge contagion can be measured through replication reach, i.e., users uploading videos of their participation in the challenges. The uniqueness of the TikTok platform where both challenge content and user preferences are evolving requires the combination of challenge and user representation. This paper investigates social contagion of TikTok challenges through predicting a user's participation. We propose a novel deep learning model, deepChallenger, to learn and combine latent user and challenge representations from past videos to perform this user-challenge prediction task. We collect a dataset of over 7,000 videos from 12 trending challenges on the ForYouPage, the app's landing page, and over 10,000 videos from 1303 users. Extensive experiments are conducted and the results show that our proposed deepChallenger (F1=0.494) outperforms baselines (F1=0.188) in the prediction task.