Will You Dance To The Challenge? Predicting User Participation of TikTok Challenges

TL;DR

This paper introduces deepChallenger, a deep learning model that predicts user participation in TikTok challenges by learning combined representations of users and challenges, demonstrating superior performance over baselines.

Contribution

The paper presents a novel deep learning approach that integrates user and challenge representations for predicting participation in TikTok challenges.

Findings

deepChallenger achieves an F1 score of 0.494

outperforms baseline models with an F1 score of 0.188

demonstrates effectiveness on a dataset of over 7,000 videos

Abstract

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.