Slapping Cats, Bopping Heads, and Oreo Shakes: Understanding Indicators of Virality in TikTok Short Videos

TL;DR

This study investigates the elements contributing to virality in TikTok videos, analyzing content, creator popularity, and platform algorithms to identify key features that drive widespread engagement.

Contribution

The paper introduces a mixed-method approach and a labeled dataset to identify and analyze features influencing TikTok video virality, highlighting the importance of creator followers and video characteristics.

Findings

Number of followers is the strongest predictor of virality.

Close-up and medium-shot scales significantly impact virality.

Video lifespan, presence of text, and point of view influence virality.

Abstract

Short videos have become one of the leading media used by younger generations to express themselves online and thus a driving force in shaping online culture. In this context, TikTok has emerged as a platform where viral videos are often posted first. In this paper, we study what elements of short videos posted on TikTok contribute to their virality. We apply a mixed-method approach to develop a codebook and identify important virality features. We do so vis-\`a-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok's recommendation algorithm, and 3) the popularity of the video creator contribute to virality. We collect and label a dataset of 400 TikTok videos and train classifiers to help us identify the features that influence virality the most. While the number of followers is the most powerful predictor, close-up and medium-shot scales also play an essential role. So does the lifespan of the video, the presence of text, and the point of view. Our research highlights the characteristics that distinguish viral from non-viral TikTok videos, laying the groundwork for developing additional approaches to create more engaging online content and proactively identify possibly risky content that is likely to reach a large audience.