TY - GEN
T1 - Slapping Cats, Bopping Heads, and Oreo Shakes
T2 - 14th ACM Web Science Conference, WebSci 2022
AU - Ling, Chen
AU - Blackburn, Jeremy
AU - De Cristofaro, Emiliano
AU - Stringhini, Gianluca
N1 - Publisher Copyright: © 2022 ACM.
PY - 2022/6/26
Y1 - 2022/6/26
N2 - 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-à-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok's recommendation algorithm, and 3) the popularity of the video creator contributes 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.
AB - 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-à-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok's recommendation algorithm, and 3) the popularity of the video creator contributes 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.
KW - TikTok
KW - Twitter
KW - mix-methods
KW - short videos
KW - viral
UR - https://www.scopus.com/pages/publications/85133665308
U2 - 10.1145/3501247.3531551
DO - 10.1145/3501247.3531551
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 164
EP - 173
BT - WebSci 2022 - Proceedings of the 14th ACM Web Science Conference
PB - Association for Computing Machinery
Y2 - 26 June 2022 through 29 June 2022
ER -