Empirical Examination of Pet-Themed Short Video Popularity and Power-Law Patterns on Douyin
Abstract
Although short-video platforms disseminate content constantly, the resulting spread is far from uniform, since a limited set of videos rapidly attract disproportionate attention while the overwhelming majority receive only marginal engagement, a pattern that researchers commonly describe as a video 'going viral.' Because prior studies have largely relied on models or simulations to explain this phenomenon, empirical verification using real-world data remains comparatively rare. To address this gap, the present study examines fifty short video segments about animals that circulated on Douyin between July 26 and August 8, 2026, using likes and comments as indicators of dissemination. A log-log analysis was conducted to determine whether the extent of this spread conforms to a power-law distribution, in which a small number of elements account for most of the total while the majority contribute only marginally, and a Gini index was calculated to capture how concentrated that distribution is, alongside a comparison between video content category and dissemination intensity. The results reveal a pronounced long-tailed distribution: the power-law model fit the data closely, yielding an R² of 0.927, while the top 20% of videos accounted for 80% of all likes and the Gini index reached 0.745. Because footage from verified or prominent accounts attracted substantially more likes than that produced by ordinary content creators, these findings lend support to the Matthew effect as it operates within social media diffusion.
References
- Dong, J., Chen, B., Liu, L., Ai, C., Zhang, F., & Qiu, X. (2018). Impact of attractiveness and influence of information on cascade size distribution.Journal of System Simulation, 30(10), 3624–3631.
- Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf's law.Contemporary Physics, 46(5), 323–351.
- Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks.Science, 286(5439), 509–512.
- Li, F., & Wei, Y. (2020). Research on evolution model of online public opinion based on temporal network.Journal of System Simulation, 32(3), 394–403.
- Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data.SIAM Review, 51(4), 661–703.
- Yang, J., & Leskovec, J. (2011). Patterns of temporal variation in online media.Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 177–186.
- Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Everyone's an influencer: Quantifying influence on Twitter.Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 65–74.
- Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2016). The structural virality of online diffusion.Management Science, 62(1), 180–196.
- Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., & Hurst, M. (2007). Patterns of cascading behavior in large blog graphs.Proceedings of the 2007 SIAM International Conference on Data Mining, 551–556.
- Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online.Science, 359(6380), 1146–1151.