A study on the characteristics of douyin short videos and implications for edge caching
Open accessAbstract
Douyin, internationally known as TikTok, has become one of the most successful short-video platforms. To maintain its popularity, Douyin has to provide better Quality of Experience (QoE) to its growing user base. Understanding the characteristics of Douyin videos is thus critical to its service improvement and system design. In this paper, we present an initial study on the fundamental characteristics of Douyin videos based on a dataset of over 260 thousand short videos collected across three months. The characteristics of Douyin videos are found to be significantly different from traditional online videos, ranging from video bitrate, size, to popularity. In particular, the distributions of the bitrate and size of videos follow Weibull distribution. We further observe that the most popular Douyin videos follow Zifp's law on video popularity, but rest of the videos do not. We also investigate the correlation between popularity metrics used for Douyin videos. It is found that the correlation between the number of views and the number of likes are strong, while other correlations are relatively low. Finally, by using a case study, we demonstrate that the above findings can provide important guidance on designing an efficient edge caching system.
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Interactions with other users on TikTok mostly occur through user-generated video content that is highly personalized.
“Also, interactions with other users on TikTok mostly occur through user-generated video content being, again, highly personalized [33].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [33]
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- Authors
- Chen, Z., He, Q., Mao, Z., Chung, H.-M., & Maharjan, S.
- Affiliations
- Guilin University of Electronic Technology (CN)
- Norwegian University of Science and Technology (NO)
- University of Oslo (NO)
- Venue
- Proceedings of the ACM Turing Celebration Conference - China
- Publisher
- ACM
- Published
- 2019-05-17
- Pages
- 1-6
- OpenAlex
- W2964282041
- Cited by
- 94
- References
- 12
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 15 · 2023: 18 · 2022: 19 · 2021: 15 · 2020: 7 · 2019: 5
- FWCI
- 9.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Turing Celebration Conference - China (ACM TURC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61661015)
- Guilin University of Electronic Technology
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Douyin, TikTok, short-form video, edge caching, Weibull distribution, video popularity, quality of experience
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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