Regensburg 2025 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 4: Urban systems, Scaling, and Social Systems
SOE 4.3: Talk
Tuesday, March 18, 2025, 10:15–10:30, H45
Scale-dependent Power Law Properties in Social Activities — •Kenta Yamada1, Jiwei Jiang2, Hideki Takayasu2, and Misako Takayasu2 — 1Univ. of the Ryukyus, Okinawa, Japan — 2Science Tokyo, Tokyo, Japan
This presentation explores the power-law characteristics of hashtag usage on Weibo, a Chinese social media platform. The study investigates the heavy-tailed distribution of daily hashtag frequencies and proposes a generalized random multiplicative model to understand the formation of these distributions[1].
Data containing approximately 20 million Weibo posts from July to August 2021 were analyzed. The analysis confirmed that hashtag frequency distributions follow a fat-tailed pattern, consistent with previous research[2]. A key finding was that the growth rate of hashtag usage depends on its frequency.
To model this, a generalized random multiplicative process incorporating size dependency was introduced. Simulations demonstrated that increasing granularity in dividing the hashtag frequency range improved the model's accuracy in replicating real distributions. The power-law exponents estimated through theoretical methods aligned closely with observed data.
[1] J. J. Jiang, K. Yamada, H. Takayasu, and M. Takayasu, Sci Rep 13, 1 (2023).
[2] Chen, H. H., Alexander, T. J., Oliveira, D. F., & Altmann, E. G. . Chaos, 30(6), 063112 (2020).
Keywords: Weibo; Hashtag; Modeling; Stochastic Process; Theoretical Analysis