Scale-dependent power law properties in hashtag usage time series of Weibo.

Autor: Jiang JJ; School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502, Japan., Yamada K; Faculty of Global and Regional Studies, University of the Ryukyus, Nishihara, Okinawa, 903-0213, Japan., Takayasu H; School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502, Japan.; Sony Computer Science Laboratories, 3-14-13 Higashi-Gotanda, Shinagawa-ku, Tokyo, 141-0022, Japan., Takayasu M; School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502, Japan. takayasu.m.aa@m.titech.ac.jp.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Dec 15; Vol. 13 (1), pp. 22298. Date of Electronic Publication: 2023 Dec 15.
DOI: 10.1038/s41598-023-49572-6
Abstrakt: We analyze the time series of hashtag numbers of social media data. We observe that the usage distribution of hashtags is characterized by a fat-tailed distribution with a size-dependent power law exponent and we find that there is a clear dependency between the growth rate distributions of hashtags and size of hashtags usage. We propose a generalized random multiplicative process model with a theory that explains the size dependency of the fat-tailed distribution. Numerical simulations show that our model reproduces these size-dependent properties nicely. We expect that our model is useful for understanding the mechanism of fat-tailed distributions in various fields of science and technology.
(© 2023. The Author(s).)
Databáze: MEDLINE
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