2020/04/27 by Hongjia H. Chen, Tristram J. Alexander, Diego F. M. Oliveira +1 · 12 citations
Computer Science · Physics and Astronomy · #Authorship Attribution and Profiling #Complex Network Analysis Techniques #Distribution (mathematics) #Divergence (linguistics) #Dynamics (music) #Function (biology) #Opinion Dynamics and Social Influence #Sample (material) #Scaling #Scaling law #Social media #Underpinning #cs.SI #physics.soc-ph
paper · pdf · doi:10.1063/5.0004983
published in Chaos An Interdisciplinary Journal of Nonlinear Science 30(6), 063112 (American Institute of Physics) · 8 pages and 4 figures. Submitted to the journal "Chaos", special edition on "Dynamics of Social Systems". Data available at https://zenodo.org/record/3673744#.Xqa5t_GhSv4
arxiv created 2020/04/27 · openalex created_date 2020/05/01 · openalex publication_date 2020/06/01 · arxiv updated 2020/06/04 · openalex updated_date 2026/08/05
In this paper, we quantify the statistical properties and dynamics of the frequency of hashtag use on Twitter. Hashtags are special words used in social media to attract attention and to organize content. Looking at the collection of all hashtags used in a period of time, we identify the scaling laws underpinning the hashtag frequency distribution (Zipf’s law), the number of unique hashtags as a function of sample size (Heaps’ law), and the fluctuations around expected values (Taylor’s law). While these scaling laws appear to be universal, in the sense that similar exponents are observed irrespective of when the sample is gathered, the volume and the nature of the hashtags depend strongly on time, with the appearance of bursts at the minute scale, fat-tailed noise, and long-range correlations. We quantify this dynamics by computing the Jensen–Shannon divergence between hashtag distributions obtained τ times apart and we find that the speed of change decays roughly as 1/τ. Our findings are based on the analysis of 3.5×109 hashtags used between 2015 and 2016.