2020/02/22 by Peter Sheridan Dodds, Dodds, P. S., Joshua R. Minot +17 · 5 citations
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Data Analysis #FOS: Physical sciences #Misinformation and Its Impacts #Physics and Society (physics.soc-ph) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2002.09770
openalex publication_date 2020/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce `allotaxonometry' along with `rank-turbulence divergence' (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of `type calculus', for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.