Regensburg 2019 – wissenschaftliches Programm
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 2: Computational Social Science and Data Science I
SOE 2.2: Vortrag
Montag, 1. April 2019, 10:00–10:15, H17
Modeling the rise and fall of online topics — •Frederik Wolf1,2, Philipp Lorenz-Spreen3, Natasa Conrad4, and Philipp Hövel3,5 — 1Potsdam Institute for Climate Impact Research, Research Domain 4 — 2HU Berlin, Department of Physics — 3TU Berlin, Department of Physics — 4Zuse Institute, Berlin — 5University College Cork, School of Mathematical Sciences
Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. In this study, we split a weighted temporal network constructed from hashtag co-occurrences into static snaphots and utilize a higher order memory approach to produce a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection of each snapshot [Lorenz, Philipp, et al. "Capturing the Dynamics of Hashtag-Communities." Int. Wo. Compl. Netw. Appl., 2017].
The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. We consider the size of the communities in time as a proxy for online popularity and find that the gains and losses, as well as the interevent times are broadly distributed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. The interplay of these mechanisms causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.