Tracking online topics over time: Understanding dynamic hashtag communities
Djurdjevac Conrad, Nataša
Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings.
Online media , Hashtags , Temporal community detection , Random walk , Memory matching , Topic dynamics , Ranking , Aging model , Bursts
Lorenz-Spreen, P., Wolf, F., Braun, J., Ghoshal, G., Conrad, N.D. and Hövel, P., 2018. Tracking online topics over time: understanding dynamic hashtag communities. Computational social networks, 5(1), 9 (18pp.). DOI:10.1186/s40649-018-0058-6
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