Tracking online topics over time: Understanding dynamic hashtag communities

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Date
2018-10-19
Authors
Lorenz-Spreen, Philipp
Wolf, Frederik
Braun, Jonas
Ghoshal, Gourab
Djurdjevac Conrad, Nataša
Hövel, Philipp
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Publisher
Springer Open
Published Version
10.1186/s40649-018-0058-6
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Abstract
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.
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Keywords
Online media , Hashtags , Temporal community detection , Random walk , Memory matching , Topic dynamics , Ranking , Aging model , Bursts
Citation
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
Link to publisher’s version
https://computationalsocialnetworks.springeropen.com/articles/10.1186/s40649-018-0058-6
URI
https://hdl.handle.net/10468/8657
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Mathematical Sciences- Journal Articles
Copyright
© The Author(s) 2018, This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.