Tracking online topics over time: Understanding dynamic hashtag communities
dc.contributor.author | Lorenz-Spreen, Philipp | |
dc.contributor.author | Wolf, Frederik | |
dc.contributor.author | Braun, Jonas | |
dc.contributor.author | Ghoshal, Gourab | |
dc.contributor.author | Djurdjevac Conrad, Nataša | |
dc.contributor.author | Hövel, Philipp | |
dc.date.accessioned | 2019-10-02T04:36:01Z | |
dc.date.available | 2019-10-02T04:36:01Z | |
dc.date.issued | 2018-10-19 | |
dc.description.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. | en |
dc.description.status | Peer reviewed | en |
dc.description.version | Published Version | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.articleid | 9 | en |
dc.identifier.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 | en |
dc.identifier.doi | 10.1186/s40649-018-0058-6 | en |
dc.identifier.eissn | 2197-4314 | |
dc.identifier.endpage | 18 | en |
dc.identifier.issued | 1 | en |
dc.identifier.journaltitle | Computational Social Networks | en |
dc.identifier.startpage | 1 | en |
dc.identifier.uri | https://hdl.handle.net/10468/8657 | |
dc.identifier.volume | 5 | en |
dc.language.iso | en | en |
dc.publisher | Springer Open | en |
dc.relation.uri | https://computationalsocialnetworks.springeropen.com/articles/10.1186/s40649-018-0058-6 | |
dc.rights | © 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. | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | Online media | en |
dc.subject | Hashtags | en |
dc.subject | Temporal community detection | en |
dc.subject | Random walk | en |
dc.subject | Memory matching | en |
dc.subject | Topic dynamics | en |
dc.subject | Ranking | en |
dc.subject | Aging model | en |
dc.subject | Bursts | en |
dc.title | Tracking online topics over time: Understanding dynamic hashtag communities | en |
dc.type | Article (peer-reviewed) | en |