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13.03.2017

Hashtag-based topic evolution in social media

verfasst von: Md. Hijbul Alam, Woo-Jong Ryu, SangKeun Lee

Erschienen in: World Wide Web | Ausgabe 6/2017

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Abstract

The rise of online social media has led to an explosion of metadata-containing user generated content. The tracking of metadata distribution is essential to understand social media. This paper presents two statistical models that detect interpretable topics over time along with their hashtags distribution. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags, i.e., the hashtag distribution. The models combine a context with a related topic by jointly modeling words with hashtags and time. Experiments with real-world datasets demonstrate that the proposed models discover topics over time with related contexts effectively.

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Titel: Hashtag-based topic evolution in social media
verfasst von: Md. Hijbul Alam
Woo-Jong Ryu
SangKeun Lee
Publikationsdatum: 13.03.2017
Verlag: Springer US
Erschienen in: World Wide Web / Ausgabe 6/2017
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-017-0451-3

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