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Social Network Analysis and Mining

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01.12.2013 | Original Article

On the impact of text similarity functions on hashtag recommendations in microblogging environments

verfasst von: Eva Zangerle, Wolfgang Gassler, Günther Specht

Erschienen in: Social Network Analysis and Mining | Ausgabe 4/2013

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Abstract

Microblogging applications such as Twitter are experiencing tremendous success. Microblog users utilize hashtags to categorize posted messages which aim at bringing order to the myriads of microblog messages. However, the percentage of messages incorporating hashtags is small and the used hashtags are very heterogeneous as hashtags may be chosen freely and may consist of any arbitrary combination of characters. This heterogeneity and the lack of use of hashtags lead to significant drawbacks in regards to the search functionality as messages are not categorized in a homogeneous way. In this paper, we present an approach for the recommendation of hashtags suitable for the message the user currently enters which aims at creating a more homogeneous set of hashtags. Furthermore, we present a detailed study on how the similarity measures used for the computation of recommendations influence the final set of recommended hashtags.

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According to http://business.twitter.com/en/basics/what-is-twitter/.

According to http://yearinreview.twitter.com/de/tps.html.

http://dev.twitter.com/docs/streaming-apis.

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Titel: On the impact of text similarity functions on hashtag recommendations in microblogging environments
verfasst von: Eva Zangerle
Wolfgang Gassler
Günther Specht
Publikationsdatum: 01.12.2013
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 4/2013
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-013-0108-x

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