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Social Network Analysis and Mining

Erschienen in:

01.12.2015 | Original Article

Language agnostic meme-filtering for hashtag-based social network analysis

verfasst von: Dimitrios Kotsakos, Panos Sakkos, Ioannis Katakis, Dimitrios Gunopulos

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2015

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Abstract

Users in social networks utilize hashtags for a variety of reasons. In many cases, hashtags serve retrieval purposes by labeling the content they accompany. More often than not, hashtags are used to promote content, ideas, or conversations producing viral memes. This paper addresses a specific case of hashtag classification: meme-filtering. We argue that hashtags that are correlated with memes may hinder many valuable social media algorithms like trend detection and event identification. We propose and evaluate a set of language-agnostic features that aid the separation of these two classes: meme-hashtags and event-hashtags. The proposed approach is evaluated on two large datasets of Twitter messages written in English and German. A proof-of-concept application of the meme-filtering approach to the problem of event detection is presented.

Vorheriger Artikel Social ties, homophily and extraversion--introversion to generate complex networks

Nächster Artikel A comprehensive comparison of graph theory metrics for social networks

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Obviously, there are exception to this rule. In our times, information about earthquakes appears on social media first. However, this is still related to a real-world event.

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Titel: Language agnostic meme-filtering for hashtag-based social network analysis
verfasst von: Dimitrios Kotsakos
Panos Sakkos
Ioannis Katakis
Dimitrios Gunopulos
Publikationsdatum: 01.12.2015
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2015
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-015-0271-3

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