Event Detection in Twitter Microblogging.
IEEE Trans Cybern
; 46(12): 2810-2824, 2016 Dec.
Article
em En
| MEDLINE
| ID: mdl-26552100
ABSTRACT
The millions of tweets submitted daily overwhelm users who find it difficult to identify content of interest revealing the need for event detection algorithms in Twitter. Such algorithms are proposed in this paper covering both short (identifying what is currently happening) and long term periods (reviewing the most salient recently submitted events). For both scenarios, we propose fuzzy represented and timely evolved tweet-based theoretic information metrics to model Twitter dynamics. The Riemannian distance is also exploited with respect to words' signatures to minimize temporal effects due to submission delays. Events are detected through a multiassignment graph partitioning algorithm that 1) optimally retains maximum coherence within a cluster and 2) while allowing a word to belong to several clusters (events). Experimental results on real-life data demonstrate that our approach outperforms other methods.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
IEEE Trans Cybern
Ano de publicação:
2016
Tipo de documento:
Article