Title: Clustering user behaviour patterns on Twitter

 

Author: Christine Klotz; Coskun Akinalp; Herwig Unger

 

Addresses:
Faculty of Mathematics and Computer Science, FernUniversität in Hagen, Universitätsstraße 27, D-58084 Hagen, Germany
Faculty of Mathematics and Computer Science, FernUniversität in Hagen, Universitätsstraße 27, D-58084 Hagen, Germany
Faculty of Mathematics and Computer Science, FernUniversität in Hagen, Universitätsstraße 27, D-58084 Hagen, Germany

 

Journal: Int. J. of Social Network Mining, 2016 Vol.2, No.3, pp.203 - 223

 

Abstract: Personalised systems and targeted services must be tailored to the characteristics of the individual. Several categorisations for Twitter users exist, but all fail to account for the complexity of human beings. Behaviour is a key feature in detecting users with specific characteristics. This study demonstrates how to extract meaningful user behaviour patterns on large-scale datasets that reflect the personalities of human users. This is a first step to prediction of user action and the underlying individual decision-making process.

 

Keywords: Twitter; tweets; user characteristics; text mining; online social networks; OSN; user behaviour; language; word usage; cluster analysis; k-means clustering; user categorisation; behaviour patterns.

 

DOI: http://dx.doi.org/10.1504/IJSNM.2016.10003518

 

Available online 27 Feb 2017

 

 

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