title: Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation.
abstract: Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable. However, it is difficult and challenging to distinguish users, and to track the temporal development of collective attention within distinct user groups in Twitter. In this paper, we formulate this problem as tracking matrices decomposed by Nonnegative Matrix Factorisation for time-sequential matrix data, and propose a novel extension of Nonnegative Matrix Factorisation, which we refer to as Time Evolving Nonnegative Matrix Factorisation (TENMF). In our method, we describe users and words posted in some time interval by a matrix, and use several matrices as time-sequential data. Subsequently, we apply Time Evolving Nonnegative Matrix Factorisation to these time-sequential matrices. TENMF can decompose time-sequential matrices, and can track the connection among decomposed matrices, whereas previous NMF decomposes a matrix into two lower dimension matrices arbitrarily, which might lose the time-sequential connection. Our proposed method has an adequately good performance on artificial data. Moreover, we present several results and insights from experiments using real data from Twitter.
authors.affiliation: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors.forename: Shota
authors.initials: S
authors.lastname: Saito
authors.affiliation: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Hirata
authors.affiliation: Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan.
authors.forename: Kazutoshi
authors.initials: K
authors.lastname: Sasahara
authors.affiliation: Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
authors.forename: Hideyuki
authors.initials: H
authors.lastname: Suzuki
mesh_terms: D000465:Algorithms; D001288:Attention / Q000502:physiology*; D005075:Biological Evolution; D057218:Blogging / Q000706:statistics & numerical data*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors