Authors: Mengjiao Wang; Donn Morrison; Conor Hayes
Addresses: Digital Enterprise Research Institute IDA Business Park, Co. Galway, Ireland ' Digital Enterprise Research Institute IDA Business Park, Co. Galway, Ireland ' Digital Enterprise Research Institute, IDA Business Park, Co. Galway, Ireland
Abstract: Many microblogging services provide tools that allow users to organise the people they follow into groups for easier information access and filtering. However, the uptake of these tools is low with a likely reason being that curating groups of followees is a time consuming task. This paper proposes methods for automatically clustering followees into groups so that users can use these groups as their user lists. As social microblogging services contain both textual content posted by users and directed followee relationships between users, members in the same list usually share common interests and/or have dense followee relationships. Under this assumption, this paper first applies separate content- and graph-based methods to cluster users. Next, we propose several novel information fusion configurations that combine textual and network features. We evaluate these approaches using both an offline evaluation and a user evaluation on datasets crawled using the Twitter API.
Keywords: document clustering; community detection; early fusion; late fusion; information fusion; automatic list creation; Twitter lists; user lists; social microblogging; user clusters.
International Journal of Social Network Mining, 2015 Vol.2 No.1, pp.19 - 43
Received: 11 May 2013
Accepted: 06 Jun 2014
Published online: 11 Jun 2015 *