Browsing by Author "De Villiers, Francois"
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- ItemConstructing topic-based Twitter lists(Stellenbosch : Stellenbosch University, 2013-03) De Villiers, Francois; Hoffmann, McElory R.; Kroon, R. S. (Steve); Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Computer Science.ENGLISH ABSTRACT: The amount of information that users of social networks consume on a daily basis is steadily increasing. The resulting information overload is usually associated with a loss of control over the management of information sources, leaving users feeling overwhelmed. To address this problem, social networks have introduced tools with which users can organise the people in their networks. However, these tools do not integrate any automated processing. Twitter has lists that can be used to organise people in the network into topic-based groups. This feature is a powerful organisation tool that has two main obstacles to widespread user adoption: the initial setup time and continual curation. In this thesis, we investigate the problem of constructing topic-based Twitter lists. We identify two subproblems, an unsupervised and supervised task, that need to be considered when tackling this problem. These subproblems correspond to a clustering and classification approach that we evaluate on Twitter data sets. The clustering approach is evaluated using multiple representation techniques, similarity measures and clustering algorithms. We show that it is possible to incorporate a Twitter user’s social graph data into the clustering approach to find topic-based clusters. The classification approach is implemented, from a statistical relational learning perspective, with kLog. We show that kLog can use a user’s tweet content and social graph data to perform accurate topic-based classification. We conclude that it is feasible to construct useful topic-based Twitter lists with either approach.