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Title: User information modelling in social communities and networks
Author: Yang, Baoguo
ISNI:       0000 0001 2437 6254
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2015
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User modelling is the basis for social network analysis, such as community detection, expert finding, etc. The aim of this research is to model user information including user-generated content and social ties. There have been many algorithms for community detection. However, the existing algorithms consider little about the rich hidden knowledge within communities of social networks. In this research, we propose to simultaneously discover communities and the hidden/latent knowledge within them. We focus on jointly modelling communities, user sentiment topics, and the social links. We also learn to recommend experts to the askers based on the newly posted questions in online question answering communities. Specifically, we first propose a new probabilistic model to depict users' expertise based on answers and their descriptive ability based on questions. To exploit social information in community question answering (CQA), the link analysis is also considered. We also propose a user expertise model under tags rather than the general topics. In CQA sites, it is very common that some users share the same user names. Once an ambiguous user name is recommended, it is difficult for the asker to find out the target user directly from the large scale CQA site. We propose a simple but effective method to disambiguate user names by ranking their tag-based relevance to a query question. We evaluate the proposed models and methods on real world datasets. For community discovery, our models can not only identify communities with different topic-sentiment distributions, but also achieve comparable performance. With respect to the expert recommendation in CQA, the unified modelling of user topics/tags and abilities are capable of improving the recommendation performance. Moreover, as for the user name disambiguation in CQA, the proposed method can help question askers match the ambiguous user names with the right people with high accuracy.
Supervisor: Manandhar, Suresh Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available