Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Multiview clustering: a late fusion approach using latent models
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Mining topics on participations for community discovery
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Tadvise: a twitter assistant based on twitter lists
SocInfo'11 Proceedings of the Third international conference on Social informatics
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Twitter's list feature allows users to organize their followees into groups for easier information access and filtering. However, the percentage of users using lists is very small and most existing lists have only a few members. One reason for this may be that curating groups of Twitter users is a time consuming task. In this paper, we propose early and late fusion methods for automatically clustering followees using both graph structure and tweet content. We evaluate our approaches using ground-truth Twitter lists crawled via the Twitter API and show that the late fusion method outperforms both the baselines and the early fusion method.