A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Exploiting semantic hierarchies for Flickr group
AMT'10 Proceedings of the 6th international conference on Active media technology
From face-to-face gathering to social structure
Proceedings of the 21st ACM international conference on Information and knowledge management
Predicting User-to-content Links in Flickr Groups
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Box office prediction based on microblog
Expert Systems with Applications: An International Journal
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This paper develops a probabilistic framework that can model and predict group activity over time on online social media. Users of social media sites such as Flickr often face the enormous challenge of which group to choose, due to the presence of numerous competing groups of similar content. Determining an empirical measure of significance of a group can help tackle this problem. The proposed framework therefore determines an optimal measure per group based on past user participation and interaction as well as likely future activity in the group. The framework is tested on a Flickr dataset and the results show that this method can yield satisfactory predictions of group activity. This implies that the computed measure of significance of a group can be used by end users to choose groups with rich activity.