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Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Discovering Temporal Change Patterns in the Presence of Taxonomies
IEEE Transactions on Knowledge and Data Engineering
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The increasing availability of user-generated content coming from online communities allows the analysis of common user behaviors and trends in social network usage. This paper presents the TweM Tweet Miner framework that entails the discovery of hidden and high level correlations, in the form of generalized association rules, among the content and the contextual features of posts published on Twitter i.e., the tweets. To effectively support knowledge discovery from tweets, the TweM framework performs two main steps: i taxonomy generation over tweet keywords and context data and ii generalized association rule mining, driven by the generated taxonomy, from a sequence of tweet collections. Unlike traditional mining approaches, the generalized rule mining session performed on the current tweet collection also considers the evolution of the extracted patterns across the sequence of the previous mining sessions to prevent the discarding of rare knowledge that frequently occurs in a number of past extractions. Experiments, performed on both real Twitter posts and synthetic datasets, show the effectiveness and the efficiency of the proposed TweM framework in supporting knowledge discovery from Twitter user-generated content.