On-line new event detection and tracking
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
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WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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Efficient network aware search in collaborative tagging sites
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Better Naive Bayes classification for high-precision spam detection
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SemNews: a semantic news framework
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning similarity metrics for event identification in social media
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DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
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Most of the new social media sites such as Twitter and Flickr are using RSS Feeds for sharing a wide variety of current and future real-world events. Indeed, RSS Feeds is considered as a powerful realtime means for real-world events sharing within the social Web. Thus, by identifying these events and their associated user-contributed social media resources, we can greatly improve event browsing and searching. However, a thriving challenge of events mining processes is owed to an efficient as well as a timely identification of events. In this paper, we are mainly dealing with event mining from heterogenous social media RSS Feeds. Therefore, we introduce a new approach, called RssE-Miner, in order to get out these events. The main thrust of the introduced approach stands in presenting a better trade-off between event mining accuracy and swiftness. Specifically, we adopted the probabilistic Naive Bayesian model within the exploitation of the rich context associated with social media Rss Feeds contents, including user-provided annotations (e.g., title, tags) and the automatically generated information (e.g., time) for efficiently mining future events. Carried out experiments over two real-world datasets emphasize the relevance of our proposal.