Extracting semantic knowledge from twitter
ePart'11 Proceedings of the Third IFIP WG 8.5 international conference on Electronic participation
Clustering of technology tweets and the impact of stop words on clusters
Proceedings of the 50th Annual Southeast Regional Conference
Improving news ranking by community tweets
Proceedings of the 21st international conference companion on World Wide Web
Life activity modeling of news event on twitter using energy function
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Twevent: segment-based event detection from tweets
Proceedings of the 21st ACM international conference on Information and knowledge management
Sentiment analysis for tracking breaking events: a case study on twitter
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Evaluating the utilization of Twitter messages as a source of security alerts
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Detecting topic labels for tweets by matching features from pseudo-relevance feedback
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Twitter has been used as one of the communication channels for spreading breaking news. We propose a method to collect, group, rank and track breaking news in Twitter. Since short length messages make similarity comparison difficult, we boost scores on proper nouns to improve the grouping results. Each group is ranked based on popularity and reliability factors. Current detection method is limited to facts part of messages. We developed an application called “Hotstream” based on the proposed method. Users can discover breaking news from the Twitter timeline. Each story is provided with the information of message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.