The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The Journal of Machine Learning Research
Topic initiator detection on the world wide web
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TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Discovery of significant emerging trends
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online multiscale dynamic topic models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Community-based topic modeling for social tagging
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Event detection with spatial latent Dirichlet allocation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Structural trend analysis for online social networks
Proceedings of the VLDB Endowment
Emerging topic detection using dictionary learning
Proceedings of the 20th ACM international conference on Information and knowledge management
Indices of novelty for emerging topic detection
Information Processing and Management: an International Journal
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Hot topics are usually those breaking news discussed most at online forums, especially microblogging systems, such as twitter, which helps to learn user concentration and public opinion. This paper focuses on the problem of predicting emerging hot topics. Previous prediction models usually focus on building the content profile to discover the hot topics, they may neglect the social network function or overlook the keyword feature of the post. In this paper, we address this problem by introducing a combined model using the content and the connection information. We define the concept of topic hotness, introduce the algorithm calculating the hotness with content based hotness and connection based hotness, and finally we predict those emerging hot topics by the hotness evolution model.