Machine Learning - Special issue on learning with probabilistic representations
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Top 10 algorithms in data mining
Knowledge and Information Systems
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Blog credibility ranking by exploiting verified content
Proceedings of the 3rd workshop on Information credibility on the web
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
WEKA---Experiences with a Java Open-Source Project
The Journal of Machine Learning Research
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy
Proceedings of the 22nd international conference on World Wide Web companion
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Twitter has shown its greatest power of influence for its fast information diffusion. Previous research has shown that most of the tweets posted are truthful, but as some people post the rumors and spams on Twitter in emergence situation, the direction of public opinion can be misled and even the riots are caused. In this paper, we focus on the methods for the information credibility in emergency situation. More precisely, we build a novel Twitter monitor model to monitoring Twitter online. Within the novel monitor model, an unsupervised learning algorithm is proposed to detect the emergency situation. A collection of training dataset which includes the tweets of typical events is gathered through the Twitter monitor. Then we manually dispatch the dataset to experts who label each tweet into two classes: credibility or incredibility. With the classified tweets, a number of features related to the user social behavior, the tweet content, the tweet topic and the tweet diffusion are extracted. A supervised method using learning Bayesian Network is used to predict the tweets credibility in emergency situation. Experiments with the tweets of UK Riots related topics show that our procedure achieves good performance to classify the tweets compared with other state-of-art algorithms.