Elements of information theory
Elements of information theory
The Journal of Machine Learning Research
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Harnessing the Crowdsourcing Power of Social Media for Disaster Relief
IEEE Intelligent Systems
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
A new metric for probability distributions
IEEE Transactions on Information Theory
Maximizing benefits from crowdsourced data
Computational & Mathematical Organization Theory
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Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief efforts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user's location and the user's affinity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques.