Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Information provenance in social media
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Real-World behavior analysis through a social media lens
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Lessons learned in using social media for disaster relief - ASU crisis response game
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
RAProp: ranking tweets by exploiting the tweet/user/web ecosystem and inter-tweet agreement
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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People increasingly use social media to get first-hand news and information. During disasters such as Hurricane Sandy and the tsunami in Japan people used social media to report injuries as well as send out their requests. During social movements such as Occupy Wall Street (OWS) and the Arab Spring, people extensively used social media to organize their events and spread the news. As more people rely on social media for political, social, and business events, it is more susceptible to become a place for evildoers to use it to spread misinformation and rumors. Therefore, users have the challenge to discern which piece of information is credible or not. They also need to find ways to assess the credibility of information. This problem becomes more important when the source of the information is not known to the consumer. In this paper we propose a method to measure user credibility in social media. We study the situations in which we cannot assess the credibility of the content or the credibility of the user (source of the information) based on the user's profile. We propose the CredRank algorithm to measure user credibility in social media. The algorithm analyzes social media users' online behavior to measure their credibility.