Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
The kappa statistic: a second look
Computational Linguistics
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
What's with the attitude?: identifying sentences with attitude in online discussions
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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
Tweeting is believing?: understanding microblog credibility perceptions
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Rumor has it: identifying misinformation in microblogs
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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The problem of gauging information credibility on social networks has received considerable attention in recent years. Most previous work has chosen Twitter, the world's largest micro-blogging platform, as the premise of research. In this work, we shift the premise and study the problem of information credibility on Sina Weibo, China's leading micro-blogging service provider. With eight times more users than Twitter, Sina Weibo is more of a Facebook-Twitter hybrid than a pure Twitter clone, and exhibits several important characteristics that distinguish it from Twitter. We collect an extensive set of microblogs which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Sina Weibo. Unlike previous studies on Twitter where the labeling of rumors is done manually by the participants of the experiments, the official nature of this service ensures the high quality of the dataset. We then examine an extensive set of features that can be extracted from the microblogs, and train a classifier to automatically detect the rumors from a mixed set of true information and false information. The experiments show that some of the new features we propose are indeed effective in the classification, and even the features considered in previous studies have different implications with Sina Weibo than with Twitter. To the best of our knowledge, this is the first study on rumor analysis and detection on Sina Weibo.