Difference between revisions of "Yang et. al., SIGKDD 2012"
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This study uses features applied in previous work on Twitter Rumor Detection and analyses the different effects of these features in Weibo. Furthermore, it proposes new features that improve the overall results. | This study uses features applied in previous work on Twitter Rumor Detection and analyses the different effects of these features in Weibo. Furthermore, it proposes new features that improve the overall results. | ||
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| + | == Features == | ||
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| + | The set of features considered contain several features from previous work. | ||
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| + | *Content-based | ||
| + | ** | ||
Revision as of 20:01, 29 September 2012
Contents
Citation
Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. 2012. Automatic detection of rumor on Sina Weibo. In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics (MDS '12). ACM, New York, NY, USA.
Online version
Automatic detection of rumor on Sina Weibo
Summary
This Paper addresses the problem of Rumor Detection in Weibo (equivalent to Twitter in Mainland China). A classifier is trained using manually annotated data (identified false rumors) from a rumor-busting service available in Sina Weibo.
This study uses features applied in previous work on Twitter Rumor Detection and analyses the different effects of these features in Weibo. Furthermore, it proposes new features that improve the overall results.
Features
The set of features considered contain several features from previous work.
- Content-based
