A survey that unifies the literature on rumor detection, rumor source detection, and joint detection into one taxonomy with formal definitions.
Predicting Factuality of Reporting and Bias of News Media Sources
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.
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Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey
A survey that unifies the literature on rumor detection, rumor source detection, and joint detection into one taxonomy with formal definitions.