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Predicting Factuality of Reporting and Bias of News Media Sources

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arxiv 1810.01765 v1 pith:3YKKJTP5 submitted 2018-10-02 cs.IR cs.LGstat.ML

Ramy Baly (1) , Georgi Karadzhov (3) , Dimitar Alexandrov (3) , James Glass (1) , Preslav Nakov (2) ((1) MIT Computer Science , Artificial Intelligence Laboratory , (2) Qatar Computing Research Institute , HBKU
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Qatar (3) Sofia University Bulgaria)
This is my paper · ORCID
classification cs.IRcs.LGstat.ML
keywords newsmediabiasfactualitypredictingreportingaccountarguably
verification ladder T0 review T1 audit T2 compute T3 formal
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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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