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Utilizing overtly political texts for fully automatic evaluation of political leaning of online news websites

Maayan Zhitomirsky-Geffet (Department of Information Science, Bar-Ilan University, Ramat Gan, Israel)
Esther David (Department of Computer Science, Ashkelon Academic College,Ashkelon, Israel)
Moshe Koppel (Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel)
Hodaya Uzan (Department of Computer Science, Bar-Ilan university, Ramat Gan, Israel)

Online Information Review

ISSN: 1468-4527

Article publication date: 13 June 2016




Reliability and political bias of mass media has been a controversial topic in the literature. The purpose of this paper is to propose and implement a methodology for fully automatic evaluation of the political tendency of the written media on the web, which does not rely on subjective human judgments.


The underlying idea is to base the evaluation on fully automatic comparison of the texts of articles on different news websites to the overtly political texts with known political orientation. The authors also apply an alternative approach for evaluation of political tendency based on wisdom of the crowds.


The authors found that the learnt classifier can accurately distinguish between self-declared left and right news sites. Furthermore, news sites’ political tendencies can be identified by automatic classifier learnt from manifestly political texts without recourse to any manually tagged data. The authors also show a high correlation between readers’ perception (as a “wisdom of crowds” evaluation) of the bias and the classifier results for different news sites.

Social implications

The results are quite promising and can put an end to the never ending dispute on the reliability and bias of the press.


This paper proposes and implements a new approach for fully automatic (independent of human opinion/assessment) identification of political bias of news sites by their texts.



Zhitomirsky-Geffet, M., David, E., Koppel, M. and Uzan, H. (2016), "Utilizing overtly political texts for fully automatic evaluation of political leaning of online news websites", Online Information Review, Vol. 40 No. 3, pp. 362-379.



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