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

Maayan Zhitomirsky-Geffet, Esther David, Moshe Koppel, Hodaya Uzan

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Purpose-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. Design/methodology/approach-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. Findings-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. Originality/value-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.

Original languageEnglish
Pages (from-to)362-379
Number of pages18
JournalOnline Information Review
Issue number3
StatePublished - 13 Jun 2016

Bibliographical note

Publisher Copyright:
© Emerald Group Publishing Limited.


  • Automatic text categorization
  • Mass media
  • Objective evaluation
  • Online news sites
  • Political bias
  • Supervised machine learning


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