Paper on how to measure news bias by quantitative text analysis


My paper titled Measuring news bias: Russia’s official news agency ITAR-TASS’s coverage of the Ukraine crisis is published in the European Journal of Communication.
In this piece, I estimated how much the news coverage of Ukraine crisis by ITAR-TASS was biased by the influence of the Russian government with quantitative text analysis techniques:

Objectivity in news reporting is one of the most widely discussed topics in journalism, and numbers of studies on bias in news have been conducted, but there is little agreement on how to define or measure news bias. Aiming to settle the theoretical and methodological disagreement, the author redefined news bias and applied a new methodology to detect the Russian government’s influence on ITAR-TASS during the Ukraine crisis. A longitudinal content analysis of over 35,000 English-language newswires on the Ukraine crisis published by ITAR-TASS and Interfax clearly showed that ITAR-TASS’s framing of Ukraine was reflecting desirability of pivotal events in the crisis to the Russian government. This result reveals Russia’s strategic use of the state-owned news agency for international propaganda in its ‘hybrid war’, demonstrating the effectiveness of the new approach to news bias.

Newsmap paper in Digital Journalism


My paper on geographical news classification is finally published in Digital Journalism, a sister journal of Journalism Studies. In this paper, I not only evaluate Newsmap’s classification accuracy, but compare it with other tools such as Open Calais and

This paper presents the results of an evaluation of three different types of geographical news classification methods: (1) simple keyword matching, a popular method in media and communications research; (2) geographical information extraction systems equipped with named-entity recognition and place name disambiguation mechanisms (Open Calais and; and (3) a semi-supervised machine learning classifier developed by the author (Newsmap). Newsmap substitutes manual coding of news stories with dictionary-based labelling in the creation of large training sets to extract large numbers of geographical words without human involvement and it also identifies multi-word names to reduce the ambiguity of the geographical traits fully automatically. The evaluation of classification accuracy of the three types of methods against 5000 human-coded news summaries reveals that Newsmap outperforms the geographical information extraction systems in overall accuracy, while the simple keyword matching suffers from ambiguity of place names in countries with ambiguous place names.