Gender Bias in the Media


News media researchers have long contended that masculine values shape journalists’ every-day decisions about what is newsworthy. As a result, it is argued that topics and issues traditionally regarded as primarily of interest and relevance to women are routinely marginalised in the news, while men’s views and voices are given privileged space. Furthermore, when women do show up in the news, it is often as “eye candy,” thus reinforcing women’s value as sources of visual pleasure rather than residing in the content of their views. To date, evidence to support such claims has tended to be based on small-scale, manual analyses of news content.

In a series of peer-viewed publications, we explored the way in which gender bias is present in the media using automated, data-driven methods of content analysis across millions of news articles. We looked at both the number of mentions of men and women in the textual content of news media, as well as how often each gender is featured in the main images associated with online news articles.

We found that males were represented more often than females in both images and text, but in proportions that changed across topics, news outlets and mode. Moreover, the proportion of females was consistently higher in images than in text, for virtually all topics and news outlets; women were more likely to be represented visually than they were mentioned as a news actor or source.

Our large-scale, data-driven approach to analysing gender bias offers important empirical evidence of macroscopic patterns in news content concerning the way men and women are represented.

Example of the information extracted from the content of a BBC news article.


Probability of an extracted face image belonging to each gender broken down by topic.

Probability of an extracted face image belonging to each gender broken down by news outlet.

Probability of an extracted entity in the text belonging to each gender broken down by news outlet.

Probability of an extracted entity in the text belonging to each gender broken down by topic.


Related Publications

Sen Jia, Thomas Lansdall-Welfare, Saatviga Sudhahar, Cynthia Carter, Nello Cristianini: Women Are Seen More than Heard in Online Newspapers. In: PLoS One, 11 (2), 2016.

Sen Jia, Thomas Lansdall-Welfare, Nello Cristianini: Measuring Gender Bias in News Images. In: Proceedings of the 2015 International Conference Companion on World Wide Web (NewsWWW 2015), 2015.

Ilias Flaounas, Omar Ali, Thomas Lansdall-Welfare, Tijl De Bie, Nick Mosdell, Justin Lewis, Nello Cristianini: RESEARCH METHODS IN THE AGE OF DIGITAL JOURNALISM: Massive-scale automated analysis of news-content—topics, style and gender. In: Digital Journalism, 1 (1), pp. 102–116, 2013.