Abstract
We examine gender bias in media by tallying the number of men and women quoted in news text, using the Gender Gap Tracker, a software system we developed specifically for this purpose. The Gender Gap Tracker downloads and analyzes the online daily publication of seven English-language Canadian news outlets and enhances the data with multiple layers of linguistic information. We describe the Natural Language Processing technology behind this system, the curation of off-the-shelf tools and resources that we used to build it, and the parts that we developed. We evaluate the system in each language processing task and report errors using real-world examples. Finally, by applying the Tracker to the data, we provide valuable insights about the proportion of people mentioned and quoted, by gender, news organization, and author gender. Data collected between October 1, 2018 and September 30, 2020 shows that, in general, men are quoted about three times as frequently as women. While this proportion varies across news outlets and time intervals, the general pattern is consistent. We believe that, in a world with about 50% women, this should not be the case. Although journalists naturally need to quote newsmakers who are men, they also have a certain amount of control over who they approach as sources. The Gender Gap Tracker relies on the same principles as fitness or goal-setting trackers: By quantifying and measuring regular progress, we hope to motivate news organizations to provide a more diverse set of voices in their reporting.
Highlights
The Gender Gap in media and in societyWomen’s voices are disproportionately underrepresented in media stories
In Data acquisition and Natural Language Processing (NLP) processing pipeline, a high-level description of the data acquisition process and how we deploy NLP to extract quotes, identify people, and predict their gender
We have focused our survey on three aspects that have informed our work the most: descriptions of direct and indirect speech in linguistics, prediction of gender based on names in text, and extraction of dependency structures and quotes in Natural Language Processing to make a connection between entities and quotes
Summary
The Gender Gap in media and in society. Women’s voices are disproportionately underrepresented in media stories. The Global News Monitoring Project has been tracking the percentage of women represented in mainstream media since 1995, when it was 17%. In 2015, it had increased to only 24%, with a worrisome stalling in the previous decade [1]. At this rate, it would take more than 70 years to see 50% women in the media, a true reflection of their representation in society.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
