Status: V5.3 (Code provided as-is; only sporadic updates expected).
This repo contains the code and framework used in our study on gender bias in the media. We present the Gender Gap Tracker (GGT), an automated system that measures men and women’s voices on seven major Canadian news outlets in real time. We analyze the rich information in news articles using Natural Language Processing (NLP) and quantify the discrepancy in proportions of men and women quoted. Our larger goals through this project are to enhance awareness of women’s portrayal in public discourse through hard evidence, and to encourage news organizations to provide a more diverse set of voices in their reporting.
The Gender Gap Tracker is a collaboration between Informed Opinions, a non-profit dedicated to amplifying women’s voices in media and Simon Fraser University, through the Discourse Processing Lab and the Big Data Initiative.
dashboard_for_research: Research dashboard and apps that allow us to explore the GGT data in more detail.NLP: NLP modules for performing quote extraction and entity gender annotation on the news articles.scraping_and_aggregation: Modules for scraping news articles from various Canadian news organizations' websites, and the code we use to extract aggregate statistics from the database.
See CONTRIBUTORS.md
Future research directions include an examination of how female and male quotations are distributed in the articles from different topics and written by female vs. male authors. In future versions of the software, we are planning to visualize more fine-grained information about who is being quoted, separating politicians, witnesses and/or victims, from experts (as informed sources of analysis, context and opinion). Finally, we are looking into different ways of separating wire copy from the original publication of each news outlet in order to provide a clearer view of the gender gap in Canadian media, produced by the news outlets themselves.
For more information about the research methodology and for questions regarding collaboration, please contact:
Maite Taboada
mtaboada@sfu.ca
Director, Discourse Processing Lab
Simon Fraser University