Radar de Parité: An NLP system to measure gender representation in French news stories
This work addresses societal issues by applying NLP to measure gender representation in French news, though it is incremental as it builds on existing methods with French-specific adaptations.
The researchers tackled the problem of measuring gender representation in French news by developing an NLP system called Radar de Parité, which analyzed over 282,512 articles from six Canadian French-language media outlets and found that women are underrepresented in news stories.
We present the Radar de Parité, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system's architecture and detail the challenges we overcame to address French-specific issues, in particular regarding coreference resolution, a new contribution to the NLP literature on French. We also showcase statistics covering over one year's worth of data (282,512 news articles). Our results highlight the underrepresentation of women in news stories, while also illustrating the application of modern NLP methods to measure gender representation and address societal issues.