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Gender Bias in the News: A Scalable Topic Modelling and Visualization Framework
Prashanth Rao1, Maite Taboada1
1Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, BC, Canada.
Frontiers in Artificial Intelligence
|July 5, 2021
Summary
This study introduces a new method for analyzing news topics and gender representation. It reveals how certain topics disproportionately feature men or women, reinforcing societal gender roles.
Area of Science:
- Computational Linguistics
- Media Studies
- Sociology
Background:
- Existing topic modeling research often uses fixed text collections (closed corpora).
- News corpora are dynamic and continuously growing (open corpora).
- Understanding gender representation in media is crucial for societal equity.
Purpose of the Study:
- To develop and present a topic modeling and data visualization methodology.
- To examine gender-based disparities in news articles across different topics.
- To analyze representation in an open, continuously growing news corpus.
Main Methods:
- Utilized Latent Dirichlet Allocation (LDA) for topic discovery.
- Applied the methodology to a 2-year corpus of mainstream Canadian news articles.
- Generated monthly topics (keyword distributions) to track trends.
Main Results:
- Identified distinct topics with prominent representation of either women or men.
- Topics like lifestyle and healthcare featured more women; sports, politics, and business featured more men.
- Observed a reinforcement of gendered societal roles: women in caregiving, men in leadership/business.
Conclusions:
- The findings highlight a self-reinforcing gendered division in news representation.
- Unequal representation can entrench societal perceptions of women as caregivers and men as leaders.
- The methodology is robust, scalable, and applicable to future studies on media representation and language analysis.
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