This repository contains code to reproduce analysis in "Does Gender Matter in the News? Detecting and Examining Gender Bias" by Jamell Dacon and Haochen Liu, as part of the The World Wide Web Conference 2021 -- WWW'21.
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Paper BiBTeX citation:
@inbook{10.1145/3442442.3452325, author = {Dacon, Jamell and Liu, Haochen}, title = {Does Gender Matter in the News? Detecting and Examining Gender Bias in News Articles}, year = {2021}, isbn = {9781450383134}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3442442.3452325}, abstract = {To attract unsuspecting readers, news article headlines and abstracts are often written with speculative sentences or clauses. Male dominance in the news is very evident, whereas females are seen as “eye candy” or “inferior”, and are underrepresented and under-examined within the same news categories as their male counterparts. In this paper, we present an initial study on gender bias in news abstracts in two large English news datasets used for news recommendation and news classification. We perform three large-scale, yet effective text-analysis fairness measurements on 296,965 news abstracts. In particular, to our knowledge we construct two of the largest benchmark datasets of possessive (gender-specific and gender-neutral) nouns and attribute (career-related and family-related) words datasets1 which we will release to foster both bias and fairness research aid in developing fair NLP models to eliminate the paradox of gender bias. Our studies demonstrate that females are immensely marginalized and suffer from socially-constructed biases in the news. This paper individually devises a methodology whereby news content can be analyzed on a large scale utilizing natural language processing (NLP) techniques from machine learning (ML) to discover both implicit and explicit gender biases. }, booktitle = {Companion Proceedings of the Web Conference 2021}, pages = {385–392}, numpages = {8} }
We conduct experiments to detect and examine the bias across the two datasets. We will now detail the datasets as follows:
The MIND dataset was collected from the Microsoft News website, for more detailed information about the MIND dataset, you can refer to the following paper: MIND paper, (Wu et al., 2020). They randomly sampled news from from October 12 to November 22, 2019 for 6 weeks creating two datasets i.e., MIND and MIND-small both totalling in 161,013 news articles. Each news article contains a news ID, a category label, a title, and a body (url); however, not every article contains an abstract resulting in 96,112 abstracts. We used the training set (largest set of news articles) since both the validation and test sets are subsets of the training set. MIND is created to serve as a new news recommendation benchmark dataset.
The NCD dataset was collected from Huffpost. The news articles were sampled from news headlines from the year 2012 to 2018 totalling in 202,372 news articles. Each news article contains a category label, headline, authors, link, and date; however, not every article contains a short description (abstract) resulting in 200,853 abstracts. NCD serves as a news classification and recommendation benchmark dataset.
Please refere to Sociology Hacks for a step by step tutorial on Centering Reasonance Analysis (CRA) and mentions the necessary python packages needed for implementation.
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