Does Gender Matter in the News?
Detecting and Examining Gender Biasin News Articles
Jamell Dacon1 and Haochen Liu1
1: Michigan State University, East Lansing, MI, USA

WWW 2021
Presenter: Jamell Dacon
daconjam@msu.edu

Online News Recommendation
Benefits:• Constantly updating• No costs• Ability to discuss the news with peers quickly• A great number of news choice

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Online News Recommendation
Cons:• The existence of several viewpoints• Ideological bias
• Coverage bias
Media Bias
• Selection bias
• Presentation bias

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Speculative Sentences/Clauses
Title example(s):• “Women who want to succeed at work should shut up while men who want the same should keep talking,research says”• “Men have been promoted 3 times more than women
during the pandemic, study finds”• “Women in the workplace.”
Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Goal• To detect and examine the phenomenon of implicit andexplicit gender bias in the abstracts of news articles• To gain a sense of understanding of the gender
representation in the news by examining therelationships between social hierarchies and news
content.

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Motivation• To identify how several forms of media bias (i.e.,coverage bias, selection bias, and presentation bias)
contribute to the problem of gender bias.

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Contributions• We construct two large benchmark datasets: (1) possessive(gender-specific and gender-neutral) nouns dataset and (2)attribute (career-related and family-related);• We conduct three large scale analyses to detect and examine
gender biases in distribution, content, and labeling and word
choice;• We introduce a series of measurements to better understandgender representation in news articles quantitatively andqualitatively.
Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Datasets• MIND: The MIND dataset was collected from the Microsoft
News website, randomly sampled news for 6 weeks fromOctober 12th to November 22th, 2019. We obtained 96,112
abstracts.
• NCD: The NCD dataset was collected from Huffpost. The news
articles were sampled from news headlines from the year
2012 to 2018 totaling in 202,372 news articles. We obtained
200,853 abstracts.

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Bias in Gender Distribution

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam

Bias in Content

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Bias in Wording• Sentiment Analysis
A popular, well known sentiment analysis tool, VADER to measure the sentiment ofeach news abstracts. VADER computes a normalized, weighted compound score ofeach word in a sentence by summing their valence scores between -1 (beingextremely negative) and +1 (being extremely positive).

• Centering Resonance Analysis
A network word-based method that constructs a network representation of correlated
words. This method exploits rich textual data and expresses the intention andmeandering behaviors of authors (or columnists). To determine textual “centers”
without the use of dictionaries i.e., identifying the most central nouns that mostly
contribute to the meaning of a document or corpora.
Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Bias in Wording
Figure 1: (a) The resulting CRA network for the top 20 nouns in the M tagged abstracts.

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Bias in Wording
Figure 1: (b) The resulting CRA network for the top 20 nouns in the F tagged abstracts.

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Conclusion
We have investigated that gender bias in media appears indifferent forms.
• Bias in gender distribution across all news categories and exploring
the top four intersecting career words (prefixes)
for females compared to their respective male counterparts• Bias in content in terms of attribute words• Bias in wording by constructing CRA networks for the top 20 mostcentral nouns for both gender-tagged abstracts. Each graphillustrated the compound nouns that contributed the most tagged
abstracts.
Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/

Thank You!
Code and Data: https://github.com/daconjam/Detecting_Gender_Bias
DSE Lab@MSU: http://dse.cse.msu.edu/

Jamell Dacon (daconjam@msu.edu),
https://www.cse.msu.edu/~daconjam/