Ly Dinh: Text-level indicators of gender bias in hurricane news coverage

Title: Text-level indicators of gender bias in hurricane news coverage
Session Lead: Ly Dinh
Time: 1 pm – 2 pm, Tuesday, 2021-10-05
Location: Zoom


Hurricanes are ungendered phenomena that are ascribed with gendered names. We examined if people, in this case, authors of news articles and individuals quoted in news, use gendered language when referring to hurricanes. This work helps identify if people use gender stereotyping when referring to gender-neutral entities, and what these stereotypes might be. We use methods from natural language processing, qualitative text analysis, and statistics to analyze how gender is expressed in disaster-related news via text-level indicators: (1) pronouns, (2) lexical (word choice), syntactic (part of speech) and semantic (sentiment) features of words related to hurricanes, and (3) types of sources quoted. 

Our sample contains news articles on 47 hurricane events from 1979 to 2012 from two weeks before to two weeks after landfall. We consider hurricanes from 1979 onwards as gender names began to alternate between male and female names starting that year. We find that: 

(1) hurricanes are mainly referred to by gender-neutral pronouns (as they should be), however, (2) when gendered pronouns are used, female-named hurricanes are five times more likely to be referred to by a gendered pronoun than male named hurricanes, (3) adjectives and verbs used in discussing female-named hurricanes are on average more negative than those used for reporting on male-named-hurricanes, and (4) governmental sources are most frequently quoted as authority voices (though voices from citizens and non-governmental entities are catching up), and a majority of these voices are positive in sentiment, and do not directly mention hurricanes with gendered references.

Related Materials: Box-Folder