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Computational text analysis reveals how TV representations of professions have changed over 70 years


The blue-colored professions have the highest 10 highest proportion of constructive sentiment mentions. The red-colored professions have the highest 10 highest proportion of unfavourable sentiment mentions. Credit: Baruah et al., 2022, PLOS ONE, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Throughout 70 years of information on media subtitles for tv and movie, structure and engineering are probably the most positively portrayed professions, whereas sales-related professions fare worst, discover the authors of a brand new research printed within the open entry journal PLOS ONE.

Probing media depictions of professions can spotlight stereotypes or discrimination. It will probably additionally underscore tendencies in career choices: for instance, the television series “Mad Men” and “Trust Me” prompted many to enroll in promoting programs, detective Dana Scully on “The X-Files” impressed young women to pursue a profession in STEM, and the discharge of the film “Top Gun” noticed a dramatic improve in US Navy recruitment.

Sabyasachee Baruah, Krishna Somandepalli and Shrikanth Narayanan on the University of Southern California have created a database of the English subset of the OpenSubtitles dataset, analyzing mentions of 4,000 professions in subtitles throughout 136,000 motion pictures and TV reveals launched between 1950 and 2017.

Whereas general the frequency of job titles in media correlated with real-world employment statistics of corresponding professions, the researchers discovered differing tendencies for particular occupations. For instance, they noticed elevated mentions of STEM, arts, sports activities, and leisure occupations through the years, and a decreased frequency of handbook labor and navy occupations.

The researchers additionally famous that:

  • Structure and engineering are probably the most positively portrayed professions, whereas sales-related professions are least positively represented
  • Over time, mentions of astronauts, detectives, therapists, musicians, singers and engineers have turn out to be extra constructive
  • Sentiment expressed towards legal professionals, police, and docs has turn out to be extra unfavourable over time
  • Gender-neutral phrases like therapeutic massage therapists and flight attendants have gotten extra frequent than their gendered counterparts
  • The frequency of some feminine job titles equivalent to waitresses, congresswomen, and policewomen has both elevated or remained regular, however these will not be as frequent as most male job titles
  • The frequency of specialised professions like cardiologists, gynecologists, and neurologists has elevated whereas generic phrases like docs and nurses have decreased

The analysis is restricted to subtitles and to UK and US media, so could not seize all facets of visible illustration on display, and the frequency and sentiment analyses don’t management for frequency and sentiment in on a regular basis language. Nonetheless, the authors consider that the research represents a beneficial perception into media depictions of professions over time.

Sabyasachee Baruah provides, “We study how the frequency and the expressed sentiment of professions in movie and TV-show subtitles change over time, and find that the media frequency of professions significantly correlates with their real-world employment trends.”

Shrikanth Narayanan notes, “AI provides a quick and fast way to quantify social trends in movies and TV over time. In this particular study, we are able to understand sentiment towards a host of professions. It is interesting to note that the careers on screen dovetail with real-life trends in employment.”


Occupational gender bias prevalent in online images, study finds


Extra info:
Sabyasachee Baruah et al, Illustration of professions in leisure media: Insights into frequency and sentiment tendencies by computational textual content evaluation, PLOS ONE (2022). DOI: 10.1371/journal.pone.0267812

Quotation:
Computational textual content evaluation reveals how TV representations of professions have modified over 70 years (2022, May 18)
retrieved 18 May 2022
from https://techxplore.com/information/2022-05-text-analysis-reveals-tv-representations.html

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