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Societal ideas and trends dictate media narratives and cinematic depictionswhich in turn influences people's beliefs and perceptions of the real world.Media portrayal of culture, education, government, religion, and family affecttheir function and evolution over time as people interpret and perceive theserepresentations and incorporate them into their beliefs and actions. It isimportant to study media depictions of these social structures so that they donot propagate or reinforce negative stereotypes, or discriminate against anydemographic section. In this work, we examine media representation ofprofessions and provide computational insights into their incidence, andsentiment expressed, in entertainment media content. We create a searchabletaxonomy of professional groups and titles to facilitate their retrieval fromspeaker-agnostic text passages like movie and television (TV) show subtitles.We leverage this taxonomy and relevant natural language processing (NLP) modelsto create a corpus of professional mentions in media content, spanning morethan 136,000 IMDb titles over seven decades (1950-2017). We analyze thefrequency and sentiment trends of different occupations, study the effect ofmedia attributes like genre, country of production, and title type on thesetrends, and investigate if the incidence of professions in media subtitlescorrelate with their real-world employment statistics. We observe increasedmedia mentions of STEM, arts, sports, and entertainment occupations in theanalyzed subtitles, and a decreased frequency of manual labor jobs and militaryoccupations. The sentiment expressed toward lawyers, police, and doctors isbecoming negative over time, whereas astronauts, musicians, singers, andengineers are mentioned favorably. Professions that employ more people haveincreased media frequency, supporting our hypothesis that media acts as amirror to society.

Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis