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

• URL: http://arxiv.org/abs/2110.03873v2
• Date: Mon, 11 Oct 2021 07:31:08 GMT
• Title: Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis
• Authors: Sabyasachee Baruah, Krishna Somandepalli, and Shrikanth Narayanan
• Abstract summary: Societal ideas and trends dictate media narratives and cinematic depictions which in turn influences people's beliefs and perceptions of the real world. We create a searchable taxonomy of professional groups and titles to facilitate their retrieval from TV show subtitles. We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics.
• Score: 36.484171652138095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Societal ideas and trends dictate media narratives and cinematic depictions which in turn influences people's beliefs and perceptions of the real world. Media portrayal of culture, education, government, religion, and family affect their function and evolution over time as people interpret and perceive these representations and incorporate them into their beliefs and actions. It is important to study media depictions of these social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against any demographic section. In this work, we examine media representation of professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups and titles to facilitate their retrieval from speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing (NLP) models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors is becoming negative over time, whereas astronauts, musicians, singers, and engineers are mentioned favorably. Professions that employ more people have increased media frequency, supporting our hypothesis that media acts as a mirror to society.

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