Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis
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.
Code (1)
Tasks
Cultural Vocal Bursts Intensity PredictionRetrievalSimilar Papers 제목 키워드 기반
Identifying professions & occupations in Health-related Social Media using Natural Language Processing
This paper describes the entry of the research group SINAI at SMM4H’s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: T…
Binary ClassificationClassificationNERScientists' Perspectives on the Potential for Generative AI in their Fields
Generative AI models, including large language models and multimodal models that include text and other media, are on the cusp of transforming many aspects of modern life, including entertainment, education, civic life, …
scientific discoveryAssessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media
This paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings i…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Word EmbeddingsWord Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media
ProfNER-ST focuses on the recognition of professions and occupations from Twitter using Spanish data. Our participation is based on a combination of word-level embeddings, including pre-trained Spanish BERT, as well as c…
DecoderWord EmbeddingsAI as Entertainment
Generative AI systems are predominantly designed, evaluated, and marketed as intelligent systems which will benefit society by augmenting or automating human cognitive labor, promising to increase personal, corporate, an…