Ethnic Representation Analysis of Commercial Movie Posters
- URL: http://arxiv.org/abs/2207.08169v1
- Date: Sun, 17 Jul 2022 13:13:02 GMT
- Title: Ethnic Representation Analysis of Commercial Movie Posters
- Authors: Dima Kagan, Mor Levy, Michael Fire, and Galit Fuhrmann Alpert
- Abstract summary: We develop a novel approach for evaluating ethnic bias in the film industry by analyzing nearly 125,000 posters using state-of-the-art deep learning models.
Our analysis shows that while ethnic biases still exist, there is a trend of reduction of bias, as seen by several parameters.
An automatic approach to monitor ethnic diversity in the film industry, potentially integrated with financial value, may be of significant use for producers and policymakers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the last decades, global awareness towards the importance of diverse
representation has been increasing. Lack of diversity and discrimination toward
minorities did not skip the film industry. Here, we examine ethnic bias in the
film industry through commercial posters, the industry's primary advertisement
medium for decades. Movie posters are designed to establish the viewer's
initial impression. We developed a novel approach for evaluating ethnic bias in
the film industry by analyzing nearly 125,000 posters using state-of-the-art
deep learning models. Our analysis shows that while ethnic biases still exist,
there is a trend of reduction of bias, as seen by several parameters.
Particularly in English-speaking movies, the ethnic distribution of characters
on posters from the last couple of years is reaching numbers that are
approaching the actual ethnic composition of US population. An automatic
approach to monitor ethnic diversity in the film industry, potentially
integrated with financial value, may be of significant use for producers and
policymakers.
Related papers
- The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention [61.80236015147771]
We quantify the trade-off between using diversity interventions and preserving demographic factuality in T2I models.
Experiments on DoFaiR reveal that diversity-oriented instructions increase the number of different gender and racial groups.
We propose Fact-Augmented Intervention (FAI), which instructs a Large Language Model (LLM) to reflect on verbalized or retrieved factual information about gender and racial compositions of generation subjects in history.
arXiv Detail & Related papers (2024-06-29T09:09:42Z) - Inclusive content reduces racial and gender biases, yet non-inclusive content dominates popular media outlets [1.4204016278692333]
This study examines the manner in which racial and gender groups are portrayed in popular media imagery.
We collect over 300,000 images spanning over five decades and utilize state-of-the-art machine learning models.
We find that racial minorities appear far less frequently than their White counterparts.
We also find that women are more likely to be portrayed with their full bodies in images, whereas men are more frequently presented with their faces.
arXiv Detail & Related papers (2024-05-10T11:34:47Z) - Demystifying Visual Features of Movie Posters for Multi-Label Genre
Identification [0.393259574660092]
We present a deep transformer network with a probabilistic module to identify the movie genres exclusively from the poster.
For experimental analysis, we procured 13882 number of posters of 13 genres from the Internet Movie Database (IMDb)
arXiv Detail & Related papers (2023-09-21T12:39:36Z) - Discrimination through Image Selection by Job Advertisers on Facebook [79.21648699199648]
We propose and investigate the prevalence of a new means for discrimination in job advertising.
It combines both targeting and delivery -- through the disproportionate representation or exclusion of people of certain demographics in job ad images.
We use the Facebook Ad Library to demonstrate the prevalence of this practice.
arXiv Detail & Related papers (2023-06-13T03:43:58Z) - The Face of Populism: Examining Differences in Facial Emotional
Expressions of Political Leaders Using Machine Learning [57.70351255180495]
We apply a deep-learning-based computer-vision algorithm to a sample of 220 YouTube videos depicting political leaders from 15 different countries.
We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S.
News Headlines [63.52264764099532]
We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022.
We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs.
Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
arXiv Detail & Related papers (2023-03-28T03:31:37Z) - Easily Accessible Text-to-Image Generation Amplifies Demographic
Stereotypes at Large Scale [61.555788332182395]
We investigate the potential for machine learning models to amplify dangerous and complex stereotypes.
We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects.
arXiv Detail & Related papers (2022-11-07T18:31:07Z) - Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of
Movie Dialogues [20.222820874864748]
Social biases and stereotypes present in movies can cause extensive damage due to their reach.
We introduce a new dataset of movie scripts that are annotated for identity bias.
The dataset contains dialogue turns annotated for (i) bias labels for seven categories, viz., gender, race/ethnicity, religion, age, occupation, LGBTQ, and other.
arXiv Detail & Related papers (2022-05-31T16:49:51Z) - Computational appraisal of gender representativeness in popular movies [0.0]
This article illustrates how automated computational methods may be used to scale up such empirical observations.
We specifically apply a face and gender detection algorithm on a broad set of popular movies spanning more than three decades to carry out a large-scale appraisal of the on-screen presence of women and men.
arXiv Detail & Related papers (2020-09-16T13:15:11Z) - Victim or Perpetrator? Analysis of Violent Characters Portrayals from
Movie Scripts [37.32711420774085]
Violent content in the media can influence viewers' perception of the society.
We propose that computational methods can aid in the large-scale analysis of violence in movies.
arXiv Detail & Related papers (2020-08-19T02:18:53Z) - Measuring Female Representation and Impact in Films over Time [78.5821575986965]
Women have always been underrepresented in movies and not until recently has the representation of women in movies improved.
We propose a new measure, the female cast ratio, and compare it to the commonly used Bechdel test result.
arXiv Detail & Related papers (2020-01-10T15:29:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.