Visualizing Gender Gap in Film Industry over the Past 100 Years
July 14, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Junkai Man, Ruitian Wu, Chenglin Zhang, Xin Tong
arXiv ID
2207.06692
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Visualizing big data can provide valuable insights into social science research. In this project, we focused on visualizing the potential gender gap in the global film industry over the past 100 years. We profiled the differences both for the actors/actresses and male/female movie audiences and analyzed the IMDb data of the most popular 10,000 movies (the composition and importance of casts of different genders, the cooperation network of the actors/actresses, the movie genres, the movie descriptions, etc.) and audience ratings (the differences between male's and female's ratings). Findings suggest that the gender gap has been distinct in many aspects, but a recent trend is that this gap narrows down and women are gaining discursive power in the film industry. Our study presented rich data, vivid illustrations, and novel perspectives that can serve as the foundation for further studies on related topics and their social implications.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted