Name
Abstract | ||
|---|---|---|
The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying such stereotypes and bias in Hindi movie industry (Bollywood). We analyze movie plots and posters for all movies released since 1970. The gender bias is detected by semantic modeling of plots at inter-sentence and intra-sentence level. Different features like occupation, introduction of cast in text, associated actions and descriptions are captured to show the pervasiveness of gender bias and stereo- type in movies. We derive a semantic graph and compute centrality of each character and observe similar bias there. We also show that such bias is not applicable for movie posters where females get equal importance even though their character has little or no impact on the movie plot. Furthermore, we explore the movie trailers to estimate on-screen time for males and females and also study the portrayal of emotions by gender in them. The silver lining is that our system was able to identify 30 movies over last 3 years where such stereotypes were broken. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Social and Information Networks | Social psychology,Graph,Media studies,Film industry,Hindi,Computer science,Centrality,Gender bias,Artificial intelligence,Phenomenon,Machine learning |
DocType | Volume | Citations |
Journal | abs/1710.04117 | 1 |
PageRank | References | Authors |
0.35 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nishtha Madaan | 1 | 2 | 2.08 |
| Sameep Mehta | 2 | 6 | 5.47 |
| Taneea S. Agrawaal | 3 | 1 | 1.03 |
| Vrinda Malhotra | 4 | 1 | 1.03 |
| Aditi Aggarwal | 5 | 1 | 1.03 |
| Mayank Saxena | 6 | 1 | 1.03 |