Bollywood Movie Corpus for Text, Images and Videos
This work addresses gender bias in text for researchers in natural language processing and fairness, but it is incremental as it builds on existing datasets by adding a new domain-specific collection.
The authors tackled the problem of detecting and removing gender bias by creating a Bollywood Movie Corpus with 4000 movies from Wikipedia and 880 trailers from YouTube, providing preliminary results that suggest the dataset is useful for such tasks.
In past few years, several data-sets have been released for text and images. We present an approach to create the data-set for use in detecting and removing gender bias from text. We also include a set of challenges we have faced while creating this corpora. In this work, we have worked with movie data from Wikipedia plots and movie trailers from YouTube. Our Bollywood Movie corpus contains 4000 movies extracted from Wikipedia and 880 trailers extracted from YouTube which were released from 1970-2017. The corpus contains csv files with the following data about each movie - Wikipedia title of movie, cast, plot text, co-referenced plot text, soundtrack information, link to movie poster, caption of movie poster, number of males in poster, number of females in poster. In addition to that, corresponding to each cast member the following data is available - cast name, cast gender, cast verbs, cast adjectives, cast relations, cast centrality, cast mentions. We present some preliminary results on the task of bias removal which suggest that the data-set is quite useful for performing such tasks.