Bollywood Movie Corpus for Text, Images and Videos

TL;DR

This paper introduces a comprehensive Bollywood movie dataset combining text, images, and videos, aimed at detecting and mitigating gender bias in media content, with initial results demonstrating its utility for bias removal tasks.

Contribution

The paper presents a new multi-modal Bollywood movie corpus with detailed annotations, and discusses challenges faced during its creation, enabling research on bias detection and removal.

Findings

Preliminary results show the dataset's effectiveness for bias removal tasks.

The corpus includes extensive annotations for gender and other attributes.

The dataset covers movies from 1970 to 2017 with diverse media types.

Abstract

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.