publication . Preprint . 2017

Bollywood Movie Corpus for Text, Images and Videos

Madaan, Nishtha; Mehta, Sameep; Saxena, Mayank; Aggarwal, Aditi; Agrawaal, Taneea S; Malhotra, Vrinda;
Open Access English
  • Published: 11 Oct 2017
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...
Subjects
ACM Computing Classification System: ComputingMilieux_MISCELLANEOUS
free text keywords: Computer Science - Computers and Society, Computer Science - Computation and Language
Download from

Anderson, H. and Daniels, M. (2017). https://pudding.cool/2017/03/film-dialogue/.

Bernardi, R., Cakici, R., Elliott, D., Erdem, A., Erdem, E., Ikizler-Cinbis, N., Keller, F., Muscat, A., and Plank, B. (2016). Automatic description generation from images: A survey of models, datasets, and evaluation measures. J. Artif. Intell. Res.(JAIR), 55:409-442. [OpenAIRE]

Carnes, M., Devine, P. G., Manwell, L. B., Byars-Winston, A., Fine, E., Ford, C. E., Forscher, P., Isaac, C., Kaatz, A., Magua, W., et al. (2015). Effect of an intervention to break the gender bias habit for faculty at one institution: a cluster randomized, controlled trial. Academic medicine: journal of the Association of American Medical Colleges, 90(2):221. [OpenAIRE]

Dobbin, F. and Jung, J. (2012). Corporate board gender diversity and stock performance: The competence gap or institutional investor bias?

Fast, E., Vachovsky, T., and Bernstein, M. S. (2016). Shirtless and dangerous: Quantifying linguistic signals of gender bias in an online fiction writing community. In ICWSM, pages 112-120. [OpenAIRE]

Ferraro, F., Mostafazadeh, N., Vanderwende, L., Devlin, J., Galley, M., Mitchell, M., et al. (2015). A survey of current datasets for vision and language research. arXiv preprint arXiv:1506.06833. [OpenAIRE]

Kay, M., Matuszek, C., and Munson, S. A. (2015). Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pages 3819-3828. ACM. [OpenAIRE]

Otterbacher, J. (2015). Linguistic bias in collaboratively produced biographies: crowdsourcing social stereotypes? In ICWSM, pages 298-307. [OpenAIRE]

Rose, J., Mackey-Kallis, S., Shyles, L., Barry, K., Biagini, D., Hart, C., and Jack, L. (2012). Face it: The impact of gender on social media images. Communication Quarterly, 60(5):588-607.

Saji, T. (2016). Gender bias in corporate leadership: A comparison between indian and global firms. Effective Executive, 19(4):27.

Soklaridis, S., Kuper, A., Whitehead, C., Ferguson, G., Taylor, V., and Zahn, C. (2017). Gender bias in hospital leadership: a qualitative study on the experiences of women ceos. Journal of Health Organization and Management, 31(2).

Terrell, J., Kofink, A., Middleton, J., Rainear, C., MurphyHill, E., Parnin, C., and Stallings, J. (2017). Gender differences and bias in open source: Pull request acceptance of women versus men. PeerJ Computer Science, 3:e111.

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...
Subjects
ACM Computing Classification System: ComputingMilieux_MISCELLANEOUS
free text keywords: Computer Science - Computers and Society, Computer Science - Computation and Language
Download from

Anderson, H. and Daniels, M. (2017). https://pudding.cool/2017/03/film-dialogue/.

Bernardi, R., Cakici, R., Elliott, D., Erdem, A., Erdem, E., Ikizler-Cinbis, N., Keller, F., Muscat, A., and Plank, B. (2016). Automatic description generation from images: A survey of models, datasets, and evaluation measures. J. Artif. Intell. Res.(JAIR), 55:409-442. [OpenAIRE]

Carnes, M., Devine, P. G., Manwell, L. B., Byars-Winston, A., Fine, E., Ford, C. E., Forscher, P., Isaac, C., Kaatz, A., Magua, W., et al. (2015). Effect of an intervention to break the gender bias habit for faculty at one institution: a cluster randomized, controlled trial. Academic medicine: journal of the Association of American Medical Colleges, 90(2):221. [OpenAIRE]

Dobbin, F. and Jung, J. (2012). Corporate board gender diversity and stock performance: The competence gap or institutional investor bias?

Fast, E., Vachovsky, T., and Bernstein, M. S. (2016). Shirtless and dangerous: Quantifying linguistic signals of gender bias in an online fiction writing community. In ICWSM, pages 112-120. [OpenAIRE]

Ferraro, F., Mostafazadeh, N., Vanderwende, L., Devlin, J., Galley, M., Mitchell, M., et al. (2015). A survey of current datasets for vision and language research. arXiv preprint arXiv:1506.06833. [OpenAIRE]

Kay, M., Matuszek, C., and Munson, S. A. (2015). Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pages 3819-3828. ACM. [OpenAIRE]

Otterbacher, J. (2015). Linguistic bias in collaboratively produced biographies: crowdsourcing social stereotypes? In ICWSM, pages 298-307. [OpenAIRE]

Rose, J., Mackey-Kallis, S., Shyles, L., Barry, K., Biagini, D., Hart, C., and Jack, L. (2012). Face it: The impact of gender on social media images. Communication Quarterly, 60(5):588-607.

Saji, T. (2016). Gender bias in corporate leadership: A comparison between indian and global firms. Effective Executive, 19(4):27.

Soklaridis, S., Kuper, A., Whitehead, C., Ferguson, G., Taylor, V., and Zahn, C. (2017). Gender bias in hospital leadership: a qualitative study on the experiences of women ceos. Journal of Health Organization and Management, 31(2).

Terrell, J., Kofink, A., Middleton, J., Rainear, C., MurphyHill, E., Parnin, C., and Stallings, J. (2017). Gender differences and bias in open source: Pull request acceptance of women versus men. PeerJ Computer Science, 3:e111.

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