Gender-based violence in 140 characters or fewer: A #BigData case study of Twitter

Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit Sheth


Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly 14 million tweets collected from Twitter over a period of 10 months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of computational social science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development.


computational social science; gender-based violence; social media; quantitative analysis; qualitative analysis; citizen sensing; public awareness; public attitude; policy; intervention campaign

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