  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 fourteen million tweets collected from Twitter over a
period of ten 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.
