Sounds of Silence Breakers: Exploring Sexual Violence on Twitter

摘要

Gender-based-violence is a serious concern in recent times. Due to the social stigma attached to these assaults, victims rarely come forward. Implementing policy measures to prevent sexual violence get constrained due to lack of crime statistics. However, the recent outcry on the Twitter platform allows us to address this concern. Sexual assaults occur at workplaces, public places, educational institutes and also at home. Policy level approaches and awareness campaign for these assaults would not be similar. So, we want to identify the risk factor associated with these sexual assaults. We extracted 0.7 million tweets during the #MeToo social media movement. Next, we employ deep learning techniques to classify these sexual violences. We observe that sexual assaults by a family member at own home is a more serious concern than harassment by a stranger at public places. This study reveals assaults by a known person are more prevalent than assaults by unknown strangers.