Towards Automated Sexual Violence Report Tracking
This work addresses the challenge of scalable and reliable sexual violence monitoring for social and policy applications, though it is incremental as it builds on existing supervised learning methods.
The paper tackles the problem of tracking sexual violence reports by developing a supervised learning model that achieves 80.4% precision and 83.4% recall, and applies it to analyze data from the #MeToo movement.
Tracking sexual violence is a challenging task. In this paper, we present a supervised learning-based automated sexual violence report tracking model that is more scalable, and reliable than its crowdsource based counterparts. We define the sexual violence report tracking problem by considering victim, perpetrator contexts and the nature of the violence. We find that our model could identify sexual violence reports with a precision and recall of 80.4% and 83.4%, respectively. Moreover, we also applied the model during and after the \#MeToo movement. Several interesting findings are discovered which are not easily identifiable from a shallow analysis.