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Tracking sexual violence is a challenging task. In this paper, we present asupervised learning-based automated sexual violence report tracking model thatis more scalable, and reliable than its crowdsource based counterparts. Wedefine the sexual violence report tracking problem by considering victim,perpetrator contexts and the nature of the violence. We find that our modelcould identify sexual violence reports with a precision and recall of 80.4% and83.4%, respectively. Moreover, we also applied the model during and after the\#MeToo movement. Several interesting findings are discovered which are noteasily identifiable from a shallow analysis.

Towards Automated Sexual Violence Report Tracking