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Towards Automated Sexual Violence Report Tracking

Authors

Naeemul Hassan,Amrit Poudel,Jason Hale,Claire Hubacek,Khandaker Tasnim Huq,Shubhra Kanti Karmaker Santu,Syed Ishtiaque Ahmed

University of Maryland, College Park,University of Mississippi,University of Mississippi,University of Mississippi,Khulna University of Engineering and Technology,Massachusetts Institute of Technology,University of Toronto


Proceedings:

Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media

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Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media

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Abstract:

Warning: This paper may contain trigger words that might be uncomfortable to some readers. 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.

DOI:

10.1609/icwsm.v14i1.7296


ICWSM

Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media