TY - JOUR AU - Hassan, Naeemul AU - Poudel, Amrit AU - Hale, Jason AU - Hubacek, Claire AU - Huq, Khandaker Tasnim AU - Karmaker Santu, Shubhra Kanti AU - Ahmed, Syed Ishtiaque PY - 2020/05/26 Y2 - 2024/03/28 TI - Towards Automated Sexual Violence Report Tracking JF - Proceedings of the International AAAI Conference on Web and Social Media JA - ICWSM VL - 14 IS - 1 SE - Full Papers DO - 10.1609/icwsm.v14i1.7296 UR - https://ojs.aaai.org/index.php/ICWSM/article/view/7296 SP - 250-259 AB - <p><em>Warning: This paper may contain trigger words that might be uncomfortable to some readers.</em> 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.</p> ER -