Towards Automated Sexual Violence Report Tracking

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

Hybrid machine learning methods for risk assessment in gender-based crime

Gender-based crime is one of the most concerning scourges of contemporary society, and governments worldwide have invested lots of economic and human resources to foretell their occurrence and anticipate the aggressions. In this work, we propose to apply Machine Learning (ML) techniques to create models that accurately predict the recidivism risk of a gender-violence offender. We feed the model with data extracted from the official Spanish VioGen system and comprising more than 40,000 reports of gender violence. To evaluate the performance, two new quality measures are proposed to assess the effective police protection that a model supplies and the overload in the invested resources that it generates. The empirical results show a clear outperformance of the ML-centered approach, with an improvement of up to a 25% with respect to the preexisting risk assessment system. Additionally, we propose a hybrid model that combines the statistical prediction methods with the ML method, permitting authorities to implement a smooth transition from the preexisting model to the ML-based model. To the best of our knowledge, this is the first work that achieves an effective ML-based prediction for this type of crimes against an official dataset

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https://www.ester.ee/record=b5360720*es

A human-centered systematic literature review of the computational approaches for online sexual risk detection

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.Accepted manuscrip