#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
This addresses the problem of under-reporting and lack of support for sexual harassment survivors, though it is an incremental application of existing NLP methods to a new domain.
The authors built a chatbot to assist survivors of sexual harassment by identifying harassment cases with over 98% accuracy and extracting spatio-temporal information with up to 90% accuracy, aiming to direct survivors to appropriate help and document under-reported incidents.
Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended). The motivation behind this work is twofold: properly assist survivors of such events by directing them to appropriate institutions that can offer them help and increase the incident documentation so as to gather more data about harassment cases which are currently under reported. We break down the problem into three data science/machine learning components: harassment type identification (treated as a classification problem), spatio-temporal information extraction (treated as Named Entity Recognition problem) and dialogue with the users (treated as a slot-filling based chatbot). We are able to achieve a success rate of more than 98% for the identification of a harassment-or-not case and around 80% for the specific type harassment identification. Locations and dates are identified with more than 90% accuracy and time occurrences prove more challenging with almost 80%. Finally, initial validation of the chatbot shows great potential for the further development and deployment of such a beneficial for the whole society tool.