2019/09/06 by Tobias Bauer, Bauer, Tobias, Emre Devrim +9 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Authorship Attribution and Profiling #Computation and Language (cs.CL) #Cybercrime and Law Enforcement Studies #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection
paper · pdf · doi:10.48550/arxiv.1909.02809
openalex publication_date 2019/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Inspired by the recent social movement of #MeToo, we are building a chatbot\nto assist survivors of sexual harassment cases (designed for the city of\nMaastricht but can easily be extended). The motivation behind this work is\ntwofold: properly assist survivors of such events by directing them to\nappropriate institutions that can offer them help and increase the incident\ndocumentation so as to gather more data about harassment cases which are\ncurrently under reported. We break down the problem into three data\nscience/machine learning components: harassment type identification (treated as\na classification problem), spatio-temporal information extraction (treated as\nNamed Entity Recognition problem) and dialogue with the users (treated as a\nslot-filling based chatbot). We are able to achieve a success rate of more than\n98% for the identification of a harassment-or-not case and around 80% for the\nspecific type harassment identification. Locations and dates are identified\nwith more than 90% accuracy and time occurrences prove more challenging with\nalmost 80%. Finally, initial validation of the chatbot shows great potential\nfor the further development and deployment of such a beneficial for the whole\nsociety tool.\n