#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment

Tobias Bauer, Emre Devrim, Misha Glazunov, William Lopez Jaramillo, Balaganesh Mohan, Gerasimos Spanakis*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationECML PKDD 2019. Communications in Computer and Information Science
EditorsP. Cellier, K. Driessens
Place of PublicationCham
PublisherSpringer International Publishing
Pages503-521
Number of pages19
Volume1167
ISBN (Electronic)978-3-030-43823-4
ISBN (Print)978-3-030-43822-7
DOIs
Publication statusPublished - Mar 2020

Publication series

SeriesCommunications in Computer and Information Science
Volume1167
ISSN1865-0929

Keywords

  • Chatbots
  • Named entity recognition
  • Classification

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