Minority report detection in refugee-authored community-driven journalism using RBMs
Abstract
Our work seeks to gather and distribute sensitive information from refugee settlements to stakeholders to help shape policy and help guide action networks. In this paper, we propose the following 1) a method of data collection through stakeholder organizations experienced in working with displaced and refugee communities, 2) a method of topic modeling based on Deep Boltzmann Machines that identifies topics and issues of interest within the population, to help enable mapping of human rights violations, and 3) a secondary analysis component that will use the probability of fit to isolate minority reports within these stories using anomaly detection techniques.
Keywords
Cite
@article{arxiv.1912.04953,
title = {Minority report detection in refugee-authored community-driven journalism using RBMs},
author = {Bogdana Rakova and Nick DePalma},
journal= {arXiv preprint arXiv:1912.04953},
year = {2019}
}
Comments
5 pages, 2 figures, In the Proceedings of the AI for Social Good NeurIPS 2018 Workshop