Minority report detection in refugee-authored community-driven journalism using RBMs

Autor:	Rakova, Bogdana, DePalma, Nick
Rok vydání:	2019
Předmět:	Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 2 figures, In the Proceedings of the AI for Social Good NeurIPS 2018 Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.04953 Zobrazit plný text záznamu View this record from Arxiv