Identifying and Monitoring Human Rights Abuses Through Natural Language Processing
The goal of this two-month Omdena AI Challenge was to build an MVP for a monitoring tool for human rights abuses. The challenge partner Human Rights First is a non-profit, non-partisan international human rights organization with a track record of success in delivering change that has made a meaningful difference in people’s lives. Whether protecting refugees, combating torture, or defending persecuted minorities, they focus not on making a point, but on making a difference; for over 30 years.
The Problem: Human Rights Abuses in Social Media
With the evolution of social media in the last decade, the world’s ability to communicate has increased exponentially. The abundance of data this communication generates provides an opportunity to evaluate social phenomena across the globe in near real-time. In particular, it can enable the identification of reported human rights abuses almost immediately. Timely identification can allow for timely aid or action from governments and civil society to prevent or reduce harm.
Human Rights First about the AI Challenge outcomes
The final Dashboard MVP
The solution is a real-time monitoring system, aimed at collecting and analyzing data from major social media platforms. This application, when complete, would collect text data from Twitter, Reddit, Instagram, and Telegram and use an ensemble of language models to identify text likely representative of human rights abuses.
For example, various applications of BERT-based models can cluster, label, and compare text to “ground truth” representing “human rights abuses” in writing.
The intent is that this application would, at a minimum, identify, characterize and visualize human rights abuses on a front-end dashboard to serve as an early warning system of more follow-on abuses.
Characterization, in this context, refers to both the identification of language indicative of human rights abuses as well as the delineation (with a qualitative foundation) of human rights abuses into clearly defined classes.
The intent of this effort is to produce an application with a back-end that can collect, store, categorize and visualize social media data at scale in real-time. The analytics involved will be aimed at extracting social media posts that describe, with confidence intervals, human rights abuses and calculates an associated “severity” score based on the overall frequency of unique incidents.
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