A New Task and Dataset on Detecting Attacks on Human Rights Defenders

TL;DR

This paper introduces a new dataset and task for detecting attacks on human rights defenders using NLP, enabling better analysis of such events through news article processing.

Contribution

It presents a novel dataset with detailed annotations on attacks on human rights defenders and demonstrates baseline models for related detection tasks.

Findings

Dataset contains 500 annotated news articles.

Baseline models achieve promising results on attack detection.

Dataset supports various sub-tasks like attack type and location prediction.

Abstract

The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events. We hypothesize that NLP can support such efforts by quickly processing large collections of news articles to detect and summarize the characteristics of attacks on human rights defenders. To that end, we propose a new dataset for detecting Attacks on Human Rights Defenders (HRDsAttack) consisting of crowdsourced annotations on 500 online news articles. The annotations include fine-grained information about the type and location of the attacks, as well as information about the victim(s). We demonstrate the usefulness of the dataset by using it to train and evaluate baseline models on several sub-tasks to predict the annotated characteristics.