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

arXiv: https://arxiv.org/abs/2306.17695 · HTML: https://mlsanity.com/paper/2306.17695/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [GraphIE: A Graph-Based Framework for Information Extraction](https://mlsanity.com/paper/1810.13083/) (2018)
- [One for All: Neural Joint Modeling of Entities and Events](https://mlsanity.com/paper/1812.00195/) (2018)
- [Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights](https://mlsanity.com/paper/1912.10819/) (2019)
- [Modeling Inter-Country Connection from Geotagged News Reports: A Time-Series Analysis](https://mlsanity.com/paper/1604.03647/) (2016)

## Built on by (most cited first, 4 of 4)

- [HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid](https://mlsanity.com/paper/2410.06370/) (2024)
- [Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLP](https://mlsanity.com/paper/2510.22823/) (2025)
- [Into the crossfire: evaluating the use of a language model to crowdsource gun violence reports](https://mlsanity.com/paper/2401.12989/) (2024)
- [CEHA: A Dataset of Conflict Events in the Horn of Africa](https://mlsanity.com/paper/2412.13511/) (2024)

## Abstract (arXiv)

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.