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Related Experiment Video

Updated: Jun 2, 2026

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Human rights violations reporting dataset.

Constantinos Djouvas¹, Nikandros Ioannidis¹, Iosif Kovras²

¹Cyprus University of Technology, 30 Archbishop Kyprianos Street, 3036 Limassol, Cyprus, P.O. Box: 50329 3603 Limassol, Cyprus.

|June 1, 2026

Summary

This summary is machine-generated.

This study introduces a new dataset for human rights violation reporting, enabling detailed computational analysis of primary organizational documents. It facilitates fine-grained research into how human rights organizations document violations.

Keywords:

Computational social science Human rights violations Large language models Natural language processing Organizational reporting Political violence Text correction Text mining

Related Experiment Videos

Last Updated: Jun 2, 2026

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Area of Science:

Computational Social Science
Human Rights Research
Natural Language Processing

Background:

Existing datasets limit fine-grained analysis of primary human rights reports.
Current data often aggregates at document or event levels, or relies on news sources.

Purpose of the Study:

To introduce the Human Rights Violation Reporting Dataset, a novel paragraph-level corpus.
To enable fine-grained computational analysis of human rights documentation.
To facilitate new research avenues in entity network analysis, topic modeling, and machine learning applications.

Main Methods:

Developed a custom pipeline for PDF extraction and data crawling.
Utilized language model-based text correction to reduce character-level errors.
Applied comprehensive Natural Language Processing (NLP) enrichment, including NER, sentiment analysis, and classification.

Main Results:

Created a dataset of 832,220 paragraphs from major human rights organizations (1999-2023).
Reduced character-level errors from 8.3% to 1.7% through text correction.
Enriched each paragraph with 23 metadata fields for advanced analysis.

Conclusions:

The dataset enables unprecedented fine-grained computational analysis of human rights reporting.
Facilitates cross-organizational comparisons and temporal/spatial analysis of violation documentation.
Supports machine learning applications for deeper insights into human rights research.