# Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization

arXiv: https://arxiv.org/abs/2410.03749 · HTML: https://mlsanity.com/paper/2410.03749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning](https://mlsanity.com/paper/2305.12537/) (2023)

## Built on by (most cited first, 1 of 1)

- [AI Application Gives Users Real-Time Feedback on the Level of Peace in the Social Media Videos They Watch](https://mlsanity.com/paper/2601.05232/) (2026)

## Abstract (arXiv)

This paper presents a machine learning approach to classify countries as peaceful or non-peaceful using linguistic patterns extracted from global media articles. We employ vector embeddings and cosine similarity to develop a supervised classification model that effectively identifies peaceful countries. Additionally, we explore the impact of dataset size on model performance, investigating how shrinking the dataset influences classification accuracy. Our results highlight the challenges and opportunities associated with using large-scale text data for peace studies.