Neural Networks Measure Peace Levels from News Data similar to Peace Indices

TL;DR

This paper introduces a neural network-based framework analyzing news text to measure national peace levels, outperforming traditional socio-economic methods and correlating well with established peace indices.

Contribution

It presents a novel NLP approach using CNNs and advanced embeddings to quantify peace from news data, capturing subtle linguistic signals linked to societal stability.

Findings

Neural network model outperforms k-NN baseline in peace classification.

Model maintains strong correlation with Positive Peace Index across countries.

Linguistic features serve as robust indicators of societal peace levels.

Abstract

Traditional methods for assessing national peace levels typically rely on socio-economic indicators or conflict incidence, often overlooking the nuanced signals embedded in public discourse. This study presents a novel computational framework to quantify peace levels by analyzing the structural and stylistic features of news text, rather than solely its content. Using the News on the Web (NOW) corpus comprising articles from 20 countries, we evaluate the efficacy of advanced word embeddings managed via ChromaDB compared to standard Doc2Vec models. We propose a 1D Convolutional Neural Network (CNN) architecture for classification and regression tasks, contrasting its performance against a k-Nearest Neighbors (k-NN) baseline. Our results demonstrate that the Neural Network significantly outperforms the k-NN model in classification metrics and, crucially, preserves the numerical relationship of peace rankings, exhibiting a strong correlation with the Positive Peace Index (PPI) even for out-of-sample countries. These findings suggest that the how of communication - the latent linguistic structures - serves as a robust, emergent indicator of societal stability. This research offers a non-invasive, scalable tool for real-time monitoring of social and societal dynamics and peacebuilding efforts.