Measuring and Fostering Peace through Machine Learning and Artificial Intelligence

2026-01-08

arXiv.org

AI Summary

This study addresses the challenge of objectively measuring national-level peace and mitigating the distorting influence of inflammatory sentiment on social media that skews public perception. To this end, the authors propose a novel approach integrating neural networks and large language models to quantify dimensions of “peacefulness” from multi-source news and social media texts. They implement this framework in MirrorMirror, a Chrome extension that delivers real-time peace assessments for YouTube videos. The method achieves high accuracy across diverse news datasets and represents the first integration of large language models with fine-grained emotion analysis (GoEmotions) for constructing peace indicators. By shifting media consumption metrics from click-driven engagement toward societal impact evaluation, this work provides the public with an actionable tool for cultivating informed peace awareness.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Security and Privacy: Large-scale security measurements

Abstract

We used machine learning and artificial intelligence: 1) to measure levels of peace in countries from news and social media and 2) to develop on-line tools that promote peace by helping users better understand their own media diet. For news media, we used neural networks to measure levels of peace from text embeddings of on-line news sources. The model, trained on one news media dataset also showed high accuracy when used to analyze a different news dataset. For social media, such as YouTube, we developed other models to measure levels of social dimensions important in peace using word level (GoEmotions) and context level (Large Language Model) methods. To promote peace, we note that 71% of people 20-40 years old daily view most of their news through short videos on social media. Content creators of these videos are biased towards creating videos with emotional activation, making you angry to engage you, to increase clicks. We developed and tested a Chrome extension, MirrorMirror, which provides real-time feedback to YouTube viewers about the peacefulness of the media they are watching. Our long term goal is for MirrorMirror to evolve into an open-source tool for content creators, journalists, researchers, platforms, and individual users to better understand the tone of their media creation and consumption and its effects on viewers. Moving beyond simple engagement metrics, we hope to encourage more respectful, nuanced, and informative communication.

Problem

• peace measurement
• machine learning
• artificial intelligence
• media diet
• social media

Innovation

• machine learning
• peace measurement
• social media analysis
• real-time feedback tool
• large language models