Study: AI reveals everyday language—not peace terms—defines peaceful societies

The language of peace may be better expressed in terms of one’s hobbies and interests, not diplomatic jargon like “truce” or “ceasefire.”

Artificial intelligence is finding unexpected applications in peace research, with a new Columbia University study revealing how machine learning can measure societal peace through news language analysis. This innovative approach challenges traditional peace metrics by identifying surprising linguistic patterns: rather than focusing on direct peace terminology, AI discovered that news from peaceful nations tends to emphasize everyday life, diverse viewpoints, and community, while less peaceful countries’ media fixates on government, politics, and formal power structures. This breakthrough suggests peace may be better understood through the prominence of ordinary life rather than the absence of conflict.

The big picture: Researchers leveraged AI to analyze language patterns in news media across countries with varying peace levels, revealing counterintuitive insights about what characterizes peaceful societies.

• The machine learning algorithms examined over 700,000 articles from nine high-peace and nine low-peace countries, uncovering patterns that human researchers failed to predict.
• Rather than finding terms like “harmony” or “cooperation” in peaceful nations, the AI discovered that news focusing on daily life—work, family, creativity, and community—was the stronger indicator of peaceful societies.

Why this matters: The AI-powered “peace index” developed through this research strongly correlates with traditional peace measures but could potentially monitor societal peace in real-time rather than annually.

• This technology demonstrates how AI can be used constructively to understand complex social dynamics rather than promoting conflict or division.
• The research challenges conventional understanding of peace, suggesting it manifests more through societies where everyday concerns take precedence over political control.

Key findings: News media in peaceful countries features more diverse, informal language reflecting comfort with multiple viewpoints, while less peaceful nations’ news is dominated by terms related to government and control.

• The United States emerged as an outlier, scoring high on traditional peace measures but showing linguistic patterns more typical of less peaceful nations in its news coverage.
• Researchers found that expert hypotheses about which words would differentiate peaceful from non-peaceful countries were largely incorrect, demonstrating the value of AI’s unbiased pattern recognition.

Behind the methodology: The team employed a “data-driven” rather than “hypothesis-driven” approach, allowing the AI to discover patterns without being limited by predetermined theories.

• This methodology helped overcome human biases and preconceptions about what constitutes peaceful societies.
• The research suggests that meaningful indicators of peace might be found in subtle linguistic patterns rather than obvious peace-related terminology.