AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity
Bohdan M. Pavlyshenko
TL;DR
The paper addresses how GPT-based retrieval-augmented analysis can produce qualitative and quantitative insights about NATO unity and Article 5 trust from diverse online sources. It proposes a two-level RAG pipeline using GPT-4.1 to extract and summarize content from Google-sourced web pages, YouTube, and Reddit, followed by a synthesis of those summaries. Quantitative signals are modeled with Bayesian regression, yielding posterior distributions for trend parameters, and a Neural Ordinary Differential Equations framework models evolving opinions under external impulses via $\frac{dx}{dt} = a x (1 - x^2) - b x + c E$ and $\frac{dE}{dt} = -d E + I(t)$ with learned RHS functions. Findings indicate a downward trend in NATO unity/opinion scores and Article 5 trust, and the work demonstrates the potential of AI-based news analytics as part of broader analytical workflows, while recognizing limitations and the need for cautious interpretation.
Abstract
The paper considers the use of GPT models with retrieval-augmented generation (RAG) for qualitative and quantitative analytics on NATO sentiments, NATO unity and NATO Article 5 trust opinion scores in different web sources: news sites found via Google Search API, Youtube videos with comments, and Reddit discussions. A RAG approach using GPT-4.1 model was applied to analyse news where NATO related topics were discussed. Two levels of RAG analytics were used: on the first level, the GPT model generates qualitative news summaries and quantitative opinion scores using zero-shot prompts; on the second level, the GPT model generates the summary of news summaries. Quantitative news opinion scores generated by the GPT model were analysed using Bayesian regression to get trend lines. The distributions found for the regression parameters make it possible to analyse an uncertainty in specified news opinion score trends. Obtained results show a downward trend for analysed scores of opinion related to NATO unity. This approach does not aim to conduct real political analysis; rather, it consider AI based approaches which can be used for further analytics as a part of a complex analytical approach. The obtained results demonstrate that the use of GPT models for news analysis can give informative qualitative and quantitative analytics, providing important insights. The dynamic model based on neural ordinary differential equations was considered for modelling public opinions. This approach makes it possible to analyse different scenarios for evolving public opinions.