用GPT+检索增强生成分析北约团结度的舆论趋势。
AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity
- 结合检索增强生成与GPT-4.1，对新闻、视频评论和Reddit讨论进行多源分析。
- 发现北约团结相关意见评分呈下降趋势，且可量化分析其不确定性。
- 适合关注舆情建模与AI辅助决策的研究者或政策分析人员。
本文研究基于GPT模型与检索增强生成（RAG）技术，对不同网络来源（谷歌搜索获取的新闻网站、带评论的YouTube视频、Reddit讨论）中的北约团结、北约团结度及《北约条约》第5条信任度等议题进行定性与定量分析。采用GPT-4.1模型，分两级实施RAG分析：第一级通过零样本提示生成新闻摘要与量化意见评分；第二级对摘要再生成总结。使用贝叶斯回归分析生成的量化意见评分，获得趋势线并评估其参数分布所体现的不确定性。结果表明，北约团结相关意见评分呈现下行趋势。该方法不旨在进行直接政治研判，而是探索可用于复杂分析流程中的AI辅助工具。研究还引入基于神经常微分方程的动态模型，支持对公众舆论演变的不同情景分析。结果表明，利用GPT模型进行新闻分析可提供具有信息量的定性与定量洞察。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。