News data have become an essential resource across various disciplines, including economics, finance, management, social sciences, and computer science. Researchers leverage newspaper articles to study economic trends, market dynamics, corporate strategies, public perception, political discourse, and the evolution of public opinion. Additionally, news datasets have been instrumental in training large-scale language models, with applications in sentiment analysis, fake news detection, and automated news summarization. Despite their significance, access to comprehensive news corpora remains a key challenge. Many full-text news providers, such as Factiva and LexisNexis, require costly subscriptions, while free alternatives often suffer from incomplete data and transparency issues. This paper presents a novel approach to obtaining full-text newspaper articles at near-zero cost by leveraging data from the Global Database of Events, Language, and Tone (GDELT). Specifically, we focus on the GDELT Web News NGrams 3.0 dataset, which provides high-frequency updates of n-grams extracted from global online news sources. We provide Python code to reconstruct full-text articles from these n-grams by identifying overlapping textual fragments and intelligently merging them. Our method enables researchers to access structured, large-scale newspaper data for text analysis while overcoming the limitations of existing proprietary datasets. The proposed approach enhances the accessibility of news data for empirical research, facilitating applications in economic forecasting, computational social science, and natural language processing.

本研究旨在解决获取全面新闻数据的困难，提出了一种利用GDELT数据重建完整新闻文本的新方法。通过提供Python代码，研究者可以将高频n-gram数据智能合并，克服现有数据集的限制，从而更便捷地进行经济预测、计算社会科学和自然语言处理等实证研究。

一种用于从GDELT重建完整新闻文本的Python工具