An approach to create time-series dataset for news propagation: Ukraine-war case study

An efficient technique to comprehend news spreading can be achieved through the automation of machine learning algorithms. These algorithms perform the prediction and forecasting of news dissemination across geographical barriers. Despite the fact that news regarding any events is generally recorded as a time-series due to its time stamps, it cannot be seen whether or not the news time-series is propagating across geographical barriers. In this article, we explore an approach for generating time-series datasets for news dissemination that relies on Chat-GPT and sentence-transformers. The lack of comprehensive, publicly accessible event-centric news databases for use in time-series forecasting and prediction is another limitation. To get over this bottleneck, we collected a news dataset consisting of 1 year and 3 months related to the Ukraine war using Event Registry. We also conduct a statistical analysis of different time-series (propagating, unsure, and not-propagating) of different lengths (2, 3, 4, 5, and 10) to document the prevalence of geographical barriers.