[2603.00787] Identifying the Geographic Foci of US Local News

Computer Science > Machine Learning arXiv:2603.00787 (cs) [Submitted on 28 Feb 2026] Title:Identifying the Geographic Foci of US Local News Authors:Gangani Ariyarathne, Isuru Ariyarathne, Greatness Emmanuel-King, Kate Lawal, Alexander C. Nwala View a PDF of the paper titled Identifying the Geographic Foci of US Local News, by Gangani Ariyarathne and 4 other authors View PDF HTML (experimental) Abstract:Local journalism is vital in democratic societies where it informs people about local issues like, school board elections, small businesses, local health services, etc. But mounting economic pressures have made it increasingly difficult for local news stations to report these issues, underscoring the need to identify the salient geographical locations covered in local news (geo-foci). In response, we propose a novel geo-foci model for labeling US local news articles with the geographic locations (i.e., the names of counties, cities, states, countries) central to their subject matter. First, we manually labeled US local news articles from all 50 states with four administrative division labels (local, state, national, and international) corresponding to their geo-foci, and none for articles without a geographic focus. Second, we extracted and disambiguated geographic locations from them using Large Language Models (LLMs), since local news often contains ambiguous geographic entities (e.g., Paris, Texas vs. Paris, France). LLMs outperformed all eight geographic entity disambigu...