Leveraging Geo-NLP for Enhanced Antiretroviral Drug Distribution in Nigeria: Insights from Social Media and News Data

Abstract

Faced with over 1.9 million HIV/AIDS cases, Nigeria’s need for efficient antiretroviral therapy (ART) distribution is critical. Conventional assessment methods, restrained by logistical issues and data scarcity, require innovative solutions. This study employs Geographic Natural Language Processing (Geo-NLP) to analyse social media and news content, offering novel insights into public discourse on HIV/AIDS and ART across Nigeria. Using a custom Named-Entity Recognition (NER) model to process data from NairaLand and major newspapers, the research uncovers geographical patterns in HIV/AIDS-related conversations, achieving a significant model performance with an overall F1-Score of 83.27. The findings highlight areas with intense discussions on HIV/AIDS, suggesting urban centres like Bauchi, Jos, and Ibadan as priority sites for targeted ART interventions. This approach promises to refine ART distribution strategies and sets a precedent for employing Geo-NLP in public health planning. Despite its brevity, the study underscores the potential of integrating Geo-NLP with traditional data to enhance healthcare delivery in Nigeria, paving the way for more effective public health interventions against the HIV/AIDS epidemic.