Leveraging Geo-Nlp for Enhanced Antiretroviral Drug Distribution in Nigeria: Insights from Social Media and News Data
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Date
2024-11-25
Authors
Bashirudeen O.I.
Olubayo A.
Anthony S.
Ahmad I.I.
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Publisher
MIRG
Abstract
Faced with over 1.9 million HIV/AIDS cases, Nigeria’s need for efficient antiretroviral therapy (ART) distribution is crucial as conventional drug distribution methods are restrained by logistical issues and data scarcity and thus 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%. Other analysis done scraopped text data spotlights areas with more discussions on HIV/AIDS, suggesting areas like Bauchi, Jos, and Ibadan as priority sites for targeted ART distribution interventions. This approach aims 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 a more effective public health interventions against the HIV/AIDS epidemic.
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Citation
Bashirudeen O. I., Olubayo A., Anthony S. & Ahmad I. I. (2024). “Leveraging Geo-Nlp for Enhanced Antiretroviral Drug Distribution in Nigeria: Insights from Social Media and News Data". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024), pp. 13-21, MIRG