A Review of Natural Language Processing for Social Media-Based Public Health Surveillance and Analytics

Maryann Inimfon Atakpa, Toyosi O Abolaji and Bisola Akeju

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 6, 1030-1060

Abstract: Social media platforms generate vast volumes of user-generated text reflecting public health attitudes, behaviours, misinformation exposure, and health event responses in near real time. This paper presents a comprehensive review of natural language processing approaches for social media-based public health surveillance across five domains: influenza and infectious disease surveillance, vaccine sentiment monitoring, mental health signal extraction, opioid crisis surveillance, and health policy discourse analysis. Lexicon-based methods including VADER and TextBlob, classical machine learning classifiers with TF-IDF feature representations, and transformer-based models including BioBERT and ClinicalBERT are systematically evaluated. Transformer-based models consistently outperform classical methods by 8 to 15 percentage points on domain-specific health text classification benchmarks. A responsible social media health surveillance framework integrating UK GDPR data governance and NHS information governance is proposed, with a comparative NLP performance table.

Keywords: NLP; natural language processing; public health surveillance; social media; sentiment analysis; VADER; BERT; pharmacovigilance; health informatics; Twitter (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT23906783
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i6:id:hcseit23906783

DOI: 10.32628/CSEIT23906783

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