Work

Big data analytics and sentiment analysis for health misinformation detection: a systematic literature review of influencing factors, implementation challenges and a data-driven adoption framework

Year: 2026

Type: review

Abstract: The study aimed to identify the factors influencing big data analytics (BDA) and sentiment analysis (SA) for health misinformation detection and associated challenges for the adoption of BDA and SA across health ecosystems. The systematic literature review (SLR) methodology was applied for addressing the study's objectives. Thirty most relevant seminal studies (peer-reviewed articles and conference proceedings) were retrieved from 11 digital platforms to conduct the SLR. Results of the study showed that advanced deep learning architectures, feature engineering, sentiment intelligence, real-time analytics, and human-centered verification were key factors that influenced big data analytics and sentiment analysis based health misinformation detection. It was also identified that annotation limitations, scalability constraints, domain adaptation challenges, ethical concerns, and sustainability challenges negatively impacted the adoption of big data and sentiment driven health misinformation detection systems. The study has developed a data-driven framework to effectively adopt and sustain big data analytics and sentiment analysis to detect health misinformation. Systematic review registration: https://osf.io/wjrm6 .

Source: Frontiers in Big Data

DOI: 10.3389/fdata.2026.1948398

Authors: Khurram Shahzad, Asfa Muhammed Din Javeed, Shakil Ahmad, Mujahid Latif, Kashaf Saleem

Institutions: Government College University, Lahore, Alfaisal University, Prince Sultan University, University of Salford

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,418