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.
4 affiliations
- 1Government College University, Lahore, Pakistan.
- 2Alfaisal University, Riyadh, Saudi Arabia.
- 3Prince Sultan University, Riyadh, Saudi Arabia.
- 4University of Salford, Salford, United Kingdom.
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.
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BibTeX
@article{shahzad2026data,
title = {{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.}},
author = {Khurram Shahzad and Asfa Muhammed Din Javeed and Shakil Ahmad and Mujahid Latif and Kashaf Saleem},
journal = {Frontiers in big data},
year = {2026},
month = oct,
doi = {10.3389/fdata.2026.1948398},
url = {https://europepmc.org/article/MED/42898224},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Khurram Shahzad
AU - Asfa Muhammed Din Javeed
AU - Shakil Ahmad
AU - Mujahid Latif
AU - Kashaf Saleem
PY - 2026
DA - 2026/10/05
JO - Frontiers in big data
DO - 10.3389/fdata.2026.1948398
UR - https://europepmc.org/article/MED/42898224
AB - 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.
SN - 2624-909X
ER -