{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Government College University, Lahore, Pakistan.",
    "Alfaisal University, Riyadh, Saudi Arabia.",
    "Prince Sultan University, Riyadh, Saudi Arabia.",
    "University of Salford, Salford, United Kingdom."
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Khurram Shahzad"
    },
    {
      "affs": [
        1
      ],
      "name": "Asfa Muhammed Din Javeed"
    },
    {
      "affs": [
        2
      ],
      "name": "Shakil Ahmad"
    },
    {
      "affs": [
        2
      ],
      "name": "Mujahid Latif"
    },
    {
      "affs": [
        3
      ],
      "name": "Kashaf Saleem"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7x2d273kc4wh6.bib",
  "cited_by": 0,
  "doi": "10.3389/fdata.2026.1948398",
  "fulltext_url": "https://europepmc.org/articles/PMC13677458",
  "id": "7x2d273kc4wh6",
  "issns": [
    "2624-909X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42898224",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3312|Misinformation and Its Impacts"
    ]
  ],
  "openalex": "W7219874428",
  "pdf_url": "https://europepmc.org/articles/PMC13677458?pdf=render",
  "pmcid": "PMC13677458",
  "pmid": "42898224",
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7x2d273kc4wh6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:37Z",
      "native_id": "MED:42898224",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42898224"
    }
  ],
  "subjects": [],
  "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.",
  "title_text": "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.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:37Z",
  "url": "https://research.draconitic.com/paper/7x2d273kc4wh6",
  "venue": "Frontiers in big data",
  "versions": []
}