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Misinformation on social platforms: A review and research Agenda

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  • Chaudhuri, Neha
  • Gupta, Gaurav
  • Bagherzadeh, Mehdi
  • Daim, Tugrul
  • Yalcin, Haydar

Abstract

Social platforms contribute to various hedonic and informational needs of their users. However, the unregulated, ubiquitous, and unhindered nature of the platforms is intricately linked to the exponential rise of online misinformation. The complexities in governing misinformation, coupled with the growing distrust of users, have diminished its value proposition for all stakeholders. Hence, it is crucial for all stakeholders to examine the state of research in this domain. We aim to examine the core value-adding governance mechanisms through a systematic scoping literature review. We propose an overarching conceptual framework that integrates the drivers of governance mechanisms with the outcomes of effective misinformation governance. Additionally, the framework illustrates the moderation role of platform monitoring efficiency for effective governance. This study will be especially useful for platform designers and practitioners to identify avenues for introducing strategic interventions on their platform and for scholars to develop a holistic understanding of the overall knowledge base in this domain and thus identify existing gaps in the literature which are rich for future examination.

Suggested Citation

  • Chaudhuri, Neha & Gupta, Gaurav & Bagherzadeh, Mehdi & Daim, Tugrul & Yalcin, Haydar, 2024. "Misinformation on social platforms: A review and research Agenda," Technology in Society, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:teinso:v:78:y:2024:i:c:s0160791x24002021
    DOI: 10.1016/j.techsoc.2024.102654
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    References listed on IDEAS

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    2. Wang, Zheng & Liu, Huiran & Fan, Xiaojun, 2025. "Hybrid machine learning and MCDM framework for consumer preference extraction and decision support in dynamic markets," Technology in Society, Elsevier, vol. 82(C).

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