logo
ResearchBunny Logo
A Conceptual Model for Fake News Risk Management in Digital Communities

Interdisciplinary Studies

A Conceptual Model for Fake News Risk Management in Digital Communities

A. 1. Name and A. 2. Name

This paper introduces a groundbreaking conceptual model designed to navigate the treacherous waters of fake news in digital communities. Developed by experienced researchers, it aims to empower law enforcement and stakeholders in their battle against misinformation, offering insights drawn from a systematic literature review and a practical case study from the CEDMO archive.

00:00
00:00
Playback language: English
Introduction
The proliferation of fake news (FN) online poses a significant threat to individuals, organizations, and societies. FN, encompassing misinformation and disinformation, erodes public trust, influences decision-making, and damages reputations. The impact of FN is evident in major political events like Brexit and the 2016 US presidential election. This research addresses the need for a comprehensive framework to understand and manage the digital risk presented by FN. A systematic literature review (SLR) was conducted to map out the concepts related to FN and digital risk, providing a foundation for the development of a conceptual model. The model is designed to clarify the various definitions of FN and its components for stakeholders, particularly law enforcement, enabling better mitigation strategies. The research employs Design Science Research methodology and leverages the ArchiMate modeling language to represent the community as an organization. A case study from the CEDMO archive is used to evaluate the model's applicability and robustness.
Literature Review
The SLR identified core concepts surrounding fake news, including misinformation (unintentionally spread false information), disinformation (intentionally spread false information), impact (the consequences of false information), context (circumstances surrounding a news item), agent (creator and spreader of FN), verifiability (ability to check accuracy), medium (platform of dissemination), event (occurrence related to FN), source (origin of FN), content (the information itself), and intention (purpose behind creating FN). The review highlighted the diverse terminology used to describe FN and the need for a unified conceptual framework.
Methodology
This research followed the Design Science Research (DSR) paradigm, with the central artifact being the conceptual model. The DSR methodology involved iteratively developing, instantiating, and evaluating the model. The ArchiMate language, a standard for enterprise architecture modeling, was chosen to represent the community as an organization. This allowed for a structured and comprehensive representation of the relationships between different aspects of the FN problem, such as the agents involved, the mediums used to spread the information, and the impact on the community. The model incorporates the key concepts identified in the SLR. A specific case of FN from the CEDMO archive, "BREAKING: COVID-19 Vaccine Can Cause Blindness," was selected to test the model's applicability and resilience in a real-world scenario. The evaluation of the model focused on aspects such as completeness, redundancy, and clarity, ensuring the model effectively captures the complexities of FN and its management. The ArchiMate model was developed using the Archi software tool.
Key Findings
The research resulted in a practical conceptual model for FN risk management, providing a structured approach to understanding and mitigating the impact of FN in digital communities. The model effectively integrates the key concepts identified in the SLR, including the various types of FN (misinformation, disinformation), the agents involved, the mediums used for dissemination, and the impact on the community. The model’s application to the CEDMO case study demonstrated its ability to represent the intricacies of a real-world FN incident. The evaluation of the model found it to be complete, free of redundancy, and easy to understand, thus indicating its robustness. The design decisions contributing to the model's robustness were highlighted. The model's value lies in its ability to assist law enforcement and other stakeholders in effectively identifying, understanding, and responding to FN threats within their communities.
Discussion
The developed conceptual model directly addresses the research question by providing a structured framework for understanding and managing FN risks. The model's significance lies in its applicability to various contexts and its potential to improve the effectiveness of FN mitigation strategies. By clarifying the relationships between different aspects of FN, the model offers a clearer understanding of the problem and enables the development of targeted interventions. The model's use of the ArchiMate language ensures its compatibility with existing enterprise architecture frameworks, facilitating integration with broader organizational risk management processes. The successful application of the model to the CEDMO case study validates its practicality and resilience.
Conclusion
This research has delivered a robust conceptual model for managing FN risks, addressing the need for a unified framework to understand and respond to the challenges posed by FN. The model’s clarity and structured approach will benefit law enforcement agencies, organizations, and policymakers in their efforts to mitigate the negative impacts of FN. Future work could involve applying the model to a wider range of case studies to further enhance its robustness and refine its applicability across different contexts. Exploration of how the model can integrate with existing technology and AI-driven solutions for FN detection and mitigation would also be beneficial.
Limitations
The reliability of this research is inherently linked to the accuracy and scope of the systematic literature review (SLR) conducted. The SLR's focus on academic literature might have excluded relevant insights from grey literature sources available online. A more comprehensive Multivocal Literature Review, incorporating both academic and grey literature, could enhance the model's comprehensiveness and generalizability. Future research should also explore the cultural and contextual nuances of FN, as the model's current applicability may vary across different cultural settings.
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny