A Model for Identifying and Examining Fake News in Digital Journalism from the Perspective of Experts and Specialists in Iraq

Abstract

This study aimed to develop and validate a comprehensive model for identifying and examining fake news in digital journalism based on the perspectives of experts and specialists in Iraq. The study adopted a qualitative approach using content analysis and meta-synthesis. The methodological procedure was based on a systematic meta-synthesis framework through which relevant studies were searched, screened, critically evaluated, and synthesized. Initial concepts related to fake-news identification were extracted through open coding and subsequently organized into broader categories through axial coding. Fuzzy Delphi analysis was then applied to refine and validate the extracted indicators. Twelve qualified academic and professional experts were purposively selected according to theoretical adequacy and relevant expertise. The experts evaluated the indicators using a fuzzy rating scale, after which fuzzy values were defuzzified. Indicators exceeding the predefined threshold of 0.50 were retained, while the iterative expert-evaluation process was used to increase convergence and establish the final framework. The meta-synthesis and fuzzy Delphi analyses supported the validity of the indicators incorporated into the proposed model. The final framework comprised 12 major categories: linguistic and content characteristics of news, source credibility and news gatekeeping, news dissemination patterns, key users and fake accounts, interactive user behavior, social-network analysis and dissemination pathways, algorithms and artificial intelligence, visual and multimedia content, media literacy and fake-news detection skills, audience sociocultural factors, fake-news countermeasures and prevention, and consequences and impacts of fake news. Fuzzy Delphi results supported retaining indicators meeting the required fuzzy-value criterion, while increased convergence across expert evaluations provided the basis for finalizing the proposed model. Fake-news identification in digital journalism requires an integrated, multidimensional approach incorporating content characteristics, source credibility, dissemination structures, user behavior, intelligent technologies, media literacy, and sociocultural context. The proposed model provides an applicable framework for journalists, media organizations, policymakers, and regulatory institutions in Iraq to develop more systematic mechanisms for detecting, controlling, and preventing the dissemination of fake news.