There has been a growing interest of research in false information, especially misinformation, disinformation, and fake news, largely due to their increased occurrence and impacts on public health and democratic processes. False information amplifies misperceptions and knowledge resistance among the public which entails consequences for citizens and society, such as organizations, countries, and the legitimacy of democracy itself, making it an appealing topic for research.
To approach this rapid expansion of literature as well as to form a comprehensive overview, this research assesses and conceptualizes the construct of misinformation, disinformation, and fake news to identify main topics and research areas within the abstract of research articles (n = 5,126). For that, this review systematically explores the state of literature by employing an automated text analysis using the Latent Dirichlet Allocation (LDA) method to classify thematic groupings.
The findings indicate that research topics include the dissemination and reception of misinformation, disinformation, and fake news, along with related concepts of falsehood and research methodologies employed in the field. More research should focus on the detection of false information, comparative analyses across media, and investigation of the effectiveness of countermeasures. Overall, this work demonstrates the potential of computational methods in literature reviews as an effective tool to gain insights across an extensive body of research and advocates for a broader adoption and methodological standardisation in future research.