Studying visual disinformation: A computational literature review of current issues, trends and research methods of a growing field

The rise of visual mis/disinformation presents a significant challenge to democratic societies, as misleading visual content increasingly influences public opinion and decision-making. This study aims to comprehensively map the current state of research on visual mis/disinformation, providing insights into its key trends, disciplinary focus, methodologies and topical priorities. Using a computational literature review approach, this study identifies and synthesizes findings from a broad corpus of scholarly publications ( N = 286) examining trends over time in visual disinformation research, highlighting the growing prominence of the field and its interdisciplinary nature. We explore the specific contributions of disciplines such as political science, communication studies and computer science. The review identifies a diverse array of research methods used to investigate visual disinformation, ranging from traditional content analysis and surveys to cutting-edge computational techniques such as machine learning and visual network analysis. It also delves into the thematic priorities of recent studies, including ‘media literacy’, ‘verification strategies of visual misinformation’, ‘visual literacy in misinformation’, ‘correction strategies and engagement’, ‘health-related disinformation’, ‘disinformation detection using machine learning’, and ‘misinformation and memory’. By integrating these perspectives, the study provides a comprehensive overview of the visual mis/disinformation field, offering valuable insights for future research. It emphasizes the need for multidisciplinary collaboration and methodological innovation to address the complexities of this pressing issue. This work contributes to the broader understanding of visual mis/disinformation’s impact and the strategies needed to mitigate its harmful effects.