The use of disinformation and misinformation campaigns in the media has attracted much attention from academics and policy-makers. Multimodal analysis or the analysis of two or more semiotic systems-language, gestures, images, sounds, among others-in their interrelation and interaction is essential to understanding dis-/misinformation efforts because most human communication goes beyond just words. There is a confluence of many disciplines (e.g. computer science, linguistics, political science, communication studies) that are developing methods and analytical models of multimodal communication. This literature review brings research strands from these disciplines together, providing a map of the multi- and interdisciplinary landscape for multimodal analysis of dis-/misinformation. It records the substantial growth starting from the second quarter of 2020-the start of the COVID-19 epidemic in Western Europe-in the number of studies on multimodal dis-/misinformation coming from the field of computer science. The review examines that category of studies in more detail. Finally, the review identifies gaps in multimodal research on dis-/misinformation and suggests ways to bridge these gaps including future cross-disciplinary research directions. Our review provides scholars from different disciplines working on dis-/misinformation with a much needed bird's-eye view of the rapidly emerging research of multimodal dis-/misinformation.
Keywords: literature review; machine learning; multimodal dis-/misinformation; qualitative analysis.
© 2023 The Authors.