Frameworks, Modeling and Simulations of Misinformation and Disinformation: A Systematic Literature Review

Abstract

Misinformation and disinformation pose a significant challenge in today’s digital landscape. To examine how these phenomena are conceptualized and evaluated, this systematic literature review analyzes frameworks, models, and simulations used to represent mis/disinformation processes and dynamics. It covers 115 studies published through 2024 and retrieved from five bibliographic sources. Following the PRISMA methodology, the studies were analyzed to identify (1) the terminology and definitions of mis/disinformation, (2) the methods used to represent mis/disinformation, (3) the primary purpose beyond modeling and simulating mis/disinformation, (4) the application contexts in which mis/disinformation is studied, and (5) the strategies used to validate these approaches for understanding mis/disinformation. The findings show a broadly consistent definition of misinformation and disinformation across studies, with intent as the key distinguishing factor. Among the reviewed studies, social frameworks and epidemiological models are the most common approaches. Belief updating and mis/disinformation diffusion are the most frequent simulation types. The approaches are mainly used to conceptualize mis/disinformation and evaluate its effects or related countermeasures. Health and politics are the most common application contexts. Among validated approaches, comparison with real-world data is the most frequently reported strategy. Finally, this paper identifies current trends and open challenges and provides recommendations for future work.

The authors' abstract, as published at the source. ACM Computing Surveys, 2026 · DOI ↗