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

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: López, Alejandro Buitrago, Pastor-Galindo, Javier, Ruipérez-Valiente, José A.
Natura: Preprint
Pubblicazione: 2024
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866909223383728128
author López, Alejandro Buitrago
Pastor-Galindo, Javier
Ruipérez-Valiente, José A.
author_facet López, Alejandro Buitrago
Pastor-Galindo, Javier
Ruipérez-Valiente, José A.
contents The prevalence of misinformation and disinformation poses a significant challenge in today's digital landscape. That is why several methods and tools are proposed to analyze and understand these phenomena from a scientific perspective. To assess how the mis/disinformation is being conceptualized and evaluated in the literature, this paper surveys the existing frameworks, models and simulations of mis/disinformation dynamics by performing a systematic literature review up to 2023. After applying the PRISMA methodology, 57 research papers are inspected to determine (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 context where the mis/disinformation is studied, and (5) the validation of the proposed methods for understanding mis/disinformation. The main findings reveal a consistent essence definition of misinformation and disinformation across studies, with intent as the key distinguishing factor. Research predominantly uses social frameworks, epidemiological models, and belief updating simulations. These studies aim to estimate the effectiveness of mis/disinformation, primarily in health and politics. The preferred validation strategy is to compare methods with real-world data and statistics. Finally, this paper identifies current trends and open challenges in the mis/disinformation research field, providing recommendations for future work agenda.
format Preprint
id arxiv_https___arxiv_org_abs_2406_09343
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Frameworks, Modeling and Simulations of Misinformation and Disinformation: A Systematic Literature Review
López, Alejandro Buitrago
Pastor-Galindo, Javier
Ruipérez-Valiente, José A.
Social and Information Networks
The prevalence of misinformation and disinformation poses a significant challenge in today's digital landscape. That is why several methods and tools are proposed to analyze and understand these phenomena from a scientific perspective. To assess how the mis/disinformation is being conceptualized and evaluated in the literature, this paper surveys the existing frameworks, models and simulations of mis/disinformation dynamics by performing a systematic literature review up to 2023. After applying the PRISMA methodology, 57 research papers are inspected to determine (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 context where the mis/disinformation is studied, and (5) the validation of the proposed methods for understanding mis/disinformation. The main findings reveal a consistent essence definition of misinformation and disinformation across studies, with intent as the key distinguishing factor. Research predominantly uses social frameworks, epidemiological models, and belief updating simulations. These studies aim to estimate the effectiveness of mis/disinformation, primarily in health and politics. The preferred validation strategy is to compare methods with real-world data and statistics. Finally, this paper identifies current trends and open challenges in the mis/disinformation research field, providing recommendations for future work agenda.
title Frameworks, Modeling and Simulations of Misinformation and Disinformation: A Systematic Literature Review
topic Social and Information Networks
url https://arxiv.org/abs/2406.09343