4.5 Article

A novel approach based on rough set theory for analyzing information disorder

Journal

APPLIED INTELLIGENCE
Volume 53, Issue 12, Pages 15993-16014

Publisher

SPRINGER
DOI: 10.1007/s10489-022-04283-9

Keywords

Information disorder; Rough sets; Fuzzy rough sets

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The paper presents and evaluates an approach based on Rough Set Theory to analyze Information Disorder phenomena. Rough Set Theory concepts and constructs are used to model and reason on social media user groups and sets of information. Information theoretic measures are used to evaluate Complexity and Milestone concepts in Information Disorder. The adoption of Rough Set Theory constructs and operators in this new field allows for modeling and reasoning on key elements of Information Disorder and interpreting its effects.
The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to model and reason on groups of social media users and sets of information that circulate in the social media. Information theoretic measures, such as roughness and entropy, are used to evaluate two concepts, Complexity and Milestone, that have been borrowed by system theory and contextualized for Information Disorder. The novelty of the results presented in this paper relates to the adoption of Rough Set Theory constructs and operators in this new and unexplored field of investigation and, specifically, to model key elements of Information Disorder, such as the message and the interpreters, and reason on the evolutionary dynamics of these elements. The added value of using these measures is an increase in the ability to interpret the effects of Information Disorder, due to the circulation of news, as the ratio between the cardinality of lower and upper approximations of a Rough Set, cardinality variations of parts, increase in their fragmentation or cohesion. Such improved interpretative ability can be beneficial to social media analysts and providers. Four algorithms based on Rough Set Theory and some variants or extensions are used to evaluate the results in a case study built with real data used to contrast disinformation for COVID-19. The achieved results allow to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of our phenomenon.

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