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Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study

Eleana Jerez-Villota,Francisco Jurado,Jaime Moreno-Llorena

Published 2025 in IEEE Access

ABSTRACT

Online Social Networks (OSNs) have become a significant research focus across various fields. The increase in their use has prompted numerous studies, particularly on the complex Information Propagation (IP) process, which researchers have approached from different perspectives and lines of investigation. The work presented in this article aims to analyse the state of the art on IP in OSNs, mapping the models, methods, algorithms, tools, and techniques developed in this domain. In particular, we have conducted a Systematic Mapping Study (SMS). To our knowledge, this is the first study to address this issue. The SMS collected 424 studies and analysed 175 primary studies, and the results reveal that most studies are model proposals, the most researched topic is Influence Maximisation (IM), and Twitter (now X) is the most commonly used resource in experiments. Also, the SMS reveals that there is no formal classification of the terms to refer to propagated information. In addition, we also found several proposals to mitigate or control IP. However, there is no common methodological framework to reduce IP. To conclude the study, we propose groups of features/attributes of users during IP and a propagated information classification. This research provides a general and organised overview for the scientific community regarding studies on IP in OSNs.

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