Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study
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 investig...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10960426/ |
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| author | Eleana Jerez-Villota Francisco Jurado Jaime Moreno-Llorena |
| author_facet | Eleana Jerez-Villota Francisco Jurado Jaime Moreno-Llorena |
| author_sort | Eleana Jerez-Villota |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-20d7fb220688487f8a4f68f37facf4fe |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-20d7fb220688487f8a4f68f37facf4fe2025-08-20T03:13:42ZengIEEEIEEE Access2169-35362025-01-0113691946923510.1109/ACCESS.2025.355876810960426Understanding Information Propagation in Online Social Networks: A Systematic Mapping StudyEleana Jerez-Villota0https://orcid.org/0000-0003-0937-0529Francisco Jurado1https://orcid.org/0000-0001-7559-8236Jaime Moreno-Llorena2https://orcid.org/0000-0003-2918-5259Departamento de Ciencias de la Computación, Universidad de las Fuerzas Armadas ESPE, Sangolquí, EcuadorDepartamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, SpainDepartamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, SpainOnline 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.https://ieeexplore.ieee.org/document/10960426/Systematic mapping studyinformation propagationonline social networkclassification of propagated informationclassification of user features in information propagation |
| spellingShingle | Eleana Jerez-Villota Francisco Jurado Jaime Moreno-Llorena Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study IEEE Access Systematic mapping study information propagation online social network classification of propagated information classification of user features in information propagation |
| title | Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study |
| title_full | Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study |
| title_fullStr | Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study |
| title_full_unstemmed | Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study |
| title_short | Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study |
| title_sort | understanding information propagation in online social networks a systematic mapping study |
| topic | Systematic mapping study information propagation online social network classification of propagated information classification of user features in information propagation |
| url | https://ieeexplore.ieee.org/document/10960426/ |
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