A Survey of Information Dissemination Model, Datasets, and Insight

Yanchao Liu, Pengzhou Zhang, Lei Shi and Junpeng Gong ()
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Yanchao Liu: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
Pengzhou Zhang: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
Lei Shi: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
Junpeng Gong: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China

Mathematics, 2023, vol. 11, issue 17, 1-30

Abstract: Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the latest models or methods. In this paper, we review information dissemination models from the past three years and conduct a detailed analysis, such as explanatory and predictive models. Moreover, we provide public datasets, evaluation metrics, and interface tools for researchers focusing more on algorithm design and modeling. Finally, we discuss the model application and future research directions. This paper aims to understand better the research progress and development trends for beginners and guide future research endeavors. We believe this article will attract more researchers’ interest and attention to the information dissemination field on social networks.

Keywords: information dissemination; explanatory model; predictive model; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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