Predictability of information spreading on online social networks

Fanhui Meng, Jiarong Xie, Xiao Ma, Jinghui Wang and Yanqing Hu
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Fanhui Meng: School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
Jiarong Xie: Center for Computational Communication Research, Beijing Normal University, Zhuhai 519087, P. R. China3School of Journalism and Communication, Beijing Normal University, Beijing 100875, P. R. China
Xiao Ma: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, P. R. China
Jinghui Wang: Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, P. R. China
Yanqing Hu: Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, P. R. China†Center for Complex Flows and Soft Matter Research, Southern University of Science and Technology, Shenzhen 518055, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 01, 1-12

Abstract: With the rapid development of the mobile Internet, online social networks are playing an increasingly vital role in the dissemination of information. Accurately predicting the size of information cascades in advance has become a crucial issue, particularly in the realms of viral marketing, risk management and resource allocation. There are numerous studies that have tackled this prediction task, but the outcomes are unsatisfactory. In this paper, we explore the predictability of information cascade size through the lens of percolation theory. Our investigation reveals that the accuracy of cascade size prediction is notably diminished in the proximity of the threshold, evident in both artificial and empirical networks. Moreover, we observe a degradation and an user-level difference in prediction performance as social media platforms undergo evolution. Our findings underscore the necessity for additional factors to enhance prediction accuracy.

Keywords: Social networks; information spreading; site percolation; predictability (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124501699

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