Social Media Diffusion Modeling: Validating Social Media Signals for E-commerce Trend Prediction

Xiaolin Chen

doi:10.54254/2755-2721/2025.LD27248

Applied and Computational EngineeringOpen access

Social Media Diffusion Modeling: Validating Social Media Signals for E-commerce Trend Prediction

Research Article

Open Access

Social Media Diffusion Modeling: Validating Social Media Signals for E-commerce Trend Prediction

Xiaolin Chen ^1*

¹ Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai, China

^*Corresponding author: t330026022@mail.uic.edu.cn

Published on 24 September 2025

ACE Vol.184

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-307-9

ISBN (Online): 978-1-80590-308-6

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Abstract

In today's digital age, social media has become an indispensable link between consumers and brands. With the popularity of the Internet and the vigorous development of social media platforms, e-commerce enterprises have set their sights on this new and dynamic marketing field, hoping to enhance brand influence, expand user groups and ultimately achieve sales growth through the power of social media. This article systematically reviews the methods to build social media information diffusion model. Focus on prediction model, network analysis and time series modeling as well as compare the traditional modeling and deep learning method. This article also discusses Hawkes process; survival analysis and other time series modeling techniques apply scenarios. The performance of the model is evaluated through indicators such as computational complexity and interpretability, and a hybrid architecture as well as online learning mechanism are proposed to address the challenges of unsteady data. In addition, it emphasized future directions such as multimodal fusion, cross-platform generalization, and privacy protection, and called for the establishment of standardized tools and benchmark datasets to promote the development of the field.

Keywords:

Social media diffusion model, Long Short-Term Memory, Graph Neural Network, Hawkes process, Business intelligent

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References

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References

[1]. Guille, A., & Hacid, H. (2012). A predictive model for the temporal dynamics of information diffusion in online social networks. WWW 2012 – MŚND’12 Workshop, 1145-1152.

[2]. Rotabi, R., & Kleinberg, J. (2016). The Status Gradient of Trends in Social Media. arXiv.Org.

[6]. Trappey et al. (2018). Consumer driven product technology function deployment using social media and patent mining. Advanced Engineering Informatics 36, 120–129.

[8]. Wang et al. (2018). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications 29, 142–156.

[10]. Cadwalladr, C., & Graham-Harrison, E. (2018). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica. The Guardian.

[11]. Abadi, M., et al. (2016). Deep Learning with Differential Privacy. ACM CCS.

[13]. Mehrabi, N., et al. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys.

Cite this article

Chen,X. (2025). Social Media Diffusion Modeling: Validating Social Media Signals for E-commerce Trend Prediction. Applied and Computational Engineering,184,30-37.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.184

ISSN: 2755-2721(Print) / 2755-273X(Online)

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