Article: Social media sentiment diffusion modelling algorithms for brand communication Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.37 pp.91 - 105 Abstract: Addressing the challenge of accurately predicting and guiding the spread of brand sentiment on social media, this study proposes an analytical framework that integrates dynamic communication modelling with intelligent algorithms. A two-layer coupled communication model (DI-SCIR) is constructed to quantify the migration patterns of sentiment by integrating users' time-varying behaviour and cross-platform interaction mechanisms. A three-dimensional influence strength measurement method (WSD-Rank) is designed to identify key communication nodes based on coverage, timeliness, and forwarding depth. AI clustering algorithms are combined to uncover primary diffusion pathways and generative intervention strategies are developed to achieve sentiment guidance and risk warning. Empirical verification shows that this method achieves an accuracy rate of 89.2% in brand sentiment prediction, providing effective theoretical modelling and algorithmic support for risk management and strategy optimisation in brand communication. Inderscience Publishers - linking academia, business and industry through research

Title: Social media sentiment diffusion modelling algorithms for brand communication

Authors: Juntao Xie

Addresses: School of Digital Communication, Guangzhou Huashang College, Guangzhou, 510000, China

Abstract: Addressing the challenge of accurately predicting and guiding the spread of brand sentiment on social media, this study proposes an analytical framework that integrates dynamic communication modelling with intelligent algorithms. A two-layer coupled communication model (DI-SCIR) is constructed to quantify the migration patterns of sentiment by integrating users' time-varying behaviour and cross-platform interaction mechanisms. A three-dimensional influence strength measurement method (WSD-Rank) is designed to identify key communication nodes based on coverage, timeliness, and forwarding depth. AI clustering algorithms are combined to uncover primary diffusion pathways and generative intervention strategies are developed to achieve sentiment guidance and risk warning. Empirical verification shows that this method achieves an accuracy rate of 89.2% in brand sentiment prediction, providing effective theoretical modelling and algorithmic support for risk management and strategy optimisation in brand communication.

Keywords: brand communication; social media sentiment analysis; AI algorithms; sentiment prediction.

DOI: 10.1504/IJICT.2025.149180

International Journal of Information and Communication Technology, 2025 Vol.26 No.37, pp.91 - 105

Received: 19 Jul 2025
Accepted: 06 Sep 2025
Published online: 16 Oct 2025 *

Title: Social media sentiment diffusion modelling algorithms for brand communication

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