Explore a series of interconnected studies that examine human behavior and decision-making processes within social networks
This event invites participants from all disciplines and backgrounds to come together, fostering interdisciplinary connections and discussions.
Talk Title: Modeling and Analyzing User Behavior and Decision-Making in Social Networks
In today’s digital ecosystem, social media platforms enable the large-scale and rapid spread of information, fostering the emergence of collective behaviors while also generating significant societal consequences. These dynamics are reshaping how individuals interact, update beliefs, and make decisions. This presentation introduces a series of interconnected studies that examine human behavior and decision-making processes within social networks from both theoretical and data-driven perspectives.
Collective decision-making: We propose a comprehensive framework that models the holistic processes of belief updating and decision-making, shifting the analytical focus from isolated individuals to socially embedded decision-makers. A modified naming game model is developed to capture belief dynamics among individuals, incorporating social learning, self-learning, and forgetting mechanisms.
Influence maximization: We develop several models, based on network topology, multi-criteria decision-making, and large language models, to identify key users capable of maximizing influence and accelerating information diffusion. These models consistently outperform traditional benchmarks across multiple real-world networks.
Source localization: A novel approach that integrates evolutionary computation with generalized Bayesian inference is introduced to locate the source of information propagation. This method achieves superior accuracy and efficiency with minimal prior knowledge about the underlying propagation dynamics or user attributes.
By illuminating the mechanisms of collective behavior and decision-making in an increasingly complex and interconnected digital society, our work advances both the theoretical modeling and analytical foundations of human behavior in networked systems.
Register here: https://tinyurl.com/4rh59u9s
Speaker Biography:
Tao Wen is currently a Research Fellow in AI Trust and Security at Alliance Manchester Business School, The University of Manchester. He received his PhD from the Centre for Doctoral Training in Data Analytics and Society at The University of Manchester in 2025. His research interests are broadly in the intersection of network science, decision analytics, data analytics, and collective dynamics.
Connect with the Centre for Digital Trust and Society:
The Centre for Digital Trust and Society organises the DTS Guest Seminar Series and is a focal point for research across the University of Manchester that explores aspects of trust and security in our digital world. The Centre is part of Digital Futures, a highly interdisciplinary network which operates across the whole range of the University of Manchester's digital research.
About Digital Futures:
Digital Futures is a highly interdisciplinary network that operates across the whole range of the University’s digital research. We aim to present a coherent overview of The University of Manchester's digital research activity to external stakeholders and bring together research communities to explore new research areas and address strategic opportunities.
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