Social contagion has been studied in various contexts. Many instances of social contagion can be modeled as an infection process where a specific state (adoption of product, fad, knowledge, behavior, etc.) spreads from individual to individual through links between them. In the meantime, other forms of social contagion may better be understood as a diffusion process where the state of an individual tends to assimilate with the social norm (i.e., local average state) within his/her neighborhood.
Unlike infection scenarios where influence is nonlinear, unidirectional, fast, and potentially disruptive, social diffusion is linear, bidirectional, gradual, and converging. The distance between an individual's state and his/her neighbors' average state always decreases, and thus a homogeneous global state is guaranteed to be the network's stable equilibrium state in the long run. This does not sound as intriguing or exciting as infection dynamics, which might be why there are very few studies on mathematical models of social diffusion processes.
Here, this study attempts to shed new light on an unrecognized characteristic of social diffusion, i.e., non-trivial drift it can cause to the network's global average state. Although somewhat counterintuitive, such global drift is indeed possible because, unlike physical diffusion processes, social diffusion processes are not conservational. In what follows, a mathematical model of social diffusion will be presented to explain the mechanism of this phenomenon, and some possible collective actions for influencing the direction of global drift will be proposed. The relevance of social diffusion to individual and collective improvement will be discussed briefly, with an emphasis on educational applications.
Quantifying the impact of persuasiveness, cautiousness and prior beliefs in (mis)information sharing on online social networks using Drift Diffusion Models
本研究使用 Drift Diffusion Model 分析在线社交网络上（错误）信息分享的决策机制，发现理性思维可抑制分享误导性新闻的本能，并指出限制高连接用户关注者数量是遏制信息病毒式传播的有效策略。
Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application
Pontifícia Universidade Católica do Paraná (PUCPR)（巴拉那天主教大学）；Instituto Federal de Santa Catarina (IFSC)（圣卡塔琳娜联邦学院）；Universidade Estadual de Ponta Grossa (UEPG)（蓬塔格罗萨州立大学）
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making
State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，北京通用人工智能研究院）；Peking University（北京大学）；New York University（纽约大学）；Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media
Beijing Institute of Technology（北京理工大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Science and Technology of China（中国科学技术大学）
