Social Influence

Social influence examines how individuals' behaviors, attitudes, and opinions are shaped by interactions within social networks. Current research focuses on understanding and mitigating biases in social influence, particularly in recommendation systems and opinion formation, employing techniques like causal disentanglement, agent-based modeling, and deep learning algorithms such as matrix factorization and reinforcement learning to model and predict influence dynamics. This research is significant for improving the accuracy and fairness of recommendation systems, predicting collective behavior, and understanding the spread of information and opinions in online and offline settings, with implications for fields ranging from marketing to public health.