Individual-level social influence identification in social media: A learning-simulation coordinated method

Abstract

This study develops a learning-simulation coordinated method to perform individual-level causal inference and social influence identification in social media. This method uses machine learning models to predict user adoption behavior, uses simulation to infer unobservable potential outcomes, and uses a counterfactual framework to identify individual-level social influence. The method also uses an adjusting strategy to reduce the effect of homophily and correlated unobservables. Empirical results obtained on a synthetic dataset and a semi-synthetic dataset show that the proposed method performs better on causal inference at the individual and aggregate levels than competitive methods. The computational experiment using a real-world database considers three applications, i.e., new product adoption, repeated purchase and cross selling. The empirical results show that the proposed method performs well on identifying influential members. The results reveal that the global hubs and local central nodes of the versatile friend circles have similar influences on the adoption behavior of the followers.