Predicting Adoption Probabilities in Social Networks

• 成果类型: Article
• 署名作者: Fang, Xiao; Hu, Paul Jen-Hwa; Li, Zhepeng (Lionel); Tsai, Weiyu
• 署名单位: Utah System of Higher Education; University of Utah
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.1120.0461
• 发表日期: 2013
• 页码: 128-145
• 关键词: contagion; models; diffusion; INNOVATION; proximity

摘要

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns, yet predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally weighted expectation-maximization method for Naive Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power and that confounding factors are critical to adoption probability predictions.