Adoption probability in social network

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Information Systems Research Vol. 24, No. 1, March 2013, pp. 128–145 ISSN 1047-7047 (print) ISSN 1526-5536 (online) © 2013 INFORMS Predicting Adoption Probabilities in Social Networks Xiao Fang, Paul Jen-Hwa Hu, Zhepeng (Lionel) Li, Weiyu Tsai David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112 {[email protected], [email protected], [email protected], [email protected]} I n a social network, adoption probability refers to the probability that a social entity will adopt a prod- uct, 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 cam- paigns, 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 influ- ence, structural equivalence, entity similarity, and confounding factors. We then develop the locally weighted expectation-maximization method for Naïve 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 meth- ods primarily using social influence to predict adoption probabilities offer limited predictive power and that confounding factors are critical to adoption probability predictions. Key words : adoption probability; social network; Bayesian learning; social influence; structural equivalence; entity similarity; confounding factor History : Chris Dellarocas, Senior Editor; Panagiotis Ipeirotis, Associate Editor. This paper was received on January 16, 2012, and was with the author 3 months for 1 revision. Published online in Articles in Advance January 14, 2013. 1. Introduction Fostered by the ubiquitous information technology, social networks such as those facilitated by Facebook, Twitter, electronic mail, or mobile phone (Dodds et al. 2003, Kleinberg 2008, Eagle et al. 2009) have attracted increasing attention from both academia and indus- try that explores how to leverage such networks for greater business and societal benefits (Domingos and Richardson 2001, Pentland 2008, Chen and Zeng 2009, Weng et al. 2010, Aral et al. 2011). A salient feature of social networks is the spread of adoption behav- ior (e.g., adoption of a product, service, or opinion) from one social entity to another in a social network (Kleinberg 2008). This feature is central to a wide vari- ety of applications in business (e.g., Domingos and Richardson 2001), public health (e.g., Chen et al. 2011), and politics (e.g., Carr 2008). Predicting the probabil-
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