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.

[1]  Christian Schlereth,et al.  Optimal Product-Sampling Strategies in Social Networks: How Many and Whom to Target? , 2013, Int. J. Electron. Commer..

[2]  Jan Vanthienen,et al.  50 years of data mining and OR: upcoming trends and challenges , 2009, J. Oper. Res. Soc..

[3]  Jae Young Lee,et al.  Neighborhood Social Capital and Social Learning for Experience Attributes of Products , 2013, Mark. Sci..

[4]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[5]  Xi Chen,et al.  Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies , 2017 .

[6]  David Godes,et al.  Commentary - Invited Comment on "Opinion Leadership and Social Contagion in New Product Diffusion" , 2011, Mark. Sci..

[7]  Dries F. Benoit,et al.  Identifying influencers in a social network: The value of real referral data , 2016, Decis. Support Syst..

[8]  Harikesh S. Nair,et al.  Social Ties and User Generated Content: Evidence from an Online Social Network , 2011 .

[9]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[10]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[11]  Emilio Carrizosa,et al.  A nested heuristic for parameter tuning in Support Vector Machines , 2014, Comput. Oper. Res..

[12]  Jing Wang,et al.  Modeling Choice Interdependence in a Social Network , 2013, Mark. Sci..

[13]  Harikesh S. Nair,et al.  Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders , 2008 .

[14]  Ronald D. Anderson,et al.  Causal modeling alternatives in operations research: Overview and application , 2004, Eur. J. Oper. Res..

[15]  Jan U. Becker,et al.  Seeding Strategies for Viral Marketing: An Empirical Comparison , 2011 .

[16]  Peter M. Aronow,et al.  Estimating Average Causal Effects Under Interference Between Units , 2013, 1305.6156.

[17]  Elizabeth L. Ogburn,et al.  Causal diagrams for interference , 2014, 1403.1239.

[18]  Francesco Bonchi,et al.  Influence Propagation in Social Networks: A Data Mining Perspective , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[19]  Daniel G. Goldstein,et al.  Predicting Individual Behavior with Social Networks , 2014, Mark. Sci..

[20]  Uri Shalit,et al.  Estimating individual treatment effect: generalization bounds and algorithms , 2016, ICML.

[21]  Robert Hable,et al.  Consistency of support vector machines using additive kernels for additive models , 2012, Comput. Stat. Data Anal..

[22]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[23]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[24]  Christopher Winship,et al.  Counterfactuals and Causal Inference: Methods and Principles for Social Research , 2007 .

[25]  Thomas W. Valente,et al.  Opinion Leadership and Social Contagion in New Product Diffusion , 2011, Mark. Sci..

[26]  A. Rangaswamy,et al.  The Impact of New Media on Customer Relationships , 2010 .

[27]  Richard J. Roiger,et al.  Data Mining: A Tutorial Based Primer , 2002 .

[28]  Dylan Walker,et al.  Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment , 2014, Manag. Sci..

[29]  Martin Bichler,et al.  Identification of influencers - Measuring influence in customer networks , 2008, Decis. Support Syst..

[30]  Stefan Lessmann,et al.  A reference model for customer-centric data mining with support vector machines , 2009, Eur. J. Oper. Res..

[31]  H. Theil.  Introduction to econometrics , 1978 .

[32]  Ramayya Krishnan,et al.  Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network , 2015, Manag. Sci..

[33]  Jon M. Kleinberg,et al.  Graph cluster randomization: network exposure to multiple universes , 2013, KDD.

[34]  Juan Romo,et al.  Interpretable support vector machines for functional data , 2014, Eur. J. Oper. Res..

[35]  Ying Xie,et al.  The Role of Targeted Communication and Contagion in Product Adoption , 2008, Mark. Sci..

[36]  Eric T. Bradlow,et al.  Beyond conjoint analysis: Advances in preference measurement , 2008 .

[37]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[38]  Harikesh S. Nair,et al.  Modeling social interactions: Identification, empirical methods and policy implications , 2008 .

[39]  Shenyang Guo,et al.  Propensity Score Analysis: Statistical Methods and Applications , 2014 .

[40]  Jeffrey M. Wooldridge,et al.  Introductory Econometrics: A Modern Approach , 1999 .

[41]  Huan Liu,et al.  Leveraging social media networks for classification , 2011, Data Mining and Knowledge Discovery.

[42]  Jianqing Fan,et al.  Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.

[43]  Ilan Lobel,et al.  Preferences, Homophily, and Social Learning , 2014, Oper. Res..

[44]  Vasant Dhar,et al.  Prediction in Economic Networks , 2014, Inf. Syst. Res..

[45]  P. Holland.  Statistics and Causal Inference , 1985 .

[46]  Paul Jen-Hwa Hu,et al.  Predicting Adoption Probabilities in Social Networks , 2012, Inf. Syst. Res..

[47]  Sinan Aral,et al.  Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion , 2010, Mark. Sci..

[48]  Chris Volinsky,et al.  Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks , 2006, math/0606278.

[49]  Minghe Sun,et al.  Behavior-aware user response modeling in social media: Learning from diverse heterogeneous data , 2015, Eur. J. Oper. Res..

[50]  M. Sarvary,et al.  Network Effects and Personal Influences: The Diffusion of an Online Social Network , 2011 .

[51]  Catherine E. Tucker.  Identifying Formal and Informal Influence in Technology Adoption with Network Externalities , 2008 .

[52]  Wouter Verbeke,et al.  A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics , 2018, Big Data.

[53]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[54]  Stefan Wager,et al.  Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.