Abstract
This chapter covers the second part of our business intelligence discussion and makes the reader learn how organizations can create business value by analyzing social network data. Diverse information about a certain person can be collected from different social media tools and combined into a database to obtain more complete profiles of employees, customers, or prospects (i.e., social engineering). The latter can supplement the social CRM database (see Chap. 5). Particularly, social media may uncover information about what people post, share, or like but also to whom they are connected. By combining or aggregating such information for many individuals in social networks, organizations can start predicting trends, e.g., to improve their targeted marketing (see Chap. 4) or to predict which people are more likely to churn, fraud, resign, etc. Hence, social media are seen as big data in the sense that they can provide massive amounts of real-time data about many Internet users, which can be used to predict someone’s future behavior based on past behavior of others. This chapter explains how social networks can be built from social media data and introduces concepts such as peer influence and homophily. The chapter concludes with big data challenges to social network data.
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Bibliography
Aral, S., & Walker, D. (2011). Identifying social influence in networks using randomized experiments. IEEE Intelligent Systems, 26(5), 91–96.
Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication and Society, 15(5), 662–679.
Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the digital humanities (pp. 460–475). Minneapolis: University of Minnesota Press.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.
Minnaert, B. (2012). [Guest lecture of Bart Minnaert in the course Creating Value Using Social media at Ghent University, November 2012].
Newman, M. (2010). Networks: An introduction. New York: Oxford University Press.
Pinheiro, C. A. R. (2011). Social network analysis in telecommunications. Hoboken, New Jersey: SAS Institute and Wiley.
Provost, F., Dalessandro, B., Hook, R., Zhang, X., & Murray, A. (2009). Audience selection for on-line brand advertising: privacy-friendly social network targeting. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Provost, F., & Fawcett, T. (2013). Data science for business. What you need to know about data mining and data-analytic thinking. California: O’Reilly Media.
Rogers, E. M. (2003). The diffusion of innovations (5th ed.). New York: Free Press.
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.
Verbeke, W., Martens, D., & Baesens, B. (2014). Social network analysis for customer churn prediction. Applied Soft Computing, 14, 431–446.
Wikipedia. (2014). Amazon.com. Fulfilment and warehousing. Retrieved July 16, 2014, from: http://en.wikipedia.org/wiki/Amazon.com#Fulfillment_and_warehousing
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Van Looy, A. (2016). Social Network Data and Predictive Mining (Business Intelligence 2). In: Social Media Management. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-21990-5_8
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DOI: https://doi.org/10.1007/978-3-319-21990-5_8
Publisher Name: Springer, Cham
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