Social Network Data and Predictive Mining (Business Intelligence 2)

Amy Van Looy
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Amy Van Looy: Ghent University

Chapter 8 in Social Media Management, 2022, pp 165-189 from Springer

Abstract: 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 the 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.

Keywords: Business intelligence; Big data; Knowledge discovery; Targeted marketing; Descriptive network modeling; Predictive network modeling; Social network; Quasi-social network; Graph mining; Peer influence; Homophily; Privacy (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-99094-7_8

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