CRISP-eSNeP: Towards a Data-Driven Knowledge Discovery Process for Electronic Social Networks

Document Type

Article

Publication Date

8-21-2018

Abstract

Value in big data is created when insights are mined to support business processes. On social networks (SNs), big data, coupled with the operational mechanisms of such networks presents challenging, yet interesting perspectives to generate insights. A key limitation to big data research on SNs is the lack of a concise methodological model that drives conceptual and analytical questions. We add specificity to existing Knowledge Discovery and Data Mining (KDDM) frameworks by proposing a methodology for analyzing big data on electronic SNs. Particularly, we propose the Cross Industry Standard Process for Electronic Social Network Platforms (CRISP-eSNeP) not only as an extension to the CRISP-DM model, but also as an advancement of the knowledge on KDDM methods. Our method emphasizes the efficient management of large semi-structured and unstructured data that reflects a specific SN. We present the results of our process development using Gregor and Hevner’s design science research schema.

DOI

10.1080/12460125.2019.1696614

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