A No-Code Platform for Tie Prediction Analysis in Social Media Networks

Sebastian Schötteler (), Sven Laumer (), Heidi Schuhbauer (), Niklas Scheidthauer (), Philipp Seeberger () and Benedikt Miethsam ()
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Sebastian Schötteler: Nuremberg Institute of Technology
Sven Laumer: FAU Erlangen-Nuremberg
Heidi Schuhbauer: Nuremberg Institute of Technology
Niklas Scheidthauer: Nuremberg Institute of Technology
Philipp Seeberger: Nuremberg Institute of Technology
Benedikt Miethsam: Nuremberg Institute of Technology

A chapter in Innovation Through Information Systems, 2021, pp 475-491 from Springer

Abstract: Abstract Conventional methods for tie prediction analysis in social media networks are often code-intensive and encompass complex steps. Against this backdrop, we used design science research to develop a no-code tie prediction analysis platform. Our evaluation indicates that the platform significantly reduces tie prediction analysis complexity and, depending on the network size, also total prediction time. Moreover, it maintains a prediction accuracy similar to that of conventional, code-intensive methods. Thus, our artifact substantially facilitates tie prediction analysis for social media network researchers and practitioners.

Keywords: Social media networks; Tie formation concepts; Tie prediction algorithms; Tie prediction analysis platform; Social media analytics (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86797-3_32

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DOI: 10.1007/978-3-030-86797-3_32

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