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

In: Innovation Through Information Systems

Author

Listed:
  • Sebastian Schötteler

    (Nuremberg Institute of Technology
    FAU Erlangen-Nuremberg)

  • 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)

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.

Suggested Citation

  • Sebastian Schötteler & Sven Laumer & Heidi Schuhbauer & Niklas Scheidthauer & Philipp Seeberger & Benedikt Miethsam, 2021. "A No-Code Platform for Tie Prediction Analysis in Social Media Networks," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 475-491, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_32
    DOI: 10.1007/978-3-030-86797-3_32
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