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This dissertation presents the design, development and evaluation of the Social Set Visualizer, an innovative Visual Analytics software tool, that expands upon a novel set-based approach to Big Social Data Analytics for large-scale datasets from social media platforms such as Facebook.

Over the course of five peer-reviewed publications, three different versions of the Visual Analytics software tool are iteratively designed and developed, and several contributions to the visualization of sets and set intersections are highlighted.
In seven case studies with the Social Set Visualizer software tool the generation of meaningful facts and actionable insights from Big Social Data are empirically demonstrated, and a pre-existing research gap with regard to the Visual Analytics of large-scale Facebook datasets vs. other social media platforms is closed.

Based on these studies, the dissertation puts forward a generalized conceptual model for interactions within Big Social Data termed the Social Interaction Model, which provides a simplification and extension of previous theoretical and formal models.
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Primary Supervisor:
Professor Ravi Vatrapu
Department of Digitalization
Copenhagen Business School
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Secondary Supervisor:
Associate Professor Raghava Rao Mukkamala
Department of Digitalization
Copenhagen Business School
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Assessment Committee:
Associate Professor Arisa Shollo
Department of Digitalization
Copenhagen Business School

Professor Dan Suthers
Department of Information and Computer Sciences
University of Hawai‘i at Mānoa

Professor Ahmed Abbasi
Center for Business Analytics
University of Virginia
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Thesis:

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Reception:
The Doctoral School of Business and Management will host a reception, which will take place immediately after the defence in DIGI’s common area at Howitzvej 60 on the 4th floor.

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