Abstract
This PhD is about the design, development and evaluation of the Social Data Analytics Tool (SODATO) to collect, store, analyze, and report big social data emanating from the social media engagement of and social media conversations about organizations. Situated with in the academic domains of Data Science, Computational Social Science and Information Systems, the PhD project addressed two general research questions about the technological architectures and design principles for big social data analytics in an organisational context.
The PhD project is grounded in the theory of socio-technical interactions for better understanding perception of, and action on, the screen when individuals use social media platforms such as Facebook. Based on the theory of socio-technical interactions, a conceptual model of social data was generated. This conceptual model of social data model consists of two components: social graph and social text. The social graph consists of the structure of the relationships emerging from social media appropriation and focuses on identifying the actors involved, the actions they take, the activities they engage in, and the artefacts they create and interact with. Social text consists of the communicative and linguistic aspects of the social media interaction such as the topics discussed, keywords mentioned, pronouns used and sentiments expressed. The conceptual model of social data is then used to specify the formal model of social data using the mathematics of set theory.
The design science methodological framework of Action Design Research (ADR) was employed to create the ITArtefact, Social Data Analytics Tool (SODATO) in close collaboration with multiple stakeholders. In order to evaluate the rigour and relevance of SODATO for conducting different kinds of academic and industry analysis, the tool was utilized in four different domain-specific empirical case studies covering the range of predictive, prescriptive, descriptive and visual analytics paradigms.
The contributions of the PhD project include the IT-Artefact SODATO itself, the triadic model of design science contributions consisting of design propositions, design principles, and software design patterns for big social data analytics in general, and an analytical framework for set-theoretical computational social science.
The PhD project is grounded in the theory of socio-technical interactions for better understanding perception of, and action on, the screen when individuals use social media platforms such as Facebook. Based on the theory of socio-technical interactions, a conceptual model of social data was generated. This conceptual model of social data model consists of two components: social graph and social text. The social graph consists of the structure of the relationships emerging from social media appropriation and focuses on identifying the actors involved, the actions they take, the activities they engage in, and the artefacts they create and interact with. Social text consists of the communicative and linguistic aspects of the social media interaction such as the topics discussed, keywords mentioned, pronouns used and sentiments expressed. The conceptual model of social data is then used to specify the formal model of social data using the mathematics of set theory.
The design science methodological framework of Action Design Research (ADR) was employed to create the ITArtefact, Social Data Analytics Tool (SODATO) in close collaboration with multiple stakeholders. In order to evaluate the rigour and relevance of SODATO for conducting different kinds of academic and industry analysis, the tool was utilized in four different domain-specific empirical case studies covering the range of predictive, prescriptive, descriptive and visual analytics paradigms.
The contributions of the PhD project include the IT-Artefact SODATO itself, the triadic model of design science contributions consisting of design propositions, design principles, and software design patterns for big social data analytics in general, and an analytical framework for set-theoretical computational social science.
Originalsprog | Engelsk |
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Udgivelsessted | Frederiksberg |
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Forlag | Copenhagen Business School [Phd] |
Antal sider | 220 |
ISBN (Trykt) | 9788793339866 |
ISBN (Elektronisk) | 9788793339873 |
Status | Udgivet - 2016 |
Navn | PhD series |
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Nummer | 11.2016 |
ISSN | 0906-6934 |