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Yoonsun Han | Seoul National University
Hayoung Kim | National Youth Policy Institute
Juyoung Song | Pennsylvania State University-Schuylkill
Tae Min Song | Sahmyook University
Abstract
Although social big data can provide a multi-faceted perspective on school bullying experiences among children and adolescents, the complexity and variety of unstructured text presents a challenge for systematic collection and analysis of the data. Development of an ontology, which identifies key terms and their intricate relationships, is crucial for extracting key concepts and effectively collecting data. The current study elaborated on the definition of an ontology, carefully described the 7 stage development process, and applied the ontology for collecting and analyzing school bullying social big data. As a result, approximately 2,400 key terms were extracted in top-, middle-, and lower-level categories, concerning domains of participants, causes, types, location, region, and intervention. The study contributes to the literature by explaining the ontology development process and proposing a novel alternative research model that uses social big data in school bullying research. Findings from this ontology study may provide a basis for social big data research. Practical implications of this study lie in not only helping to understand the experience of school bullying participants, but also in offering a macro perspective on school bullying as a social phenomenon.
keywords
Child and Adolescent, School Bullying, Social Big Data, Ontology
The Journal of the Korea Contents Associationv.19 no.6, 2019, pp.10 - 23