ScholarWorks@숙명여자대학교

초록

Child abuse has been increasing rapidly globally especially since the outbreak of the Covid-19 pandemic. However, studies on child abuse still tend to rely on the traditional research methods. This study was intended to develop a child abuse ontology as a first step toward social big data collection and analysis. Following Noy and McGuinness’ (2001) seven-stage development process of ontology, 25,092 keywords were extracted from 3,090 online news and 295 research articles published in 2019 using Textom and R 4.1.0. After undertaking pre-processing, 4,077 keywords in total were included in the child abuse ontology. The results yielded domains for (a) participants (victims, perpetrators, and the persons concerned); (b) types (abuse, neglect, and trafficking); (c) factors (protective factors and risk factors); (d) characteristics (signs, place, time, methods, and distinct features); (e) impacts (individual, family, and environmental problems); and (f) countermeasures (coping strategies, prevention, welfare intervention, judicial intervention, and organizations involved). The findings provide a child abuse ontology that embraces multi-faceted domains of this issue for the first time, thus laying the groundwork for social big data analysis on the topic of child abuse.

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