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http://dx.doi.org/10.20465/KIOTS.2022.8.4.057

Understanding Child Abuse Based on Big Data Analysis -A Basic Study on the Development of Machine Learning Algorithm-  

Bae, Jungho (Child Education, Baekseok Culture University)
Burm, Eunae (Nursing, Baekseok Culture Universityrsity)
Publication Information
Journal of Internet of Things and Convergence / v.8, no.4, 2022 , pp. 57-63 More about this Journal
Abstract
The purpose of this study is to provide basic data on policy development using big data analysis and machine learning algorithms as part of preparing measures to prevent child abuse. In order to analyze big data for developing machine learning algorithms to prevent child abuse, frequency analysis, related word analysis, and emotional analysis were performed after defining academic databases and social network service data as big data. related words, and emotional analysis were conducted. As a result of the study, a preventive child abuse algorithm can be developed by preparing a data collection and sharing network system to prevent child abuse from the perspective of children affected by child abuse, perpetrators, and government authorities. Although it will be possible by institutionalizing infant self-esteem, depression, and anxiety tests with clues that depression and anxiety appear due to a decrease in self-concept in the characteristics of children affected by child abuse. We suggest that continuous progress of big data collection and analysis and algorithm development research to prevent child abuse, and expects that effective policies to prevent child abuse will be realized to eradicate child abuse crimes.
Keywords
Child abuse; Big data; Machine learning; Algorithms;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Korea Law Information Center, "CHILD WELFARE ACT," 2022.05.30. https://law.go.kr/LSW/lsInfoP.do?lsiSeq=198257&viewCls=engLsInfoR&urlMode
2 S. H. OH & H. A. Kang, "Analysis of News on Child Abuse based on the Major Policy Changes: Using Text Mining," Journal of the Korean society of child welfare, Vol.70, No.3, pp.1-31, 2021.
3 J.I.Sorensen, R.M.Nikam and A.K.Choudhary, "Artificial intelligence in child abuse imaging," Pediatric radiology, Vol.51, No.6, pp.1061-1064, 2021.   DOI
4 A. Amado, P, Cortez, P. Rita, and S. Moro, "Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis," European Research on Management and Business Economics, Vol.24, No.1, pp.1-7, 2018.   DOI
5 A.V.Annapragada, M.D.Donaruma-Kwoh, A.V. Annapragada and Z.A.Starosolski, "A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records," PLoS ONE Vol.16, No.2, e0247404.
6 Amrit, C., Paauw, T., Aly, R., & Lavric, M,. "Identifying child abuse through text mining and machine learning," Expert systems with applications, Vol.88, pp.402-418, 2017.   DOI
7 J.H.Bae and E.A.Burm, "Big data analysis for child well-being on academic research," Medico Legal Update, Vol.20, No.1, pp.2058-2062, 2020.
8 Ministry of Health and Welfare, "2020 Annual Report on Child Abuse," http://www.mohw.go.kr/react/al/sal0301vw.
9 E.S.Gokten and C.Uyulan,"Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier," Journal of Affective Disorders, Vol.279, pp.256-265, 2021.   DOI
10 J.H,Bae and E.A.Burm, "Strategies for College Entrance Based on Big Data Analysis," Journal of Internet of Things and Convergence, Vol.8, No.2, pp.25-33, 2022.   DOI
11 J.H.Bae and E.A.Burm, "Big data analysis: Medical accident," Medico-Legal Update, Vol.19, No.1, pp.646-652, 2019.   DOI
12 J.H.Bae and E.A.Burm, "Big data analysis for child well-being on academic research," Medico Legal Update, Vol.20, No.1, pp.2058-2062, 2020.
13 C.Armit, T.Paauw, R.Aly & M.Lavric, "Identifying child abuse through text mining and machine learning," Expert Systems With Applications, 88(1), pp402-418, 2017.   DOI
14 M. Fattori, G. Pedrazzi & Turra, R. "Text mining applied to patent mapping," A practical business case. World Patent Information, 25(4), pp335-342, 2003.   DOI
15 C.Henry, S.Carnochan, & M.J.Austin, "Using qualitative data-mining for practice research in child welfare," Child Welfare; Arlington, 93(6), pp7-26, 2014.