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

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2022, 8(4), pp.57-63
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : June 15, 2022
  • Accepted : July 29, 2022
  • Published : August 31, 2022

Bae Jung Ho 1 Burm,EunAe 1

1백석문화대학교

Accredited

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.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.