Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data |
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| Affiliation: | 1. Department of Health Management, Sahmyook University, Seoul, South Korea;2. Administration of Justice, Pennsylvania State University, Schuylkill, PA, USA;1. Department of Criminology and Criminal Justice, Southern Illinois University, Carbondale, IL, 62901, USA;2. Flagler College, St. Augustine, Florida, USA;1. Department of Administration of Justice, Pennsylvania State University, Schuylkill Haven, Pennsylvania;2. Statistics and Information Department, Korea Institute for Health and Social Affairs, Sejong City, South Korea;3. Department of Applied Health Science, Indiana University School of Public Health, Bloomington, Indiana;1. Department of Educational Psychology and Counseling, National Tsing Hua University, Taiwan, 101, Sec. 2. Kuang Fu Road, Hsinchu City 30013, Taiwan;2. Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan. No. 43, Sec. 4, Keelung Road, Da''an Dist., Taipei City 10617, Taiwan |
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| Abstract: | The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors. |
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| Keywords: | Social big data Data mining Decision trees Cyberbullying |
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