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.

该研究使用社会大数据对决策树进行了分析，以对与韩国网络欺凌有关的风险因素类型进行预测模型。该研究对103,212次嗡嗡声进行了分析，这些嗡嗡声指出了网络欺凌的原因，并从227个在线渠道收集了数据，这些渠道包括新闻网站，博客，在线团体，社交网络服务和在线公告板。使用意见挖掘方法和决策树分析，使用SPSS 25.0对网络欺凌类型进行分类。结果表明，韩国网络欺凌的总比率为44％，其中包括32.3％的受害者，6.4％的犯罪者和5.3％的旁观者。根据结果​​，冲动因素对风险因素的预测影响最大，主导因素的倾向是预测风险因素类型的第二大因素。尤其是，冲动因素对旁观者的影响最大，而主导因素的倾向在影响在线犯罪者方面也很重要。有必要制定一项计划，以减少由旁观者以及受害者和作恶者发起的冲动，因为许多旁观者倾向于加剧冲动性网络欺凌行为。