Understanding and Fighting Bullying With Machine Learning
Understanding and Fighting Bullying With Machine Learning
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通过机器学习理解和打击欺凌行为
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Junming Sui
中科院分区：
文献类型：
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作者：
Junming Sui
在现实世界和网络世界中，欺凌已被认为是青少年中严重的健康问题。鉴于其重要性，学者们负责及时识别影响欺凌参与的因素。然而，以前的社会研究的欺凌受到数据稀缺。研究欺凌的标准心理科学方法是在学校进行个人调查。样本量通常为数百个，这些调查通常只收集一次。另一方面，为数不多的计算研究狭隘地将自己局限于网络欺凌，这只占所有欺凌事件的一小部分。
Bullying, in both physical and cyber worlds, has been recognized as a serious health issue among adolescents. Given its significance, scholars are charged with identifying factors that influence bullying involvement in a timely fashion. However, previous social studies of bullying are handicapped by data scarcity. The standard psychological science approach to studying bullying is to conduct personal surveys in schools. The sample size is typically in the hundreds, and these surveys are often collected only once. On the other hand, the few computational studies narrowly restrict themselves to cyberbullying, which accounts for only a small fraction of all bullying episodes.
回复：“使用数值方法设计模拟：重新审视平衡截距”。
影响因子：
5
作者：
Zivich,PaulN;Ross,RachaelK
通讯作者：
Ross,RachaelK