This study investigated whether social position (e.g., gender, migration, family status), intrapersonal-level (e.g., online risk behaviors, motives of Internet use), interpersonal-level (e.g., victimization and bullying), family-level (e.g., parental mediation), and class-level (e.g., teachers’ mediation, ethnic diversity) variables predict bias-based cybervictimization. Self-report questionnaires were completed by 1,018 Austrian adolescents (52.3% girls), aged 12 to 17 years ($\overline{X}$ = 13.55, SD = 0.88). The logistic part of a multilevel zero-inflated Poisson model showed that higher levels of offline victimization and a higher proportion of immigrants in classes were predictors for students reporting at least one form of bias-based cybervictimization. The Poisson part of the model showed that being a girl, higher levels of cybervictimization, lower levels of avoiding online risks, and more discussions about media use with teachers in classes were predictors for students reporting a higher number of bias-based cybervictimization. Implications for prevention are discussed.

这项研究调查了社会地位（例如性别，移民，家庭状况），人际层面（例如在线风险行为，互联网使用动机），人际层面（例如受害和欺凌），家庭层面（例如，家长调解）和班级（例如，老师的调解，种族多样性）变量可预测基于偏见的网络受害。12到17岁的1,018名奥地利青少年（52.3％的女孩）填写了自我报告调查表（$\overline{X}$= 13.55，SD = 0.88）。多层次零膨胀泊松模型的逻辑部分表明，班级中较高的离线受害人数和移民中的较高比例是学生至少报告了一种基于偏见的网络受害形式的预测因素。该模型的Poisson部分表明，女孩子，较高的网络受害程度，较低的规避在线风险以及与班级老师进行的有关媒体使用的更多讨论是学生报告基于偏见的网络受害人数较多的预测因素。讨论了预防的含义。