Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on ado...Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on adolescent online trolling behavior,proposing that hostile attribution bias mediated this relationship and that trait mindfulness moderated both the direct and indirect effects.Methods:A total of 833 Chinese adolescents completed the measurements of peer victimization,hostile attribution bias,trait mindfulness,and online trolling.Moderated mediation analysis was performed to examine the relationships between these variables.Results:After controlling for gender and residential address,the study found a significant positive correlation between peer victimization and online trolling,with hostile attribution bias serving as a mediator.In addition,trait mindfulness moderated the direct relationship between peer victimization and online trolling.Specifically,the effect of peer victimization on online trolling was attenuated when adolescents had high levels of trait mindfulness.The results of the study emphasized the joint role of peer and personal factors in adolescents’online trolling behavior and provide certain strategies for intervening in adolescents’online trolling behavior.Conclusion:The results of the study suggest that strategies focusing on peer support and mindfulness training can have a positive impact on reducing online trolling behavior,promoting adolescents’mental health,and their long-term development.展开更多
针对传统方法难以对大规模钓鱼网站进行批量检测的问题,提出基于特征筛选的轻量级层次化检测方法(lightweight hierarchical detection method based on feature filtering,LHFF).该方法首先使用互信息对原始特征集进行筛选,剔除冗余特...针对传统方法难以对大规模钓鱼网站进行批量检测的问题,提出基于特征筛选的轻量级层次化检测方法(lightweight hierarchical detection method based on feature filtering,LHFF).该方法首先使用互信息对原始特征集进行筛选,剔除冗余特征,并将筛选后的特征按照提取特征耗时长短划分为URL特征和网站特征,然后根据划分后的特征,使用轻量级层次化检测框架对钓鱼网站进行检测.实验结果表明,LHFF能够在保障良好检测性能的前提下,减少网站检测所需要的时间,满足对大规模钓鱼网站进行批量检测的需求.展开更多
基金supported by the Sichuan Provincial Philosophy and Social Science Foundation Project(General Project)titled‘Research on the Influence Mechanism and Intervention of Mindfulness on Online Trolling among Adolescents’(Grant Number:SCJJ23ND227).
文摘Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on adolescent online trolling behavior,proposing that hostile attribution bias mediated this relationship and that trait mindfulness moderated both the direct and indirect effects.Methods:A total of 833 Chinese adolescents completed the measurements of peer victimization,hostile attribution bias,trait mindfulness,and online trolling.Moderated mediation analysis was performed to examine the relationships between these variables.Results:After controlling for gender and residential address,the study found a significant positive correlation between peer victimization and online trolling,with hostile attribution bias serving as a mediator.In addition,trait mindfulness moderated the direct relationship between peer victimization and online trolling.Specifically,the effect of peer victimization on online trolling was attenuated when adolescents had high levels of trait mindfulness.The results of the study emphasized the joint role of peer and personal factors in adolescents’online trolling behavior and provide certain strategies for intervening in adolescents’online trolling behavior.Conclusion:The results of the study suggest that strategies focusing on peer support and mindfulness training can have a positive impact on reducing online trolling behavior,promoting adolescents’mental health,and their long-term development.
文摘针对传统方法难以对大规模钓鱼网站进行批量检测的问题,提出基于特征筛选的轻量级层次化检测方法(lightweight hierarchical detection method based on feature filtering,LHFF).该方法首先使用互信息对原始特征集进行筛选,剔除冗余特征,并将筛选后的特征按照提取特征耗时长短划分为URL特征和网站特征,然后根据划分后的特征,使用轻量级层次化检测框架对钓鱼网站进行检测.实验结果表明,LHFF能够在保障良好检测性能的前提下,减少网站检测所需要的时间,满足对大规模钓鱼网站进行批量检测的需求.
