Department of Psychology, Michigan State University, East Lansing, Michigan.
Department of Advertising and Social Media, Michigan State University, East Lansing, Michigan.
Aggress Behav. 2020 Sep;46(5):449-460. doi: 10.1002/ab.21911. Epub 2020 Jun 24.
Cyberaggression (CA), or the use of information communication technologies to inflict harm on others, is an emerging public health crisis. Unfortunately, our current ability to assess CA in a research context remains limited, curtailing efforts to address this important issue. We sought to fill this gap in the literature by developing an adapted "chat" version of the Taylor aggression paradigm (TAP) that would more closely resemble a social gaming format (hereafter referred to as the TAP-Chat). In the TAP-Chat, participants have a chat function available to communicate with their (fictitious) co-player. Following loss trials in a competitive reaction time task, they receive a "mean chat" from their co-player. Participant messages to their (fictitious) co-player are then coded for aggressive content by a team of trained research assistants, and via automated linguistic analysis software (Linguistic Inquiry and Word Count). The current study evaluated the predictive utility of the TAP-Chat task in independent discovery and replication samples (N = 843 and N = 350, respectively). Participants' publicly available tweets served as an important external criterion variable, along with a handful of self-report questionnaires assessing CA and related constructs. Analyses suggest that, although it can be completed in ∼13 min, the TAP-Chat predicts CA on Twitter and, to a lesser extent, as reported on questionnaires. Although there are still several issues to address, it is our hope that the research community will benefit from this straightforward behavioral assessment of CA.
网络攻击(CA),即利用信息通信技术对他人造成伤害,是一种新兴的公共卫生危机。不幸的是,我们目前在研究中评估 CA 的能力仍然有限,限制了我们解决这一重要问题的努力。我们试图通过开发泰勒攻击范式(TAP)的适应性“聊天”版本来填补文献中的这一空白,该版本更接近社交游戏格式(以下简称 TAP-Chat)。在 TAP-Chat 中,参与者有一个聊天功能可供与他们的(虚构)同伴交流。在竞争性反应时间任务的失败试验后,他们会从同伴那里收到“刻薄的聊天”。然后,由一组经过培训的研究助理和自动语言分析软件(语言探究和词汇计数)对参与者发送给他们的(虚构)同伴的消息进行编码,以获取攻击性内容。目前的研究在独立发现和复制样本中评估了 TAP-Chat 任务的预测效用(N = 843 和 N = 350,分别)。参与者公开的推文是一个重要的外部标准变量,以及一些评估 CA 和相关结构的自我报告问卷。分析表明,尽管它可以在大约 13 分钟内完成,但 TAP-Chat 可以预测在 Twitter 上的 CA,并且在较小程度上可以预测问卷上的 CA。尽管仍有几个问题需要解决,但我们希望研究界能够从这种对 CA 的直接行为评估中受益。