During the COVID-19 (coronavirus disease 2019) epidemic in 2020, private information such as ID numbers and photographs of confirmed COVID-19 patients and people who live in Hubei province was widely spread on social networks. For example, Doctor An, the protagonist of the “collision” incident at the Deyang swimming pool in 2018, committed suicide as a result of the added stress of cyber violence. In the past 2 years, with the rapid growth of Internet users, cyber violence incidents on social media have appeared frequently, including such behavior as bullying, flaming, and verbal abuse, and even death threats. To test our hypotheses, we have constructed an empirical, multidimensional model, including the role of specific mediators in creating relationships.Ĭyber violence refers to any behavior on the Internet advocating violence or using language calculated to inflame the passions to achieve mass emotional catharsis (Hou and Li, 2017), which can be considered an extension of social violence to the Internet (Li et al., 2017).
The results of this study have implications for individual practitioners and scholars, for organizations, and at the governmental level regarding cyber-violence behavior. This study also presents evidence for competitive mediation of digital distrust by information overload, information inequality, and cyber violence. Furthermore, information overload, communication overload, information inequality, and digital distrust show a 27.1% change in negative emotions. Both digital distrust and negative emotions have significant positive impacts on cyber violence and cumulatively represent 11.5% changes in cyber violence.
However, the effects of information overload on digital distrust and the adverse effects of communication overload on negative emotions should not be ignored. Research findings show that information inequality is a strong external stimulus with a significant positive impact on digital distrust and negative emotion. A proposed model was tested using partial least squares structural equation modeling using SmartPLS 3.0, v (3.2.8). A closed-ended questionnaire was administered to collect data through an online survey, which results in 531 valid responses. Using elements of the stimulus–organism–response theory, we constructed a model of cyber-violence behavior. This article explores the causes behind cyber violence and provides empirical data for developing means for effective prevention.
In recent years, there has been an escalation in cases of cyber violence, which has had a chilling effect on users' behavior toward social media sites.
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