Cyberpsychology, behavior and social networking最新文献

Since generative AI (GenAI) launched, interactions between humans and artificial intelligence have rapidly evolved. This study explores how discourse practices in human-AI interactions influence collaborative problem-solving with ChatGPT. Grounded in small group dynamics research, the investigation examines whether three discourse practices (i.e., construction, co-construction, and constructive conflict) help users and ChatGPT find common ground (i.e., shared cognition) to effectively and efficiently complete creative writing projects. Participants completed creative writing projects using ChatGPT for a month and subsequently responded to surveys assessing discourse practices, shared cognition, team effectiveness, and concepts from the Technology Acceptance Model (TAM). Results indicate that discourse practices significantly predict shared cognition, which in turn mediates the relationship between discourse practices and perceived team effectiveness. Furthermore, shared cognition positively influences behavioral intentions to use ChatGPT, primarily through its effects on perceived ease of use and perceived usefulness, aligning with TAM predictions. These findings have important implications for understanding users' communication styles, suggesting that principles from human-to-human interactions can and should be applied to AI conversations. The current study proposes that ChatGPT is capable of mirroring and reciprocating these discourse practices, which may open the possibility to begin optimizing dialogue with GenAI similar to the goals of prompt engineering.

This study investigated the complex relationship between the Dark Triad (DT) and anonymity levels in the context of cyber aggression on social media. By employing an experimental design, the study aimed to bridge the gap between traditional survey-based experiments and real-time online interactions among social media users. Participants (N = 115) from Taiwan took part a 2 × 2 experimental design, which varied along two factors: anonymity (high vs. low) and DT (high vs. low). Over the course of a four-day simulated exclusionary cyber aggression event, participants' attitudes were measured via surveys, while their aggressive behaviors were assessed using the polling function on social media. The findings revealed that participants with high DT exhibited significantly higher levels of cyber aggression under the low-anonymity condition compared to those with low DT. However, there was no difference between groups under the high-anonymity condition. Notably, no significant differences were found in attitudes towards cyber aggression. This study makes a significant contribution by employing a simulated cyber aggression scenario that captures participants' real-time attitudes and behaviors, rather than relying solely on self-report measures, as is common in previous research.

Online aggression is a serious social problem in the Internet era, which seriously threatens the physical and mental health of individuals. Exploring how online aggression differs from offline aggression can help us develop targeted prevention and intervention measures. However, the basic difference between online aggression and traditional offline aggression remains unclear. This study tried to address the issues from the perspective of structure and predictors by using network analysis in 1,009 Chinese college students. The dimensions of online and traditional offline aggression were utilized to detect the community. And incorporate individual and situational predictors as nodes for network analysis. The results showed that the nodes of Cyber-Aggression Typology Questionnaire and Buss Perry Aggression Questionnaire were divided into two distinct communities. The edge-weight bootstrapped difference test showed that callous-unemotional traits, trait anger, empathy, and guilt were only associated with offline aggression. Furthermore, moral disengagement and social exclusion were more closely associated with offline aggression than online aggression, while violent attitude was more associated with online aggression. These findings support and expand the Barlett and Gentile Cyberbullying Model, enhancing our comprehension of both offline and online aggression, and providing inspiration for prevention and intervention in offline and online aggression in the future.