Cyberpsychology, behavior and social networking最新文献

Problematic gaming has detrimental effects on individuals' mental and physical health. Adopting the self-determination theory as a theoretical framework and differentiating two types of game motivation (self-determined vs. non-self-determined), this study examined how these two types of game motivation may be oppositely associated with gamers' problematic gaming behaviors. By conducting a cross-sectional survey on massively multiplayer online video game players in China (n = 1,201), the current study found that individuals' self-determined motivation was negatively associated with their problematic gaming, while non-self-determined motivation positively predicted problematic gaming. Furthermore, these direct effects were mediated through individuals' harmonious passion and obsessive passion while playing games, respectively. Implications were discussed in terms of problematic gaming prevention and game design.

This study quantified the prevalence of two risky online behaviors among adolescents in Vietnam: online privacy disclosure and online pornography use. We conducted a field experiment with 1,313 junior high school students aged 13-15 years in Hoa Binh city. In addition to conventional direct questions, we employed list experiments to address social desirability bias in the students' responses. The results indicated that 49.9% of the adolescents engaged in online privacy disclosure and that 58.5% were involved in online pornography use. This study revealed significant underreporting among adolescents (35.6 and 43.3 percentage points for privacy disclosure and pornography use, respectively). The heterogeneous analyses revealed that recent smartphone ownership and active smartphone and Facebook use were associated with a greater prevalence of these behaviors. Notably, urban adolescents showed greater engagement in pornography use than did their rural counterparts. This study represents a pioneering effort to empirically investigate sensitive online behaviors among adolescents utilizing an experimental approach to address measurement bias. Our findings suggest that list experiments are a robust method for assessing sensitive issues and emphasize the critical need for educational interventions to mitigate online risks faced by young people.

Generative artificial intelligence (AI) tools could create statements that are seemingly plausible but factually incorrect. This is referred to as AI hallucination, which can contribute to the generation and dissemination of misinformation. Thus, the present study examines whether forewarning about AI hallucination could reduce individuals' acceptance of AI-generated misinformation. An online experiment with 208 Korean adults demonstrated that AI hallucination forewarning reduced misinformation acceptance (p = 0.001, Cohen's d = 0.45) while forewarning did not reduce acceptance of true information (p = 0.91). In addition, the effect of AI hallucination forewarning on misinformation acceptance was moderated by preference for effortful thinking (p < 0.01) such that forewarning decreased misinformation acceptance when preference for effortful thinking was high (vs. low).

Cyberscams are a pervasive global issue with losses exceeding $1 trillion worldwide and resulting in significant psychosocial impacts, particularly shame. People with disabilities, such as acquired brain injury (ABI), may be additionally vulnerable due to cognitive impairments and social isolation. Increased scam vulnerability and risk factors for people with ABI have not been investigated. This study aimed to (a) determine whether people with ABI have greater risk of being scammed than people without ABI, and (b) explore demographic and psychosocial factors associated with cyberscam risk for people with and without ABI. Using a cross-sectional design, participants with (n = 149) and without (n = 153) ABI provided scam experience details and completed a validated measure of self-rated cybersafety and practical scam identification (The CyberAbility Scale) and measures of psychosocial risks of loneliness, impulsivity, mood, trust, and community integration. Correlation analyses showed that participants with ABI performed worse on a scam identification task than those without ABI. As expected, higher self-rated scam safety was associated with lower loneliness, impulsivity, and fewer mood symptoms, and higher trust and community integration. In multiple regression analyses, higher loneliness was most significantly associated with higher self-rated cyberscam risk, and older age and presence of ABI were associated with poorer scam identification. This study illustrates the multifaceted nature of cyberscam risk, involving distinct social and knowledge-based risks. Findings underscore the need for scam prevention and recovery initiatives targeting at-risk groups and considering the needs of people with ABI in staying safe online.

How has the field of virtual reality (VR) evolved and what type of research has made an impact? We used natural language processing techniques and generative artificial intelligence to develop the most complete review of experimental social science VR research to date (1992-2024). From a collection of 21,195 abstracts written by 52,543 unique authors, 13 reliable themes emerged over time, with immersive experiences receiving the most recent attention. Interdisciplinary teams were cited more than less interdisciplinary teams, and watershed moments like mainstream industry embracing VR (i.e., Google Cardboard's release) correlated with changes in scholars' research focus. Based on such available data, we observed that more than half of all articles over the past 30 years have been published in the last 6 years. Our database-the VRbalARchive-is publicly available, helping scholars investigate VR's history and enhancing our theoretical understanding of the medium.

Recent research has investigated the connection between artificial intelligence (AI) utilization and feelings of loneliness, yielding inconsistent outcomes. This meta-analysis aims to clarify this relationship by synthesizing data from 47 relevant studies across 21 publications. Findings indicate a generally significant positive correlation between AI use and loneliness (r = 0.163, p < 0.05). Specifically, interactions with physically embodied AI are marginally significantly associated with decreased loneliness (r = -0.266, p = 0.088), whereas engagement with physically disembodied AI is significantly linked to increased loneliness (r = 0.352, p < 0.001). Among older adults (aged 60 and above), AI use is significantly positively associated with loneliness (r = 0.352, p < 0.001), while no significant correlation is observed (r = 0.039, p = 0.659) in younger individuals (aged 35 and below). Furthermore, by incorporating positive attitudes toward AI, the study reveals that the influence of AI use in exacerbating loneliness outweighs the reverse impact, although both directions show significant positive relationships. These results enhance the understanding of how AI usage relates to loneliness and provide practical insights for addressing loneliness through AI technologies.

In recent years, a plethora of artificial intelligence (AI) chatbots have been developed and made available to the public. Consequently, an increasing number of individuals are integrating AI chatbots into their daily lives for various purposes. This trend has also raised concerns regarding AI chatbot dependence. However, a valid and reliable scale to assess AI chatbot dependence is yet to be developed. Therefore, this study was designed to develop and validate an AI chatbot dependence scale. We obtained initial items from previous publications and in-depth interviews. Subsequently, item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability, and validity analyses were performed to validate the AI chatbot dependence scale. Seventeen items underwent item analysis and EFA, resulting in a single-factor model with eight items explaining 58.42% of the total variance. The CFA indicated that our AI chatbot dependence scale had acceptable model fitting indices, with standardized loadings ranging between 0.50 and 0.76. In addition, this scale exhibited good reliability and validity. Thus, the current AI chatbot dependence scale can effectively evaluate individuals' dependence on AI chatbots in their daily lives.