Cyberpsychology, behavior and social networking最新文献

Extending previous research on creative AI to the context of AI advertising, the present study examined whether creativity in AI advertising could evoke perceptions of uncanniness and consequently have a negative impact on ad outcomes. Based on a two (ad creativity: low vs. high) by two (creator label: human vs. AI) between-subjects experimental design, the present study showed that ad creativity in general had a positive direct impact on attitude toward the ad (A_ad), attitude toward the brand (A_b), and purchase intentions. In addition, there were significant mediation effects of perceived creativity on A_ad and A_b via perceived uncanniness, which was moderated by creator label. For the AI-creator-labeled condition, perceived creativity had negative indirect effects on A_ad and A_b via perceived uncanniness, such that greater perceived creativity led to greater perceived uncanniness, which in turn induced less favorable A_ad and A_b. However, the mediation effect was not found for the human-creator-labeled condition. Thus, the positive effect of ad creativity was smaller for the AI-creator-labeled ad.

Digital addictions, such as Internet, game, social media, and smartphone addiction, pose health risks. This cross-sectional online survey of 1,764 participants aged 16-28 (75.7 percent women) identified four addiction subtypes using specific scales. Network analysis revealed significant comorbidity (95.35 percent) among subtypes, with smartphone addiction playing a dominant role, leading to social media, Internet, and potentially game addiction. High comorbidity suggests a shared vulnerability, with smartphone use as a catalyst. Smartphone addiction is a key precursor to other digital addictions.

Social media plays a powerful role in accelerating the spread of misinformation, especially in the mental health domain, where misleading content may even cause disasters. Given the extensive coverage and complexity of social media data, manually moderating online misinformation is infeasible. Therefore, the present study proposes an integrated framework that combines qualitative analysis and deep learning to automatically detect and evaluate mental health misinformation. Guided by expert interviews and grounded theory, in the present study, a 21-level, fine-grained credibility assessment framework covering seven dimensions was developed. Using the framework, in this study, 814 Chinese social media posts were manually annotated, and a high-quality dataset was constructed. On this dataset, we trained and evaluated three deep learning models, that is, Gated Recurrent Unit (GRU), Bidirectional Encoder Representations from Transformers (BERT), and Robustly Optimized BERT Approach (RoBERTa), to automatically assess the credibility of mental health content. The results show that BERT, GRU, and RoBERTa models are effective at leveraging a range of clear sentiment-related cues and surface-level patterns to evaluate mental health misinformation on social media, particularly on dimensions such as Inflammatory Expression and One-sidedness of Expression. However, all three models face challenges in evaluating evidence quality and detecting context-dependent misinformation. When dealing with these challenges, BERT and GRU outperform RoBERTa, particularly in dimensions such as Logical Rigor. This study provides a robust, scalable, and expert-informed approach to improve the credibility of mental health information online.