Artificial Intelligence (AI) is increasingly used for data collection, integration, and predictive analysis in large-scale public projects like digital twin cities—virtual replicas of real-world cities. However, privacy concerns remain, particularly when personal data are involved. While research indicates people may be more willing to disclose private information to AI systems than to humans, the privacy-related psychological mechanisms in human-AI interaction remain unclear. This study examines how anthropomorphism and belief in positive machine heuristics interact to influence personal data disclosure intention in AI-powered digital twin cities. Results from an online survey in Singapore (N = 1,000) reveal that belief in positive machine heuristics is associated with greater perceived benefits, lower perceived privacy risks, and greater disclosure intention. Anthropomorphism, however, showed a dual mechanism, associated with both higher perceived privacy risks and greater disclosure intention. Additionally, anthropomorphism is positively associated with perceived benefits, but only when belief in positive machine heuristics is low.

Drawing upon self-determination theory, this study investigates whether the effects of competition interact with individuals’ prior knowledge to influence the motivations for and antecedents to their pro-environmental behaviour. Using a virtual reality serious game about plastic waste, we conducted a 2 (Game environment: Competition vs. Non-competition) × 3 (Prior knowledge about plastic waste: Low vs. Medium vs. High) between-subjects experiment with 61 participants (Mage = 23.31, SDage = 2.77). Results indicated that competition had differential impacts depending on individuals’ prior knowledge. Competition had negative effects on motivation and antecedents to pro-environmental behaviour for players with low levels of prior knowledge and positive effects for players with medium levels of prior knowledge. As the first study to investigate prior knowledge as a moderator for the effects of competition in a virtual reality serious game, our research contributes to the literature by clarifying the conditions under which competition could promote pro-environmental behaviour and offers suggestions on customised use of competition for communication practitioners.

In response to the growing threat of scientific misinformation, the development of advanced AI chatbots presents an opportunity to provide customized and timely misinformation corrections on a large scale. Informed by the computers as social actors (CASA) paradigm, we conducted a 2 × 2 between-subjects online experiment, examining how chatbot anthropomorphism and expertise influence the effectiveness of chatbot delivered corrections. We found that anthropomorphic cues of the chatbot strengthened participant’s trust in the chatbot and promoted correction sharing behavioral intentions. Furthermore, we also found that chatbot expertise strengthened the influence of its anthropomorphism on perceived correction credibility and intentions to use the chatbot. This study highlights the potential for AI chatbot to tackle misinformation, providing practical design insights for misinformation correction and advancing our understanding of the CASA paradigm.

This paper explores nuclear energy-related Twitter discussions as a response to the 2011 Fukushima Nuclear Disaster and the 2017 Nobel Peace Prize won by the International Campaign to Abolish Nuclear Weapons. We have considered a total of 2 million tweets for these two events. In particular, we employed CNN, LSTM, and Bi-LSTM to investigate whether social media users are supportive or cynical about nuclear energy. Our AI algorithms have performed better for polarity detection (accuracy in the range of 90%) with respect to subjectivity detection (accuracy in the range of 75%). We also note that dominant aspects of supporting tweets revolve around concepts like clean energy, lower CO2 emission, and sustainable future. On the contrary, cynical users see nuclear energy as a threat to the environment, human life, and safety.