Cross-Cultural Perspectives on Artificial Intelligence Generated Content (AIGC): A Comparative Study of Attitudes and Acceptance Among Global Products

Given the popularity of interactive artificial intelligence models like ChatGPT, public perceptions of emerging Artificial Intelligence Generated Content (AIGC) have sparked widespread discussion. Companies and research institutions worldwide have been studying and releasing AIGC products, where the data used for training models in different regions is influenced by local cultures, inevitably leading to variations in the products. Public sentiment plays a crucial role on social media platforms, offering valuable insights that reflect the public’s opinions and attitudes. Currently, there is a lack of analysis on the social emotions evoked by AIGC content, not to mention studies on the emotional response to products across different cultural backgrounds. This research aims to fill this gap by collecting relevant data and establishing an analytical model. Specifically, we selected six AIGC products from different regions, collected posts and related interactive data from users on Chinese social media platforms, and developed a user attitude and acceptance calculation model to analyze attitudes and acceptance towards products from different cultural backgrounds. The study concludes with two main findings: users currently hold positive attitudes towards all AIGC products, with a slightly higher inclination towards local products compared to non-native ones; when confronted with AIGC products that exhibit cultural differences, users show a preference for products trained with local data. These conclusions not only demonstrate the differences in user acceptance of AIGC products across various cultural backgrounds but also underscore the importance of localization in the development and promotion of AIGC products. To enhance the user acceptance of non-native AIGC products, developers need to consider adopting more localization strategies to better meet users’ cultural needs and expectations.