TY - GEN
T1 - Cross-Cultural Perspectives on Artificial Intelligence Generated Content (AIGC)
T2 - 16th International Conference on Cross-Cultural Design, CCD 2024, held as part of the 26th HCI International Conference, HCII 2024
AU - Liu, Zequn
AU - Chen, Zhe
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Acceptance
KW - AIGC
KW - Attitudes
KW - ChatGPT
KW - Cross-Cultural
UR - https://www.scopus.com/pages/publications/85196213784
U2 - 10.1007/978-3-031-60913-8_20
DO - 10.1007/978-3-031-60913-8_20
M3 - 会议稿件
AN - SCOPUS:85196213784
SN - 9783031609121
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 287
EP - 298
BT - Cross-Cultural Design - 16th International Conference, CCD 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
A2 - Rau, Pei-Luen Patrick
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 29 June 2024 through 4 July 2024
ER -