The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Cross-Cultural Perspectives on Artificial Intelligence Generated Content (AIGC): A Comparative Study of Attitudes and Acceptance Among Global Products
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Research highlights that relatable AI systems are those that not only address functional needs but also connect with users on a personal and cultural level [28].”
From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [28]
Citations
Cites
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Research works printed but not linked: 11
- Cao, Y., et al.: A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226 (2023) · arXiv
- Zhang, C., et al.: A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need?. arXiv preprint arXiv:2303.11717 (2023) · arXiv
- Else, H.: Abstracts written by ChatGPT fool scientists. Nature 613(7944), 423–423 (2023) · DOI · Paper page
- Dwivedi, Y.K., et al.: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manage. 71, 102642 (2023) · DOI · Paper page
- Fu, P., Jing, B., Chen, T., Yang, J., Cong, G.: Modeling network public opinion propagation with the consideration of individual emotions. Int. J. Environ. Res. Public Health 17(18), 6681 (2020) · DOI
- Guo, D., Chen, H., Wu, R., Wang, Y.: AIGC challenges and opportunities related to public safety: a case study of ChatGPT. J. Safety Sci. Resilience 4(4), 329–339 (2023) · DOI
- Shao, G.: Understanding the appeal of user-generated media: a uses and gratification perspective. Internet Res. 19(1), 7–25 (2009) · DOI · Paper page
- Rajbhandari, S., et al.: Deepspeed-moe: advancing mixture-of-experts inference and training to power next-generation AI scale. In: International Conference on Machine Learning, pp. 18332-18346. PMLR (2022)
- Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023 · arXiv
- Rasley, J., Rajbhandari, S., Ruwase, O., He, Y.: Deepspeed: system optimizations enable training deep learning models with over 100 billion parameters. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 3505–3506 (2020) · DOI · Paper page
- Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., Catanzaro, B.: Megatron-lm: training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053 (2019) · arXiv
Cited by
Sorted by citations
Indongo, N. N., Smith, R. C., Winschiers-Theophilus, H., Kandjengo, S., & Mbinge, U. (2026) · Proceedings of the 19th Participatory Design Conference 2026, Vol. 1: Full Papers · doi:10.1145/3796624.3796667 · Cited by 1
“Research highlights that relatable AI systems are those that not only address functional needs but also connect with users on a personal and cultural level [28].”
Metadata
- Authors
- Liu, Z., & Chen, Z.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Liu, Zequn
- ORCID
- Chen, Zhe
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 287-298
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4399266345
- Cited by
- 11
- References
- 1
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 2
- FWCI
- 5.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- AI-generated content, user attitudes, technology acceptance, cross-cultural comparison, localization, ChatGPT, social media sentiment, Chinese social media users
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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