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What are the Chinese public's attitudes towards AI-generated portrait images? A text mining analysis based on social media

Xiang He & XuWei Pan

Aslib Journal of Information Management2026https://doi.org/10.1108/ajim-01-2025-0024article
AJG 1
Weight
0.50

What the paper says

Purpose The purpose of this paper is to analyze the Chinese public's attitudes towards artificial intelligence (AI)-generated portrait images through social media data, offering data support for the subsequent development of AI technology. Design/methodology/approach We employed a text mining approach to scrutinize textual data from social media, encompassing semantic network analysis to identify public focus, sentiment analysis to assess the emotional orientation of the public and, ultimately, topic clustering to uncover the primary reasons behind positive and negative comments. Findings The discussions surrounding AI-generated portrait images show a fluctuating pattern in 2023. 76.84% of the comments exhibited positive sentiment, primarily attributed to (1) satisfaction with the current quality of AI-generated portraits and (2) optimism towards the future development of AI technology. Conversely, 19.02% of the comments expressed negative sentiment, primarily due to (1) the insufficient detail in current AI-generated portraits; (2) concerns about China's lagging AI technology compared to the United States; (3) fears of AI technology replacing human labor and (4) legal issues surrounding AI technology and the potential impact of AI on the art employment market. Originality/value In this paper, through an in-depth examination of public attitudes, we aim to gain a more profound understanding of the level of acceptance and the influencing factors of AI-generated portrait images in society, ultimately providing theoretical support for future research and policymaking.

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https://doi.org/https://doi.org/10.1108/ajim-01-2025-0024

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@article{xiang2026,
  title        = {{What are the Chinese public's attitudes towards AI-generated portrait images? A text mining analysis based on social media}},
  author       = {Xiang He & XuWei Pan},
  journal      = {Aslib Journal of Information Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ajim-01-2025-0024},
}

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What are the Chinese public's attitudes towards AI-generated portrait images? A text mining analysis based on social media

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.