The stories told about artificial intelligence (AI) play a central role in shaping public understanding, policy debates, and expectations about technological change. News media are especially influential in this process. Using a cross-regional corpus of 12,924 English-language news articles about AI published since 2022, we examine how storytelling about AI varies across media systems in four regions: North America, China, India, and the Middle East. Leveraging large language models, we extract the implicit narrative lessons conveyed by each article---commonly known as story morals---capturing value-laden interpretations that extend beyond thematic content or sentiment. We find that North American AI coverage is uniquely characterized by narratives of caution, while under-emphasizing narratives of innovation and collaboration that are more prevalent elsewhere. However, this pattern is largely explained by a broader regional negativity bias in North American news sentiment, rather than by heightened concern about AI itself. Our findings bring into view the culturally conditioned nature of AI news narratives.
Figures & tables
Figure 1: Example of our story moral extraction process.
Figure 2: Moral keywords most strongly / weakly associated with North American news stories.
Figure 3: Regional variation in the balance between caution and innovation narratives, which represent 50% of the total documents in our corpus.
Figure 4: Predicted probabilities of negative, neutral, and positive sentiment by article content from a multinomial logistic regression including region, article subject, their interaction, annotation model, and categorical prompt identity.
Figure 5: Log odds ratios (OR) of positive valence in North American news by industry for the top 15 industries.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Source Name
Region
AI
Non-AI
South China Morning Post
China
986
423
China Daily
China
700
577
The Times of India
India
1,766
493
Hindustan Times
India
902
315
The Hindu
India
499
192
The National
M. East
638
372
Appendix
Table A1: Article Counts by News Source, Region, and Category
Neutral vs. Negative
Positive vs. Negative
Term
Estimate
SE
p
Estimate
SE
p
Intercept
− 0.045
0.070
.519
− 0.709
0.075
< .001
Region: China
0.775
0.082
< .001
1.702
0.086
< .001
Region: Middle East
0.398
0.093
< .001
1.669
0.093
< .001
Region: India
0.411
0.067
< .001
1.708
0.069
< .001
Subject: Non-AI
− 0.536
0.077
< .001
− 0.055
0.088
.534
Appendix
Table A2: Multinomial logistic regression of article sentiment (25,848 annotations of 12,924 articles). Negative sentiment is the outcome reference. Coefficients are log odds. Standard errors are clustered by article (HC1). Reference categories: North America, AI-focused articles, Gemini-2.0-Flash, and prompt 1.
Unadjusted
Sentiment-adjusted
Term
Estimate
SE
p
Estimate
SE
p
Intercept
0.632
0.062
< .001
1.207
0.071
< .001
Region: China
− 0.780
0.054
< .001
− 0.430
0.057
< .001
Region: Middle East
− 0.454
0.061
< .001
− 0.019
0.063
.769
Region: India
− 0.663
0.046
< .001
− 0.240
0.049
< .001
Model: GPT-4o
− 0.727
0.022
< .001
− 0.704
0.024
< .001
Appendix
Table A3: Logistic regressions of cautionary moral framing in AI-focused articles (17,848 annotations of 8,924 articles), without and with sentiment. The outcome is whether any extracted moral keyword is coded as caution. Coefficients are log odds. Standard errors are clustered by article (HC1). Reference categories: North America, Gemini-2.0-Flash, prompt 1, and negative sentiment.
Caution
Innovation
accountability
adaptation
caution
adaptability
ethics
creativity
fairness
efficiency
justice
empowerment
privacy
growth
Appendix
Table A4: Moral keywords mapped to caution and innovation framing categories.