Landscape of Generative AI in Global News: Topics, Sentiments, and Spatiotemporal Analysis
CoRR(2024)
摘要
Generative AI has exhibited considerable potential to transform various
industries and public life. The role of news media coverage of generative AI is
pivotal in shaping public perceptions and judgments about this significant
technological innovation. This paper provides in-depth analysis and rich
insights into the temporal and spatial distribution of topics, sentiment, and
substantive themes within global news coverage focusing on the latest emerging
technology –generative AI. We collected a comprehensive dataset of news
articles (January 2018 to November 2023, N = 24,827). For topic modeling, we
employed the BERTopic technique and combined it with qualitative coding to
identify semantic themes. Subsequently, sentiment analysis was conducted using
the RoBERTa-base model. Analysis of temporal patterns in the data reveals
notable variability in coverage across key topics–business, corporate
technological development, regulation and security, and education–with spikes
in articles coinciding with major AI developments and policy discussions.
Sentiment analysis shows a predominantly neutral to positive media stance, with
the business-related articles exhibiting more positive sentiment, while
regulation and security articles receive a reserved, neutral to negative
sentiment. Our study offers a valuable framework to investigate global news
discourse and evaluate news attitudes and themes related to emerging
technologies.
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