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.

翻译：生成式人工智能在改变各行各业及公共生活方面展现出巨大潜力。新闻媒体对生成式AI的报道在塑造公众对这一重大技术创新的认知与判断中起着关键作用。本文针对全球新闻中关于这一最新兴起技术——生成式AI的时空分布、主题、情感及实质性议题，进行了深入分析与丰富洞察。我们收集了涵盖2018年1月至2023年11月的全面新闻文章数据集（共24,827篇）。在主题建模方面，采用BERTopic技术并结合定性编码来识别语义主题；随后，利用RoBERTa-base模型进行情感分析。对数据时间模式的分析显示，各关键主题（商业、企业技术发展、监管与安全、教育）的报道覆盖率存在显著差异，文章数量激增时期与重大AI发展及政策讨论相吻合。情感分析表明，媒体立场以中性偏正面为主，其中商业相关文章呈现更积极的情感倾向，而监管与安全文章则保持保留性、中性至负面的情感态度。本研究为考察全球新闻话语、评估与新兴技术相关的新闻态度及主题提供了一个有价值的分析框架。