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Landscape of Generative AI in Global News: Topics, Sentiments, and Spatiotemporal Analysis

Lu Xian, Lingyao Li, Yiwei Xu, Ben Zefeng Zhang, Libby Hemphill

TL;DR

Landscape of Generative AI in Global News analyzes how media coverage shapes public understanding of generative AI. The study combines BERTopic-based topic modeling, qualitative coding, and RoBERTa-based sentiment analysis on 24,827 English-language articles from 2018–2023 to map topics, sentiment, and spatiotemporal patterns. It reveals a predominantly neutral-to-positive stance, with business and corporate technological development receiving more favorable sentiment, while regulation and security coverage is more reserved. The findings offer a framework for monitoring global news discourse around emerging technologies and for evaluating media attitudes toward AI adoption.

Abstract

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.

Landscape of Generative AI in Global News: Topics, Sentiments, and Spatiotemporal Analysis

TL;DR

Landscape of Generative AI in Global News analyzes how media coverage shapes public understanding of generative AI. The study combines BERTopic-based topic modeling, qualitative coding, and RoBERTa-based sentiment analysis on 24,827 English-language articles from 2018–2023 to map topics, sentiment, and spatiotemporal patterns. It reveals a predominantly neutral-to-positive stance, with business and corporate technological development receiving more favorable sentiment, while regulation and security coverage is more reserved. The findings offer a framework for monitoring global news discourse around emerging technologies and for evaluating media attitudes toward AI adoption.

Abstract

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.
Paper Structure (21 sections, 5 figures, 4 tables)

This paper contains 21 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Data
  4. Data Preparation
  5. Data Profiling
  6. Methodology
  7. BERT Topic Modeling
  8. Qualitative Coding
  9. Sentiment analysis
  10. Findings
  11. RQ1: When were news articles of different topics published and where?
  12. Temporal distribution of articles by topic
  13. Spatial distribution of articles by topic
  14. RQ2: What is the sentiment of articles?
  15. Temporal sentiment of articles by topic
  16. ...and 6 more sections

Figures (5)

  • Figure 1: Result of the Elbow method to determine the optimal K clusters in BERTopic modeling.
  • Figure 2: Temporal trends of news articles across topics.
  • Figure 3: Collected English news articles (a) Spatial distribution across countries and regions. (b) Count and percentage of news articles of top five countries across topics.
  • Figure 4: Weekly sentiment scores from November 2022 to November 2023, for the top four topics: corporate technological development, regulation and security, business, and education.
  • Figure 5: Semantic network for the most discussed four topics covered by news articles, (a) Business, (b) Corporate technological development, (c) Regulation and security, and (d) Education.