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

Lu Xian 1, Lingyao Li 1, Yiwei Xu 2, Ben Zefeng Zhang 1, Libby Hemphill 1

###### 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.

## Introduction

Media plays an important role in disseminating information to the public and
shaping their perceptions of emerging technologies (Brossard 2013). News
reporting on artificial intelligence (AI)-related content can help foster
essential technology literacy among the general public (Nguyen and Hekman
2022). Studying generative AI is crucial, not only for its advanced
capabilities and applications beyond traditional machine learning technologies
but also due to the emerging challenges and societal implications, which
require specialized understanding and careful scrutiny. Despite the
significant potential of Generative AI to transform various of industries and
aspects of public life(Marr 2023), public understanding of this emerging
technology remains nascent. By analyzing news media coverage of generative AI,
researchers can understand how the public is exposed to media interpretations
of data and events related to this innovative technology, and how this may
form public opinions or judgments about generative AI.

In the realm of AI-related news, prior research has investigated the broader
context of AI news, often emphasizing Western mainstream media outlets.
However, the investigation into the reporting of Generative AI news remains
understudied, particularly from a global perspective (Sun et al. 2020; Chuan,
Tsai, and Cho 2019). As a result, the global media’s response to the rapidly
evolving generative AI industry remains unclear. Our work aims to bridge these
gaps by analyzing a large corpus of global AI-related news coverage. This
study also seeks to illuminate the general public’s exposure, sentiment, and
perceptions toward the latest advancements in innovative technology.

Our analysis is based on a large-scale dataset consisting of English-language
news articles about large language models (LLMs) published by a total of 703
US national, US local, and international news outlets. This dataset includes
over 24,827 articles collected over a period of 70 months, from January 2018
to November 2023, allowing for a comprehensive analysis of the evolution and
impact of LLMs since their conception. To the best of our knowledge, our data
represents the most comprehensive and current news resource available,
capturing the global discourse surrounding LLMs. In this paper, we address the
following research questions:

RQ1: After the emergence of Generative AI, what topics do news articles about
it focus on, and how does the quantity of news articles on these topics vary
temporally and spatially?

RQ2: How does the sentiment of news articles about Generative AI vary across
topics and the different categories of news outlets?

RQ3: What are the most popular topics covered by Generative AI news with
positive sentiment?

We found that after the introduction of ChatGPT in late 2022, news coverage of
generative AI has been marked by temporal and spatial variabilities in the
number of news articles. Those trends coincide with major events regarding
technological development and specific interests. Our sentiment analysis
reveals that generative AI is predominantly portrayed in a neutral to positive
light, echoing the optimistic tone towards emerging technologies consistently
documented in prior research (Garvey and Maskal 2020; Fast and Horvitz 2017).
However, in the context of generative AI, our analysis uncovers a more
reserved sentiment in reports focusing on regulation and security reporting.

This work makes the following contributions:

  1. 1.

First, we gathered a diverse range of global news articles spanning various
types of news outlets, countries, and time 111Our dataset is available upon
request.. This collection extends from the initial release of BERT in 2018 to
November 2023, allowing a comprehensive understanding of the global news
coverage and perspectives on the rapidly evolving generative AI technologies
since the inception of LLMs. This dataset can also be used to continue
exploring the global news discourse around generative AI and other emerging
technologies.

  2. 2.

Second, we constructed an extensive codebook for the topics of generative AI
news articles, which builds on both quantitative topic modeling and
qualitative manual coding results. This codebook encompasses applications of
generative AI across sectors, such as business and education, as well as
responses to generative AI like regulation and security. Our methodology and
the developed codebook offer a valuable framework for future researchers to
capture and assess the news coverage of future emerging technologies.

  3. 3.

Third, we utilized two approaches, sentiment and semantic analysis, to further
capture the attitudes and themes present in global news coverage of generative
AI technologies. Aligning with the overall optimistic views of emerging
technologies in early studies, our analysis also reveals a reserved tone in
coverage related to regulatory and security aspects of generative AI. This
dichotomy highlights the complexity of global media coverage regarding the
integration of generative AI into various industries and the social fabric,
emphasizing the nuanced nature of its reception across diverse contexts.

## Related Works

While prior research has not explicitly focused on analyzing news coverage of
Generative AI, several studies have delved into the broader context of AI news
coverage and provided nuanced insights into the specific themes and sentiment
analysis over time.

One study analyzed five major American newspapers (i.e., USA Today, The New
York Times (NYT), Los Angeles Times, New York Post, and Washington Post) from
2009 to 2018 and found that business and technology were the predominant
subjects in AI news coverage (Chuan, Tsai, and Cho 2019). Another study
analyzed the New York Times, Washington Post, the Guardian, and USA Today from
1977 to 2019, identified fourteen major topics, including research and
education, media products, health care, jobs, economy, and others (Sun et al.
2020).

Previous research found mixed sentiment analysis evidence for AI news
reporting. An automated content analysis reveals the rapid emergence of AI’s
ubiquity in the mid-2010s and demonstrates a growing critical tone in news
discourse over time among The NYT, The Guardian, Wired, and Gizmodo (Nguyen
and Hekman 2022). However, others found that the majority AI news reporting
was positive over six decades (1956 to 2018) among The NYT, Associated Press,
The International Herald Tribune, Reuters, CNBC, International NYT, and
Internet Video Archive) (Garvey and Maskal 2020). Similarly, the analysis of a
30-year news reporting from the New York Times also revealed overall
consistently optimistic tones for AI news coverage (Fast and Horvitz 2017).

While discussions about the benefits of AI were more frequent than its risks,
the discussions on AI risks were generally more specific (Chuan, Tsai, and Cho
2019). News reporting of AI showed growing concerns about loss of control,
ethical issues, and negative impacts on work in recent years, despite of
increasing hopes for AI in healthcare and education (Fast and Horvitz 2017).
Leading English-speaking global media outlets reported concerns include
privacy invasion, data bias, cybersecurity, and information disorder,
underscoring the importance of interventions to clarify the detrimental
impacts of datafication and automation on citizens (Nguyen 2023). Journalists
portrayed AI as sophisticated, powerful, and value-laden, but the perspectives
of ordinary citizens were notably absent in media discourses (Sun et al.
2020).

## Data

### Data Preparation

We collected news data from ProQuest TDM (ProQuest 2023c), a platform
encompassing multiple databases across disciplines, including newspapers,
magazines, dissertations, and other primary sources. To collect relevant news,
we conducted a search based on the following conditions as presented in Table
1: date, search terms, and ProQuest newsstream databases. Each ProQuest
newsstream database covers an array of news outlets. For example, North
Central Newsstream databases curate news articles published by state-level
news outlets in Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, North
Dakota, Nebraska, South Dakota, and Wyoming (ProQuest 2023c).

Search Category |  Search Conditions
---|---
Date |  2018-01-01 to 2023-11-18
Search terms |  Large language model, LLM, ChatGPT, BERT, GPT, PaLM, LLaMA
Newsstream |  African Newsstream, Asian Newsstream, Australia Newsstream, New Zealand Newsstream, Canadian Newsstream, European Newsstream, Latin American Newsstream, Middle East Newsstream, U.S. Hispanic Newsstream, U.S. Midwest Newsstream, U.S. North Central Newsstream, U.S. Northeast Newsstream, U.S. South Central Newsstream, U.S. Southeast Newsstream, U.S. West Newsstream
Table 1: Search conditions to collect related news articles from ProQuest.

We established our search time frame from January 2018 to November 2023,
aligning with the initial release of the widely acclaimed Bidirectional
Encoder Representations from Transformers (BERT) in 2018. Our focus for search
terms included “large language model,” along with popular LLMs such as BERT,
OpenAI’s GPT, Google’s PaLM, and Meta’s LlaMa. In addition, we included a list
of popular ProQuest newsstream databases worldwide (ProQuest 2023a).
Collectively, these criteria resulted in a total of 38,199 news articles.

We refined the acquired dataset in several ways. First, after detecting the
language of news articles, we included only articles that were written in
English for later analysis, which is the predominant language in our dataset.
Second, we removed duplicated news articles based on their title, content, and
publisher. After reading 100 randomly selected copies of news articles, we
found that the content of many articles was not related to generative AI but
was included in our dataset. This is because common names of popular LLMs
(e.g., “PaLM”) overlap with regular expressions (e.g., “palm”). Thus, we
implemented a third filter requiring the mention of popular LLMs alongside
their respective company names. For instance, “PaLM” needed to be paired with
“Google”. Implementing these conditions resulted in a final dataset comprising
24,827 news articles for subsequent analysis.

Prior to topic modeling, we further cleaned the text. This process included
removing short URLs, digits, emojis, and punctuation from news. Additionally,
non-informative stop-words like “the,” “is,” and “and” were eliminated from
the text. Subsequently, we tokenized each piece of news into individual words
and characters and then lemmatized to their base or stemming forms. The
resulting dataset contains 24,827 English news articles, with their full text,
title, author, publication date, publication country, and publication address.

### Data Profiling

Our dataset, sourced from the ProQuest Newsstream database, comprises 24,827
English-language news articles from a diverse array of publications. To give
an overview of the dataset, we categorized these news outlets into three
groups, based on the categorization used by ProQuest Newsstream databases,
such as (ProQuest 2023b), and corroborated by existing literature, such as
(Shearer and Mitchell 2021). The groups are: US national news outlets, US
local and specialized news outlets, and international news outlets. The
categorization details are provided in Table 2.

News outlet |  Pub title | Count |  Sampled news title
---|---|---|---
US national news |  Wall Street Journal | 607 |  “OpenAI to Offer ChatGPT Subscription Plan for $20 a Month;” “How Worried Should We Be About AI’s Threat to Humanity? Even Tech Leaders Can’t Agree”
US national news |  USA Today | 176 |  “Why Elon Musk wants to build ChatGPT competitor: AI chatbots are too ‘woke’;” “Sears pioneered the modern prefab house in the early 20th century: Look back at ‘kit homes”’
US national news |  The New York Times | 128 |  “Microsoft Says New A.I. Shows Signs of Human Reasoning;” “Google Tests an A.I. Assistant That Offers Life Advice”
US local and specialized news |  PR Newswire | 1921 |  “Pioneering Real-World AI: Wecover Platforms Brings Generative AI Experience to MBA Students at Georgia State University;” “Treehouse Adopts AI to Help Students Prepare for the Next Great Technology Wave”
US local and specialized news |  Business Wire | 1155 |  “Folloze AI, Powered by ChatGPT, adds Critical Layer of Buyer Engagement Insights to Drive Increased Revenue;” “Flatiron School Launches New Artificial Intelligence Training Programs”
US local and specialized news |  Barron’s | 431 |  “ $90 Billion Valuation for OpenAI? Tech’s New Star Is Red Hot;” “AI to Pick Stocks? JPMorgan’s Move Hints at Banks Following Big Tech’s Lead”
International news |  The Times of India | 753 |  “Google to launch new chatbots for advertisers and YouTube content creators;” “Samsung bans use of ChatGPT and other AI tools for employees”
International news |  The Guardian | 369 |  “Monday briefing: What the AI boom really means for your job (and mine);” “Everything you wanted to know about AI but were afraid to ask”
International news |  South China Morning Post | 275 |  “Baidu’s ChatGPT alternative gets positive reviews for handling of Chinese translations as search giant’s stock jumps;” “Alibaba tests ChatGPT rival as Chinese tech giants like Baidu race to build country’s best AI chatbot”
Table 2: Representative examples of categorization of news outlets.

Within the US national news outlets category, our dataset includes
publications like the Wall Street Journal, which alone contributes 607
articles and accounts for 60% of the articles in this category. Other major US
national news outlets in our dataset include USA Today (228, 22%) and The New
York Times (128, 13%). These outlets are generally recognized for their
national presence and have a significant impact on shaping public opinion and
national discourse.

Popular US local and specialized news outlets and publishers in our dataset
include: PR Newswire (1921, 24%), Business Wire (1155, 15%), Targeted News
Service (1124, 14%), University Wire (723, 10%), Politico (348, 4%), and
Boston Globe (111 2%). Publishers like PR Newswire and Business Wire usually
disseminate press releases and corporate news, while specialized news outlets
like Politico focus on political journalism and in-depth coverage of
Washington D.C. and policy, and local news outlets like Boston Globe cater to
specific local or regional audiences with news that resonates with their
immediate geographical and cultural context.

Within the international news outlets category, top outlets in our dataset
are: India’s The Times of India (753, 5%), Indian Express (645, 4%), Mint
(4%), IANS English (471, 3%), and Financial Express (448, 3%); UK’s
Telegraph.co.uk (393, 2%) and The Guardian (369, 2%); and Thailand’s Asia News
Monitor (286, 2%). These outlets represent a rich source of international
English news coverage and contribute to the geographical diversity of our
dataset, especially in Asia.

## Methodology

### BERT Topic Modeling

Topic modeling allows for the identification of semantic themes in a large
volume of textual data (Vayansky and Kumar 2020). To mine the themes from the
collected news data, we applied the BERTopic technique, which involves using
BERT word embedding to extract semantically relevant sentence embeddings from
documents. We chose BERTopic over Latent Dirichlet Allocation (LDA) for topic
modeling due to its distinct advantage in understanding the semantic meanings
of words with contextualized representation (Reimers and Gurevych 2019). Prior
research has also demonstrated that BERTopic performs better than LDA and
Top2Vec in identifying topics extracted from online posts (Egger and Yu 2022).

Due to the high dimensionality of the vectors generated by the BERT embedding
from news text that presents a challenge for machine processing, we utilized a
dimensionality reduction technique called Uniform Manifold Approximation and
Projection (UMAP) proposed by McInnes et al. (2018) (McInnes, Healy, and
Melville 2018). The UMAP method can help to mitigate high dimensionality
issues while retaining the local and global structure of the dataset (McInnes,
Healy, and Melville 2018).

Subsequently, we used the elbow method in conjunction with K-means to
determine the optimal number of clusters for topic modeling. The elbow method
is a graphical method that allows us to find the best K clusters by assessing
the Within-Cluster Sum of Squares (WCSS) — the summation of squared distances
between cluster points and their centroids. We implemented the experimentation
using numbers ranging from 2 to 300 clusters, as depicted in Figure 1. This
figure shows a significant reduction in WCSS, around 50 clusters. For a more
refined clustering result, we chose to use 100 clusters to manage our
collected news data. This handling enhances granularity and precision while
allowing the research team to use a bottom-up approach (i.e., manually
annotating each of the 100 clusters into categories) to validate these
clusters. With the determined optimal $K=100$, we applied K-Means clustering
(Buitinck et al. 2013) to group articles in the dataset into 100 clusters.
Specifically, we applied the model to the main text of news articles, given
that it holds richer information compared to news titles.

The final stage of our topic modeling process involves the representation of
topics. We used a count vectorizer technique called the class-based Term
Frequency-Inverse Document Frequency (c-TF-IDF) within the Scikit-learn Python
package (Grootendorst 2022) to tokenize the topics. This method can help to
extract the topical keywords and representative documents from each cluster.

Figure 1: Result of the Elbow method to determine the optimal K clusters in
BERTopic modeling.

### Qualitative Coding

Using BERTopic to cluster news articles prompted a subsequent need to
categorize the topics of the clusters of news articles. To do so, we first
employed an iterative approach that combined both top-down and bottom-up
processes to identify the potential topics. For consistency, we used the term
“cluster” to refer to the clusters returned by BERTopic and the term “topic”
to refer to the manually identified topic information from each cluster. In
the top-down phase, we referenced relevant papers, such as the work by (Sun et
al. 2020), which identified 14 key topics in news articles related to emerging
AI technologies. These encompassed education, healthcare, job market, life
science, business, risk assessment, art and creation, regulation and policy,
gaming, software, transportation, robotics, and algorithms. Drawing insights
from prior research enabled us to collect potential general topics the news
articles cover regarding emerging AI technology. For the bottom-up process, we
reviewed a random sample of 100 collected articles and manually assigned
topics to each article based on our understanding and expertise on the
subject. This bottom-up process ensured that the collected topics aligned with
our specific context and contributed to the completeness of our compiled list.

Upon collecting candidate topics, four authors of the research team engaged in
a brainstorming session to discuss and refine the identified topics. Following
three iterations, we finalized the topics into a set of 10 topics, including
education, business, labor and work, corporate technological development,
regulation and security, content creation, healthcare, politics, finance, and
a “miscellaneous” category covering areas like engineering applications and
robot development. We removed articles that are tangentially related to
generative AI in this process, which amounts to about 10% of the entire
dataset.

Next, we employed a “paired-coding” method to label each cluster output by
BERTopic. That is, each cluster was guaranteed to be labeled by two authors.
During the labeling process, each author first examined the representative
word list generated through the c-TF-IDF method in the BERTopic model. Then,
we reviewed a sample of news articles within each cluster. Next, each author
independently labeled the cluster and assigned it to one of the ten
predetermined topics. During this labeling process, we also observed a few
clusters that were unrelated to generative AI settings, which were excluded
from subsequent analysis.

After each author completed the labeling, we employed Krippendorff’s $\alpha$
(Krippendorff 2018), an inter-coder reliability index, to quantify the level
of agreement between independent annotators. In our experiment, the calculated
Krippendorff’s $\alpha$ is 0.88. As suggested, a Krippendorff’s $\alpha$ above
0.8 is generally indicative of good agreement (Krippendorff 2018). For
clusters that received different labels, we conducted an additional
brainstorming session. Through the discussion, all authors reached a consensus
on the annotation for each of these clusters. The results of the topic
annotation are listed in the table below.

Topic |  Explanation |  #Clusters |  #Articles |  Representative clusters (keywords)
---|---|---|---|---
Corporate technological development |  Development and innovation of generative AI led by corporate companies |  17 |  4798 |  chatgpt-language-model-user, bard-google-search-pichai, microsoft-copilot-openai-bing, user-image-content
Regulation and security |  Risks, ethical issues, and policies of AI, including AI regulation in global context |  15 |  4031 |  ai-congress-agency-government, attack-cybersecurity-cyber-phishing, security-cybersecurity-data-threat, eu-european-act-parliament
Business |  Impact on customer service and user experience, product management, marketing, and retail |  9 |  3177 |  data-cloud-customer-enterprise, customer-business-solution-data, generative-ai-business-customer, brand-customer-retailer-consumer
Education |  Generative AI’s applications in the school context, and its impact on students, teachers, and professors |  8 |  2710 |  student-school-teacher-education, student-college-professor-university, science-research-university-student, text-chatgpt-write-paper
Labor and work |  Generative AI’s impact on labor force and job market |  6 |  1925 |  job-worker-ai-work, job-hr-employee-skill, news-journalist-journalism-medium
Content creation |  The use of generative AI in creative fields, including writing, music, and art |  5 |  1117 |  writer-film-actor-strike, music-song-artist-elvis, art-image-artist-create
Finance |  Generative AI’s impact on investment, stock, cryptocurrency, or company revenue |  4 |  1049 |  investor-stock-investment-fund, financial-company-investment-investor, rypto-cryptocurrency-worldcoin-blockchain
Healthcare |  Generative AI’s applications in analyzing biomedical information and healthcare |  6 |  1043 |  health-patient-healthcare-care, medical-patient-doctor-health, drug-protein-cell-disease
Politics |  Generative AI’s impact on US politics and elections, and global politics of AI |  5 |  1008 |  trump-republican-ramaswamy-debate, labour-political-conservative, japan-kishida-minister-japanese
Miscellaneous |  Generative AI’s applications in other areas such as food and environment |  7 |  1371 |  energy-water-carbon-climate, food-restaurant-recipe-eat
Table 3: Topic identification and examples of representative clusters within
each topic.

### Sentiment analysis

With each news article categorized into a topic, we employed sentiment
analysis to identify whether the new article carries a positive, neutral, or
negative sentiment. This allowed us to further make the comparative analysis
from both temporal and spatial perspectives regarding the sentiment. We used
the RoBERTa-base model (Barbieri et al. 2020; Loureiro et al. 2022) to
implement the sentiment analysis.

RoBERTa is a robustly optimized Bidirectional Encoder Representations (BERT)
pre-training approach based on BERT embedding. BERT embedding is built on
transformer’s architecture and attention mechanisms to create contextualized
representations from text. The RoBERTa-based sentiment model was fine-tuned
for sentiment analysis using the TweetEval benchmark. (Barbieri et al. 2020)
also enhanced the model by integrating a dense layer to reduce the dimensions
in the last layer to match the number of classifications in the sentiment
task. This model has been demonstrated to outperform FastText and Support
Vector Machine (SVM)-based models employing n-gram features. Specifically, it
achieved an accuracy of about 72% on the TweetEval testing dataset, as
compared to the 63% accuracy achieved by SVM and FastText (Barbieri et al.
2020).

Loureiro et al. (2022) further enhanced the model using an expanded training
corpus of social media postings, consisting of 123.86 million tweets collected
until the end of 2021, a notable increase from its predecessor’s 58 million
tweets. This update resulted in an improved performance of sentiment analysis,
demonstrating an accuracy of 73.7% on the TweetEval dataset (Loureiro et al.
2022).

## Findings

Manually coding the news articles by a variety of themes gave us the following
distribution of news: there are 4798 articles (19%) on corporate technological
development, 4031 articles (16%) on regulation and security, 3177 articles
(13%) on business, 2710 articles (10%) on education, 1925 articles (10%) on
labor and work, 1117 articles (8%) on content creation, 1049 articles (6%) on
finance, 1043 articles (4%) on healthcare, and 1008 articles (4%) on politics.
About %6 articles cover other miscellaneous topics. Table 3 presents the
codebook used in qualitative coding and the relationship between topics of
articles and BERTopic clusters in more detail, with examples of representative
BERTopic clusters for each topic.

### RQ1: When were news articles of different topics published and where?

#### Temporal distribution of articles by topic

Based on topic modeling and qualitative coding results, we analyzed the
temporal distribution of article topics, centering on articles published
following the introduction of ChatGPT in November 2022. Figure 2 shows a time
series of the number of articles across different topics. The dominant topics
throughout this period—business, corporate technological development,
regulation and security, and education—underscore the impact of generative AI
across these topics. Over the span of a year, from November 2022 to November
2023, we observed notable variability in the volume of articles across topics.
The moments of high news coverage may be related to key developments or
product releases of generative AI, the shifting focus areas in news coverage,
as well as the news’ responsiveness to specific events in the course of new
technology development.

Figure 2: Temporal trends of news articles across topics.

For example, the topic of corporate technological development shows a
significant spike in articles in the weeks of February 12, 2023, and February
19, 2023. This could indicate a surge of interest or events related to the
introduction of Bard by Google on February 6 and Microsoft’s launch of an
updated version of its Bing search engine on February 7. In another example,
the topic regulation and security exhibits a surge in the week of May 7, 2023,
which might be related to the White House’s meeting with CEOs of large
technology companies on advancing responsible AI innovation on May 4. The
variability and volume of coverage for each topic would be indicative of their
relevance and newsworthiness.

#### Spatial distribution of articles by topic

In addition, we found distinct patterns reflective of regional focus and
interests by examining the spatial distribution of news articles by topic. For
this analysis, we focused on the top five countries by article count (US,
India, UK, Australia, and Canada), as illustrated in Figure 3.

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.

In the US, the topic of business constitutes the largest segment of articles
(2013 articles, 21% of all articles in this country), followed closely by
corporate technological development (1725, 18%) and education (1477, 16%). The
prominence of news coverage on those topics may reflect the country’s news
attention on business-related technological implications, technological
advancements by corporate companies, and the widespread impact of generative
AI on educational frameworks. India’s news distribution shows an overwhelming
majority of articles concentrated on corporate technological development
(1605, 38%). This focus could be indicative of the country’s burgeoning tech
industry and the rapid development of technology that drives media attention.
The UK exhibits a more balanced distribution, with a substantial portion of
articles dedicated to regulation and security (734, 31%), followed by
corporate technological development (452, 19%). This could suggest a
heightened awareness and engagement with generative AI in terms of governance
and the implications for security frameworks within the region. In Australia,
similar to the UK, a significant share of coverage is directed at regulation
and security (267, 22%), suggesting a national focus on generative AI’s
governance and ethical implications. This is closely followed by education
(249, 21%), which might reflect the country’s prioritization of educational
initiatives in generative AI. Canada’s news articles display a strong emphasis
on regulation and security (320, 35%), which is the most prominent topic,
overshadowing corporate technological development (135, 15%) and other topics.
This might suggest Canada’s proactive stance in addressing the complexities of
AI governance and the associated security challenges.

Comparing the news coverage across countries reveals distinct national
interests and priorities in generative AI. For instance, corporate
technological development and business are leading topics in the US and India,
which could reflect their positions as major hubs of technological innovation.
In contrast, the UK, Australia, and Canada show a heightened interest in
regulation and security, pointing to a more cautious and governance-oriented
approach. Australia’s focus on education suggests an investment in developing
human capital to navigate and leverage AI advancements.

### RQ2: What is the sentiment of articles?

We utilized a RoBERTa-based model to determine the sentiment of each article,
which categorized them into negative, neutral, and positive sentiments. The
overall discourse in the news articles on generative AI leans towards a
neutral and positive portrayal, with 66% neutral and 28% positive sentiment
articles. The tendency towards positive sentiment across diverse topics may
reflect an overall optimistic or progressive narrative in media reporting or a
trend in the media towards focusing on positive aspects or developments within
these areas, which has been documented by prior scholarship (Garvey and Maskal
2020). In this section, we delve deeper into the news articles by looking into
the topic, type of news outlet, and content to capture the nuances behind the
predominance of neutral and positive sentiments.

#### Temporal sentiment of articles by topic

For each of the top four topics (business, corporate technological
development, regulation and security, and education), we computed weekly
sentiment scores for articles within the topic for the time frame from
November 2022 to November 2023. The sentiment scores were calculated using a
scale where positive sentiment was assigned a value of 1, negative sentiment a
value of -1, and neutral sentiment a value of 0. These values were then
averaged on a weekly basis to produce a score ranging from -1 to 1 to
normalize for comparison.

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 4 presents a weekly sentiment analysis trend of articles across four
topics: corporate technological development, regulation and security,
business, and education. There are observable differences in sentiment across
the four topics. Business and corporate technological development generally
show more positive sentiment scores compared to the other topics, indicating
news articles on these topics are often reported with a positive tone. The
topic of education also trends positively, though with some variation,
suggesting a generally favorable portrayal in the news. Regulation and
security articles exhibit a markedly lower sentiment, often hovering around
the neutral to negative areas. This trend may reflect the inherently critical
nature of news related to regulatory measures and security issues, which often
involve reporting on conflicts, debates, and challenges within the realm of AI
governance. As such, the sentiment of news articles may influence public
perception and discourse around these aspects of generative AI.

#### Sentiment of articles by news outlet

Sentiment towards generative AI varies significantly across different types of
news outlets. For each of the three news outlet categories, we obtain a list
of sentiment scores using the same scale as before, where positive sentiment
was assigned a value of 1, negative sentiment a value of -1, and neutral
sentiment a value of 0.

We performed the non-parametric Kruskal-Wallis H-test test on sentiment scores
of three groups of news outlets: US national, US local and specialized, and
international news outlets. With a test statistic of 141.12 and a significant
p-value ($\leq$ 0.001), there is a statistically significant difference in
sentiment scores among these groups. This result is further substantiated by
the pairwise comparisons of Dunn’s test in Table 4 that accounted for multiple
comparisons. Specifically, international news outlets are more neutral
compared to US national news outlets, while US local and specialized news
outlets show a tendency towards more positive sentiment. This may imply that
US local and specialized outlets are experiencing more positive impacts or are
more optimistic about the potential of generative AI. Additionally, the
prevalence of positive sentiment in US local and specialized news might be
related to their sourcing strategies, which could include a higher proportion
of press releases from news wire services. Conversely, US national news
outlets may adopt a more cautious or critical stance, reflecting their broader
audience and perhaps a responsibility to present a more balanced view of the
developments in generative AI.

Type 1 | Type 2 | Mean Diff | Adj. p-value
---|---|---|---
International | US national | -0.069 | 0.0***
International | US local | 0.0813 | 0.0***
US national | US local | 0.1503 | 0.0***
*$p\leq$ 0.05, **$p\leq$ 0.01, ***$p\leq$ 0.001
Table 4: Dunn’s test for multiple pairwise comparison of news outlet types, as
well as the mean difference in sentiment scores between news outlet types.

### RQ3: What are the articles about?

In this section, we delved into the substantive themes within generative AI
news discourse. Building on the spatiotemporal and sentiment analysis
conducted in RQ1 and RQ2, which indicated a predominantly positive media
perspective across various topics, RQ3 seeks to elucidate the content that
characterizes these discussions. Considering the scope of the paper, we
focused on the four primary topics in news coverage of generative AI:
business, corporate technological development, regulation and security, and
education.

Through semantic network analysis—a method that visualizes the associative
relationships between terms in a given discourse (Van Eck and Waltman 2014;
Jung and Lee 2020)—we synthesized the common narratives and terminologies
prevalent in the positively-toned news articles within these domains.
Utilizing the VOSviewer tool (Van Eck and Waltman 2013), we aimed to unearth
the frequent sub-themes resonating in optimistic news narratives. The results
are presented in Figure 5.

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.

Within the semantic network of topic business (Figure 5(a)), the green cluster
centers around generative AI solutions and their applications at an enterprise
level. The interplay of terms like “enterprise”, “ai solutions”, and
“efficiency” suggests a focus on the pragmatic application of generative AI in
enhancing business processes and reflects an optimistic narrative that
appreciates the integration of generative AI, LLMs, and NLP applications in
business settings. A separate but equally compelling narrative emerges in the
blue cluster: “startup”, “technology”, “workforce”, “skill”, and “creativity”.
The lexicon of innovation illustrates a dialogue on AI’s role in redefining
professional landscapes and fostering creative economies. In addition, the
yellow-hued cluster emerges on the top of this network, centering on the
topics of company revenues, stock shares, and financial statements.

Corporate technological development stands out as another prominent topic in
the news discussions, and Figure 5(b) encapsulates the semantic network for
this topic. One significant cluster within this network underscores the
contributions of technology giants like “NVIDIA”, “Google”, “Microsoft”,
“Amazon”, “Meta”, and “Apple” to advancing LLM products. This cluster
underlines the tech industry’s focus on corporate growth, technological
innovation, and infrastructure. It also reflects the significance of LLMs and
their role in the current tech ecosystem. The other major cluster, represented
by the green segment, focuses on how LLM products can elevate knowledge, drive
design innovation, and benefit users. Prime examples of such advancements
include tools like “Copilot”, “Bard” and “Bing” Search. Additionally,
prominent figures frequently featured in the news, such as Elon Musk and Sam
Altman, are associated with these discussions.

Within the semantic network of regulation and security (Figure 5(c)), the
green cluster emphasizing “security”, “team”, and “solution”, which indicates
a discourse centered around cybersecurity and collaborative approaches to
safeguarding digital ecosystems. This narrative is complemented by the
intertwining of national and global “governance”, hinting at the regulatory
and ethical dimensions of AI technology. The blue cluster highlights the
discussion of whether to embrace current generative AI trends. These
discussions emphasize the ongoing development of generative AI as a prevailing
trend. While the progression has revolutionized knowledge and skills, enabling
bots to engage in interactive conversations with users, articles within this
cluster advocate for regulating AI use for the betterment of humanity.

Last, the education network reveals three distinct clusters (Figure 5(d)). The
green cluster spotlights the implementation of LLMs within academic
institutions and industry, encompassing terms like “college”, “institute”,
“faculty”, “staff”, “professor”, and “professional”. This suggests a
significant emphasis on AI’s role in personalizing learning experiences and
managing educational resources. The blue cluster hints at a narrative
surrounding “answer”, “homework”, “assignment”, “teacher”, “parent”, and
“creativity”, indicating a focus on the human elements of education, including
the impact on students and educators. The overall discourse explores the
potential of AI to support or transform traditional educational roles and
outcomes. The yellow cluster suggests a more technical discussion is
illustrated by the presence of terms like “large language model”, “gpt”,
“outcome”, “platform”, and “tool”, suggesting an examination of specific AI
tools and their implications for educational technology.

## Discussion and Conclusions

#### Key findings

Our findings provide a comprehensive portrayal of generative AI’s reception in
global news media based on an in-depth descriptive analysis of news articles
after the introduction of BERT in 2018. This paper offers rich insights into
the temporal and spatial distribution of topics, sentiment, and substantive
themes in major topics of news articles on generative AI.

Temporal patterns reveal variabilities 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, such as the release of Google’s Bard and Microsoft’s Bing
in early 2023, and the White House’s AI meeting in May 2023. Spatially,
there’s a clear delineation of focus, with the US and India leading in
business and tech development coverage, whereas the UK, Australia, and Canada
are more attuned to regulation and security. Notably, Australia exhibits a
unique concentration on education.

Sentiment analysis via a RoBERTa-based model indicates a predominantly neutral
to positive media stance, with 66% of articles being neutral and 28% positive.
News articles on topics like business and corporate technological development
show a more positive sentiment, and articles on regulation and security show a
more reserved, neutral to negative sentiment, reflective of the critical
nature of the discourse in these areas. This finding underscores the intricate
nature of global media’s portrayal of generative AI’s integration into
different industries and societies, reflecting the diverse responses it
receives in different contexts.

In terms of content, business-related articles focus on the pragmatic
application of generative AI at an enterprise level, with a parallel narrative
on AI’s influence on reshaping professional spheres and fostering creative
economies. News about corporate technological development is marked by the
contributions of tech giants to LLM products and their implications for user
benefit and industry innovation. Regulation and security discussions pivot
around cybersecurity, collaborative solutions, and the balancing act between
embracing innovation and establishing governance for AI’s ethical use.
Education-related news coverage unveils three narratives: the integration of
AI in academic and professional development, the impact on traditional
educational roles, and a technical exploration of AI tools in educational
settings.

#### Limitations and opportunities for future work

Our study presents several limitations, which in turn illuminate potential
paths for more comprehensive and detailed research in future endeavors.
Firstly, our data collection was restricted to English articles, which could
result in biased analysis for those non-English-speaking countries,
particularly in the global south. Future efforts could involve collecting news
articles in diverse languages, which would provide a more comprehensive global
perspective. Limitations in data collection also stem from our access to
ProQuest’s newsstream databases, which is contingent on our university’s
subscription policies, as well as from ProQuest’s own management and curation
of these resources. Although full-text access to the US major dailies
newsstream database is not available through our portal, we accessed full-text
articles from these outlets via other ProQuest newsstream databases. While we
cross-validated these articles by consulting the original news outlets, this
approach may not capture the full spectrum of US national news coverage. To
address this gap, future research could focus specifically on US national news
outlets to ensure a more comprehensive analysis of such news articles.

Secondly, the topic modeling approach presents two limitations. First, the
assumption that each document contains only a single topic doesn’t always
align with the complexity of news articles, potentially leading to suboptimal
representations by BERTopic. In addition, we observed instances where
clustered topics encompassed news items from different categories. This is
possibly because BERTopic clusters rely solely on textual similarity for
clustering. Particularly, those clusters with a significant number of news
articles are more likely to have diverse topics. To overcome this limitation,
future research could explore LLMs, such as integrating GPT-based models into
the topic modeling process, allowing for the generation of multiple topics for
a single news article.

Thirdly, future work could consider improving sentiment analysis based on more
granular units (e.g., paragraph-level or sentence-level) for news articles.
Our current sentiment analysis provides a singular classification for each
news item, regardless of the potential for conflicting sentiments within an
article. This could affect our analysis, especially when a news article covers
some topics that might be unrelated to Generative AI. Moreover, the accuracy
of sentiment analysis relies on the RoBERTa-base model’s capabilities, which
weren’t specifically trained for sentiment analysis in the news context,
possibly leading to inaccuracies. Therefore, another future direction could
involve a more nuanced analysis, such as sentiment examination at the
paragraph level in news articles.

## Acknowledgments

We would like to express our gratitude to the ProQuest team for providing us
with the tutorial on accessing their data.

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