The findings of this paper consist of an overview of the hierarchical topic structure, a story map with geographic locations, and a topic evolution view integrating fragmented information.
Overview of hierarchical topic structure
To address our first research question (RQ1), about how AI ethics discourse is framed within Twitter discourse overall, we extracted all relevant tweets and clustered them into a hierarchical topic structure, as shown in Fig. 2. This structure consists of three layers. The first layer comprises seven main topics: Legal & Ethical, Society & Culture, Technology, Science & Research, Health & Safety, Education & Learning, and Business & Economics. Figure 2 presents a clear and intuitive visualization of the hierarchical structure of AI ethics discourse through the visualization of the sunburst chart. This shows the categories of each topic and the containment relationships between the hierarchies. Furthermore, the underlying 64 fine-grained topics (lowest level) have not been overlooked, covering mainstream public discourse and issues of small but critical scope, such as Intellectual Property in AI and Gender Discrimination.
A Hierarchical Thematic Structure of AI Ethics Twitter Discourse.
Table 1 is a codebook for the topic of AI ethics discourse. It describes each category of topics in the top-level structure and provides examples of topics discussed within each category. An in-depth analysis of the topics of the hierarchical structure can be found in the Discussion section.
In addition to the fine-grained hierarchical structure analysis, the temporal trend of topic discussions over time also conveys rich meaning. we chose stream chart and bar chart to illustrate the changes of the seven main topics related to AI ethics from 1 January 2015 to 31 December 2022 as Fig. 3A shows. The overall volume of discussions on AI ethics was relatively low during 2015–2016, gradually increasing from 2017 and peaking in early 2020. There was a slight decline in 2020, possibly influenced by the pandemic outbreak, which may have diverted substantial discourse resources. After 2020, the discourse on AI ethics remained relatively stable. Figure 3B shows that among the seven topics, a significant portion of the discussions revolve around “Legal & Ethical,” surpassing 90% of the total tweet volume. The remaining six topics have relatively similar amount of discussion, with “Business & Economics” being the least discussed. This skewed distribution, where a few categories (also called heads) contain a large number of samples, while most categories (also called tails) have very few samples, conforms to a long-tail distribution (Anderson, 2012). The long-tail distribution of topics related to AI ethics reveals that although public concern in this field is focused on Legal & Ethical discussions, niche topics such as Education & Learning and Business & Economics are also significant and should not be overlooked (Agarwal et al., 2012; Mustafaraj et al., 2011).
A Trend of the number of the seven topics over time; B graph of the distribution of the seven topics, with their numbers ranked from highest to lowest.
Visual analytics of AI ethics discourse in Twitter
To answer RQ2, this study presents coherent and readable visual analytics from two parts, including story map and topic evolution diagram.
Story Map: The World and The United States as Examples
We constructed a global story map and a more detailed story map of the United States, which has the highest tweet volume, as an example. Among them, the global story map combines mainstream AI ethics discourse with geospatial information, presenting the distribution of seven topics worldwide. The American Story Map integrates mainstream AI ethics discourse, sentiment information, and spatial locations, built upon the changes in the number of tweets related to AI ethics in the United States from 2015 to 2022.
Figure 4 illustrates the distribution of AI ethics-related tweets worldwide. Most AI ethics discourse is concentrated in the United States and Europe. Some countries, such as China and Cuba, have limited use of Twitter, so their distribution data may not provide accurate references. The surrounding maps in Fig. 4 display the distribution of the seven topics worldwide, they are generally similar but with some subtle differences. For instance, discussions on “Health & Safety” in African countries are more prevalent compared to “Technology” and “Business & Economics,” while India has more discussions on “Technology” and “Education & Learning” compared to other topics.
Global Distribution of AI Ethics Twitter Data and Distribution of Seven Topics Worldwide.
To analyze the information conveyed by the story map in Twitter data further, we present an example using the United States. Figure 5A displays the distribution of AI ethics-related tweets in the United States. We observe that the discussion of AI ethics topics is most concentrated in California, New York, and Massachusetts. This concentration might be attributed to the large population size, numerous high-tech companies, and the abundance of universities in these three states. Considering the “long-tail distribution” characteristic of the seven topics related to AI ethics, although the tail-end topics constitute a relatively small proportion, they still reveal significant information. After excluding the most discussed topic “Legal & Ethical,” we illustrate the distribution of the remaining topics in Fig. 5B. More than 50% of states focus on “Science & Culture,” with “Technology” as the next prominent topic. Interestingly, New Mexico is most interested in the “Health & Safety” topic. This may be due to the diverse social and cultural backgrounds of the different federal states. For example, California and Washington State are home to numerous large tech companies. Still, California’s industries include globally renowned tourism and film industries, while Washington State is known for aerospace and agriculture, resulting in differing AI ethics discourse between the two states.
A The overall distribution of AI ethics discourse in the United States. B The distribution of the seven topics of AI ethics discourse across US states. C The distribution of positive sentiments in AI ethics discourse across US states. D The distribution of negative sentiments in AI ethics discourse across US states. E The word cloud distribution of positive sentiments in AI ethics discourse. F The word cloud distribution of negative sentiments in AI ethics discourse. G The normalized curve showing the number of AI ethics-related tweets over time in the USA, with significant AI-related events annotated for context.
Figure 5C, D present the distribution of positive and negative sentiments related to AI ethics discourse in various states. Interestingly, the top five states with the strongest positive sentiment are the same as the top five states with the strongest negative sentiment: California, New York, Massachusetts, Washington, and Texas. This result reflects the consistent intensity of public sentiment; regions expressing positive sentiments do not necessarily have reduced negative sentiments. Furthermore, we explored the content discussed behind these positive and negative sentiments and displayed them using word clouds. Figures 5E, F show the word clouds corresponding to positive sentiment and negative sentiment respectively. This indicates that people discuss similar topics with different sentiments, focusing on data, humans, and artificial intelligence. However, those expressing positive sentiments are more likely to see the positive impacts of data and AI on humanity, while those expressing negative sentiments demonstrate more ethical concerns and are also more concerned about the potential problems with data. Figure 5G provides statistical information on the trend of AI ethics discourse in the United States over time, serving as background information for the story map.
Topic evolution view
We integrated fragmented AI ethics discourse information into readable and coherent narratives. Figure 6A depicts the evolution of AI ethics discourse worldwide over time, with some critical events related to AI as background information. Figure 6B, C illustrate the evolution of AI ethics discourse from 2015 to 2022. The horizontal axis represents the timeline, while the vertical axis indicates the magnitude of each topic. Since this topic evolution diagram aims to display the evolution of topics, time and quantity serve as reference information for the distribution of topic bubbles. We first extracted the main topics from the discourse in January 2015, identifying “Internet” as the most discussed one in that month. Then, by calculating semantic similarity, we selected five topics semantically most related to “Internet” in 2015, such as AI-tech, Crypto, etc. The topic with the latest timestamp in the previous year then evolved into the following year’s five topics, and so on.
A The normalized curve showing the number of AI ethics-related tweets over time in the world, with significant AI-related events annotated for context. B, C Topic evolution graphs on Twitter from 2015 to 2022, using the topic “Internet” as an example. B shows period from 2015 to 2018, while C shows the period from 2019 to 2022. The same color represents topics belonging to the same category within the seven topics of AI ethics discourse. The x-axis in the figure represents the timeline, indicating the appearance of topics over time, while the y-axis represents the number of topics. The higher the bubble representing a topic is distributed on the Y-axis in the graph, the larger its relative quantity. Note that this graph is a schematic, and the time and quantity do not represent precise values.
Figure 6 takes ‘Internet’, the most frequent keyword in AI ethics discourse in January 2015, as an example of how the story metaphor framework can be used to build fragmented information into easy-to-understand stories in cross-level fine-grained social media data mining. For other topics, we can also select an event that happened at a particular time and explore the topic discussions or other events triggered by the event. This paper starts with the evolution of the ‘Internet’. Through the consolidation of fragmented information related to AI ethics from 2015 to 2022, combined with background information, we present a topic evolution view. In 2015, the formal launch of Ethereum and incidents such as hacking of Bitcoin exchanges sparked discussions on “Crypto” and “Cryptocurrency” related to AI technology. In 2016, following the theft of Ether from the DAO, an Ethereum-based intelligent contract organization, attention towards financial technology continued to rise. Blockchain technology gradually began to be used in the financial sector to ensure security and compliance. In 2017, the continued development of AI technology drew multidisciplinary attention. Single-discipline advancements were no longer sufficient to address complex real-world issues, leading to an emphasis on interdisciplinary research. In 2018, public discussions on ethics reached unprecedented levels. In May of the same year, the European Union formally implemented the General Data Protection Regulation (GDPR), setting a benchmark for AI ethics regulations. In addition to “Ethics,” the public showed increased interest in cultural and economic-related topics. In 2019, the trade war triggered fluctuations in the global economic market. Bitcoin plummeted, leading to widespread closures of mining operations. The strengthening of the US dollar and continuous interest rate hikes by the Federal Reserve caused emerging market currencies to collapse. Topics such as “Economic Studies,” “Business Analytics,” and “Market Research” became focal points of discussion. In 2020, the outbreak of COVID-19 consumed a significant portion of social media resources, resulting in fewer discussions related to AI ethics. As the pandemic spread, many countries implemented surveillance and tracking measures to control its spread, leading to ethical debates on privacy and surveillance. The pandemic-induced home isolation and remote work shifted learning and work patterns, making topics like “Ethics & Artificial Intelligence,” “Collaborative Learning Community,” and “E-Learning Teleformacion” new focal points of discussion. In 2021, the global gaming platform Roblox became the first metaverse concept stock listed on the New York Stock Exchange, sparking discussions on “Augmented Reality” and “Virtual Reality.” In April of the same year, the European Union proposed the Artificial Intelligence Act (EU AI Act), the world’s first comprehensive legislative attempt to address the phenomenon and risks of artificial intelligence. The establishment of this AI regulatory framework led to an increase in discussions on “Ethical AI.” In 2022, DeepMind successfully predicted the structures of approximately 200 million proteins from 1 million species using AlphaFold, covering almost all known proteins on Earth, ushering humanity into a new era of digital biology. In April, the international academic journal Science revealed the mystery of human genes, announcing the completion of the first complete map of the human genome. Topics such as “Neuromuscular Network,” “Molecular Biology,” “Cellular Biology,” and “Ergonomic Design” became focal points of discussion.