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Article

Analyzing Trends in Digital Transformation Korean Social Media Data: A Semantic Network Analysis

by
Jong-Hwi Song
1 and
Byung-Suk Seo
2,*
1
Division of Software, Yonsei University, Wonju City 26493, Republic of Korea
2
Department of Computer Engineering, Sangji University, Wonju City 26339, Republic of Korea
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2024, 8(6), 61; https://doi.org/10.3390/bdcc8060061
Submission received: 8 March 2024 / Revised: 16 May 2024 / Accepted: 2 June 2024 / Published: 4 June 2024
(This article belongs to the Special Issue Challenges and Perspectives of Social Networks within Social Computing)
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Abstract

:
This study explores the impact of digital transformation on Korean society by analyzing Korean social media data, focusing on the societal and economic effects triggered by advancements in digital technology. Utilizing text mining techniques and semantic network analysis, we extracted key terms and their relationships from online news and blogs, identifying major themes related to digital transformation. Our analysis, based on data collected from major Korean portals using various related search terms, provides deep insights into how digital evolution influences individuals, businesses, and government sectors. The findings offer a comprehensive view of the technological and social trends emerging from digital transformation, including its policy, economic, and educational implications. This research not only sheds light on the understanding and strategic approaches to digital transformation in Korea but also demonstrates the potential of social media data in analyzing the societal impact of technological advancements, offering valuable resources for future research in effectively navigating the era of digital change.

1. Introduction

The digital era’s boom has ushered in a paradigm shift known as the “digital transformation”, which significantly impacts various societal sectors, including business, education, and governance [1,2]. This shift transcends mere technological upgrades, signifying a comprehensive overhaul of organizations, culture, and operations [1]. As our society becomes increasingly digitalized, with a rise in public and private services based on such technologies, the ability to adapt to variable situations has become crucial [2].
In the face of inevitable digital transformation across most industries, companies facing a challenging internal and external business environment, especially those with limited resources, have been compelled to seek breakthroughs via open innovation in corporate and business models [3,4]. Accelerated by pandemics or radical technological innovations, businesses have had to continuously evolve and adjust their innovation strategies to maintain their operations [5].
Additionally, digital transformation in business dismantles barriers among people, businesses, and objects, enabling the creation of new products and services and the genesis of new ventures [6]. It emphasizes the construction of new business models, processes, and software and systems that lead to increased revenue, competitive advantage, and higher efficiency [6].
In this context, social media becomes a vital tool for capturing public sentiment and social trends. Especially regarding the digital transformation, the role of these platforms in shaping public discourse is crucial. Considering Korea’s high social media penetration rate in harmony with traditional values and technological advancements, it presents an opportunity to gauge the social and individual impacts of the digital transformation.
Within the context of social media, semantic network analysis holds particular importance. It enables mapping how ideas, trends, and sentiments interconnect and evolve over time within digital conversations. Researchers can gain insights into public opinion, emerging trends, and the cultural zeitgeist of specific communities or societies by examining these networks [6,7]. This approach not only enhances our understanding of the dynamics of digital communication but also offers practical applications across various domains, from marketing to political science, providing a lens through which to view the complex tapestry of human thought and expression in the digital realm [7,8,9].
This study applies semantic network analysis techniques to social media data from Korea, focusing on the keyword “digital transformation” to analyze the relationships between keywords and phrases within social media posts. This method reflects systematic and meta-analytical techniques previously employed, with an emphasis on distilling core ideas from extensive data. Moreover, the research integrates perspectives based on social media, particularly within the non-Western context of Korea, to provide insights into the concept of digital transformation.

2. Related Studies

2.1. Digital Transformation

Digital transformation involves using new digital technologies such as social media, mobile technologies, analytics, or embedded devices to enable key business improvements, including enhanced customer experience, streamlined operations, or new business models [10]. It represents the use of technology to fundamentally improve a company’s performance or reach, encompassing changes related to the application of digital technologies in all aspects of human society [11,12].
Digital technologies can be seen as key assets for leveraging organizational innovation, considering their disruptive nature and inter-organizational and systemic effects [13]. To achieve successful digital transformation, changes must occur at various levels within an organization, including the exchange of resources and capabilities, adaptation of core businesses, restructuring of processes and structures, and practical implementation of a digital culture [14,15,16,17].
It is argued that digital transformation needs to capture both technology-centric and actor-centric perspectives [18]. For leveraging the technology-centric view, the literature on technological disruptions was included and merged with research on digital transformation [18]. Regarding the actor-centric perspective, intrinsic implications were derived from the field of entrepreneurship, which is seen as capable of adding valuable insights into action-driven innovation and renewal processes within the framework [18].
The rapid development of various digital technologies enables the transformation into digital service, thereby facilitating the accelerated growth of the service industry through digital transformation [19,20]. Considered foundational technologies for digitalization, IoT (Internet of Things), cloud computing, and Big Data analytics provide service firms with the capacity to develop customer-oriented business models [21,22]. Manufacturing companies are also shifting their primary focus to ecosystems that integrate products with services to maximize customer value [20].
The recent trend is evolving the world into a single competitive market through one platform [23]. Consequently, suppliers and buyers strive to secure a competitive advantage by offering more choices in an increasingly fierce market [23]. As a result, digital transformation becomes a key strategic force that can enable innovation for creating customer value [23].

2.2. Big Data and Semantic Network Analysis

The term “Big Data” has attracted considerable attention since the early 21st century, with various researchers attempting to establish a widely accepted definition. One of the most common definitions introduced the challenge of Big Data through the 3Vs: volume (large amounts of data), velocity (rapid data streams), and variety (heterogeneous content) [24]. Big Data has been defined as large volumes of structured or unstructured data, indicating that traditional data processing technologies struggle to manage and process it due to the data’s complexity and volume [25].
From a corporate perspective, the same information is required across various aspects such as customers, their needs, competition, products, distribution channels, service providers, and laws, making Big Data analytics necessary for making informed decisions [26]. Mobile marketing and social media platforms can extend knowledge by incorporating detailed personal information such as geographical location, time, interests, and gender [26].
Data exist almost everywhere in business and everyday life, and their volume is continuously increasing [26]. With the growing amount of data, scalability issues have become apparent, leading to increased processing times [27,28]. However, combining traditional algorithms with Big Data technologies has played a role in mitigating these scalability issues [27,29].
Among various Big Data analytics techniques, semantic network analysis is a method that models semantic relationships represented by graphs with nodes and edges [30]. Semantic networks can be automatically extracted from unstructured text data and used as a medium for visual text analysis, incorporating information retrieval and text mining techniques to extract relationships within the text [31,32,33].
Compared to traditional methods of text data analysis, semantic network analysis allows for the objective and accurate understanding of the structural relationships between individual words and the overall context, with relatively less reflection of the researcher’s subjective thoughts [34,35]. In a semantic network model, nodes represent semantic or lexical units, while edges denote the associations and similarities, co-occurrences, or intensity between them [36,37]. Representing relationships with graphs that have labeled nodes and edges enables the identification of semantic relationships, patterns, and similarities between words regarding a specific topic, making it easier to discover insights [34,38]. Therefore, semantic network analysis can be actively used to explore the qualitative aspects or intrinsic meanings of issues by focusing on relationships within online Big Data, such as news on portal sites or posts on social media.
Data from various social media posts, news, and blogs on internet portal sites have become major sources supplying the raw materials necessary for Big Data analysis. Therefore, this paper aims to identify public perceptions related to digital transformation and discover widely recognized trends using text mining techniques and semantic network analysis.

3. Method

In this study, we analyzed the key thoughts of Korean users on digital transformation using text mining techniques and semantic network analysis on Big Data collected from the internet. Text mining is the process of extracting meaningful information from unstructured text data, exploring core themes and trends from multiple perspectives. Furthermore, to understand the relationships between the extracted keywords, semantic network analysis was utilized. This paper outlines an analytical process to comprehend the semantics between words related to digital transformation in online news articles and blogs, based on the degree of their co-occurrence. The overall process is illustrated in Figure 1.

3.1. Data Collection

For the semantic network analysis conducted in this research, a search was performed on Korea’s two major portals, Naver [38] and Daum [39], using the keyword “digital transformation” to collect data from online news and blogs. Based on this, 1236 online news articles from Naver News and 1137 blog posts from both Naver and Daum blogs were collected as the data for analysis.
Online news article texts were collected exclusively from Naver, as most Korean news articles can be accessed through it. However, given the occurrence of various news outlets providing identical articles, duplicates were removed from the collected news articles using cosine similarity on the texts. Since Naver and Daum blogs rarely contain posts with identical content on both platforms, no duplicate checks were conducted when collecting data from these blogs. Although the collection period was not specified, it was confirmed that over 90% of the data originated from within the last ten years.
During the collection process, it was observed that some websites had anti-crawling features. To circumvent these, the Selenium library, implemented in Python for automating web browser interactions, was utilized. The data thus collected were processed using the BeautifulSoup library and stored in the form of DataFrames using the Pandas library.

3.2. Data Extraction and Preprocessing

The extraction and preprocessing of the data were performed using KoNLPy, a Python open-source library for natural language processing of the Korean language [40]. Utilizing KoNLPy, only nouns, verbs, and adjectives were selected as Korean unigrams, and stopwords, which are commonly used or insignificant words, were excluded to filter the data. The refined list of words was then used to calculate their TF-IDF (Term Frequency-Inverse Document Frequency) values, enabling the identification and weighting of the most relevant words within the dataset.
TF-IDF formally measures how the occurrence of a given word is concentrated in relatively fewer documents. It is calculated by multiplying two metrics: the word frequency in a document and the inverse document frequency of the word across a set of documents. This value is primarily used to gauge similarity within documents, in addition to assessing the relevance of a document in search queries and the importance of specific words in search results [41,42]. TF-IDF helps in highlighting words that are distinctive to certain documents, thereby facilitating more accurate and meaningful analysis of textual data.
After sorting the extracted TF-IDF word list in descending order, the top 50 words were selected as nodes for the semantic network analysis. During the selection of words, unrelated terms such as “person” and “society” were excluded, and semantically similar words were consolidated. For example, the frequency of “core” was combined with the frequency of “center”, a similar term.
Based on the 50 selected words, a Document-Term Matrix (DTM) was created, representing the frequency of each word across various articles and blogs. DTM enables the quantification of the relationship between words and documents. Subsequently, a Co-Occurrence Matrix (COM) was constructed to represent the relationships of word co-occurrences across all documents. Due to the complexity of the analysis with the generated COM, all values were binarized by changing values higher than the median of all elements to 1 and those lower to 0, resulting in a binary matrix. This process simplifies dense values to 1 and 0, creating a looser relationship for network analysis. The semantic network analysis utilized this binary-structured keyword COM.

3.3. Semantic Network Analysis and Visualization

To discover the relationships among the top 50 words related to digital transformation, a semantic network analysis was conducted. This leverages the data mining techniques for unstructured Big Data analysis, a method distinct from social network analysis, which identifies the structural characteristics of social phenomena [43]. The co-occurrence relationships among the refined words within social media data were intuitively visualized using NetDraw 2.175, a network visualization software, with the previously created keyword COM [44].
To examine the connection structure of words related to digital transformation, the Python open-source package NetworkX [45] was utilized. Four types of network centrality metrics [46] were calculated using NetworkX for the keyword COM as follows:
  • Degree centrality, which calculates the number of nodes connected to a specific node, indicating the node’s activity or popularity within the network;
  • Betweenness centrality, measuring a node’s mediating role within the network, indicating its importance in facilitating information flow between other nodes;
  • Closeness centrality, calculating the inverse of the average distance to all other nodes, indicating how close a node is to all other nodes in the network, which can suggest its accessibility or centrality in the network’s communication pathways;
  • Eigenvector centrality, a measure of a node’s influence in the network, indicating not just how many connections a node has but also how important those connections are.
To identify mutually exclusive subgroups within the semantic network, a CONCOR (Convergence of Iterated Correlations) analysis was performed. CONCOR is based on structural equivalence, iteratively dividing nodes into subsets and then analyzing the Pearson correlation to identify groups with a certain level of similarity before forming clusters that include these groups [47]. This method is commonly used to find clusters of similar keywords and to identify the co-occurrence relationships between words across all possible terms [48]. UCINET 6.0 [49] was utilized to conduct the CONCOR analysis, and the results were visualized using NetDraw.

4. Results

4.1. The Frequencies of Keywords Related to Digital Transformation

The results of the word frequency analysis from online news articles and blogs, showing the top 50 words, are presented in Table 1 and Table 2. The top five keywords from online news articles were “Education”, “Innovation”, “Corporation”, “Information”, and “Artificial Intelligence”, highlighting a focus on how digital changes impact education, business innovation, and the integration of AI across sectors. Blogs, however, put “Artificial Intelligence”, “Corporation”, “Education”, “Data”, and “Innovation” at the forefront, indicating a stronger emphasis on the technical aspects of digital transformation, such as AI and data utilization, while still valuing education and innovation. This nuanced difference between online news articles and blogs suggests varying degrees of engagement with digital transformation themes across different platforms, but both recognize the importance of education and innovation in adapting to and capitalizing on digital advancements.

4.2. Analysis of Centralities of Keywords Related to Digital Transform

In Section 4.1, we initially present the raw frequency results as shown in Table 1 and Table 2. These results depict the unmodified occurrence of keywords across our dataset with common stopwords filtered out. Following this initial analysis, we apply the TF-IDF method to refine these frequencies, thereby highlighting words that hold unique significance within our corpus. The relationships and centrality of these keywords are then explored in greater depth using a Document-Term Matrix (DTM) and a binary-structured Co-Occurrence Matrix (COM).
Table 3 presents the results of the network centrality analysis from the keyword COM for online news articles. The keyword “Innovation” had the highest degree of association with other keywords, resulting in the highest degree centrality, followed by “Education”, “Artificial Intelligence”, “Support”, and “Data”. The order of betweenness centrality was high for “Data”, “Education”, “Innovation”, “Support”, and “Artificial Intelligence”. Closeness centrality was high for “Innovation”, “Education”, “Artificial Intelligence”, “Support”, and “Data”, in that order. Eigenvector centrality was high for “Innovation”, “Artificial Intelligence”, “Support”, “Education”, and “Corporation”.
In the analysis of network centrality from the keyword COM for blogs, as detailed in Table 4, “Artificial Intelligence” emerged as the most centrally connected term, exhibiting the highest degree of association with other keywords. This centrality was closely followed by the terms “Data”, “Corporation”, “Innovation”, and “Service”, in that order. Furthermore, “Artificial Intelligence” also led in betweenness centrality, suggesting its role as a pivotal bridge within the network. This pattern was similarly observed in closeness centrality, with “Artificial Intelligence”, “Data”, “Corporation”, “Innovation”, and “Service” ranking high, indicating their close connections within the network. Additionally, “Artificial Intelligence”, “Data”, “Corporation”, “Service”, and “Development” were found to have high eigenvector centrality, highlighting their influence across the network.

4.3. CONCOR Analysis and Visualization

A CONCOR analysis was conducted based on structural equivalence by analyzing the Pearson correlation from the keyword COM, resulting in clusters. Figure 2 presents the outcome of the CONCOR analysis performed on the digital transformation network generated from online news, identifying a total of seven clusters. The clusters are represented as [Word1, Word2, …]. The cluster [Operation, Data, Bio, Development, Contents, Plan, Smart, Construction] can be interpreted as embodying the theme of technological advancement and strategic growth. The cluster [Support, Government, Corporation, Field, Project, Promotion] suggests that the government and various corporations collaborate to support innovative projects aimed at advancing key industrial sectors. The cluster [Training, Infrastructure, Cloud, Leading, Competence, Institution, Personal Information, Finance, Professor] represents the context of education, technology, and expertise development within an institutional framework. The [Software, Tech, Study, School, Student] cluster indicates an education or learning environment focused on technology and software. The cluster [Information, Innovation, Artificial Intelligence, Education, Future] encompasses future-oriented and technology-driven themes. The [Citizens, New, Human Resources, Investment, Region, Cooperation, Service, Metaverse] cluster can be seen as focusing on community and technological development within geographic or digital spaces. Lastly, the [Strategy, Platform, Society, Policy, Center, Global, Economy, Nation, Era] cluster can be interpreted as countries developing policies centered around digital platforms to drive economic growth or societal development in the global era.
Figure 3 depicts the results of a CONCOR analysis on the digital transformation network generated from blogs, identifying a total of seven clusters. The cluster [Finance, Corona, Study, Space, Big Data, Change, Economy] can be interpreted as showing that the coronavirus pandemic has accelerated the digital transformation of finance and the economy, with research highlighting the importance of Big Data and digital spaces in driving change. The cluster [Personal Information, Citizens, Strategy, Information, Plan] suggests that themes of personal information, public engagement, and strategy are crucial regarding information. The cluster [Nation, Government, Era, Society, Policy, Future, Global] presents themes related to national and global governance, societal adaptation, and future-oriented policies in a digitally evolving world. The cluster [Development, Center, Education, System, Platform, Field] indicates that digital transformation is central to the development of the educational sector, platforms, and systems. The cluster [Region, Leading, Cloud, Infrastructure, Online, Market] points to a focus on regional development through cutting-edge cloud infrastructure and online marketplaces. The cluster [Project, Construction, Metaverse, Promotion, Corporation, Smart, Support, Service, Data, Innovation, Artificial Intelligence] represents a comprehensive approach to integrating advanced technologies into corporate projects and services. This can be interpreted as innovative projects initiated by corporations to focus on building smart services like the metaverse, supported by artificial intelligence and data analytics, to facilitate a new era of digital transformation and customer engagement. The cluster [New, Professor, Industrial Revolution, Human Resource, Software, Research, Science and Technology, Business] expresses the narrative of education and industrial evolution towards a technologically advanced future, emphasizing the collaborative role of academia and industry in pioneering R&D efforts using cutting-edge software and human resource innovation to underpin the digital transformation of businesses and society.

5. Discussion

The arrival of the digital transformation era brings technological advancements and consequential changes, fundamentally restructuring educational, occupational, and everyday life practices. The integration of digital technologies presents new opportunities and challenges across all age groups. This research utilizes text mining techniques to analyze social media data generated online on a large scale, aiming to understand the phenomena of digital transformation and its impacts. It delves into the effects of digital technology on various sectors such as society, economy, and education, seeking adaptation strategies and policy responses necessary for navigating the digital age. Also, this study has been conducted to elucidate the distinct patterns observed in both formal (articles) and informal (blogs) discourse on digital transformation within the Korean context. By analyzing these diverse sources of content, our intent is to provide a comprehensive view that enables readers from various countries to gain a nuanced understanding of how digital transformation is perceived and discussed in Korea.
Text mining related to digital transformation revealed the top five words with the highest frequency in online news articles as “Education”, “Innovation”, “Corporation”, “Information”, and “Artificial Intelligence” and in blogs as “Artificial Intelligence”, “Corporation”, “Education”, “Data”, and “Innovation”. These results indicate the significant impact of technological advancement in various fields such as education, innovation, corporations, and artificial intelligence [50,51]. Additionally, artificial intelligence has been confirmed as one of the key elements in the era of digital transformation [52,53].
To refine our analysis, we employed the TF-IDF methodology, which assists in distinguishing significant keywords from those frequently appearing across different texts without substantial informational value.
An analysis of online news articles showed high centrality for “Innovation”, “Education”, “Artificial Intelligence”, “Support”, and “Data”. These words are strongly interconnected as the main themes of digital transformation, highlighting the interaction between these themes in the context of technological progress in modern society, the evolution of education, data-driven decision-making processes, the expansion of artificial intelligence applications, and the importance of supporting systems in all these areas. A blog centrality analysis highlighted “Artificial Intelligence”, “Data”, “Corporation”, “Innovation’”, and “Service” as highly central. While “Education” and “Support” were emphasized as important themes in news articles, “Corporation” and “Service” showed greater centrality in blogs, suggesting that interests may vary depending on the community or platform discussing digital transformation. Blogs tend to focus more on in-depth analysis or opinions on personal or corporate experiences, products, and services, particularly highlighting corporate activities and service provision. In contrast, online news pays attention to broader topics like education and social support, dealing with the impact of digital transformation across society. The high centrality of “Innovation”, “Data”, and “Artificial Intelligence” in both mediums suggests that the advancement of digital technology is interconnected in areas such as innovation, data analysis, and artificial intelligence. The technological innovation enhances data-based decision-making processes, and the advancement of artificial intelligence enables new innovative solutions, driving change across various fields, as emphasized in other studies [54,55,56].
The CONCOR analysis identified seven clusters each in online news and blogs related to digital transformation, confirming the importance of technology and education in both mediums. The emphasis on technological advancements, particularly digital technologies like artificial intelligence, cloud computing, and Big Data, alongside education, is recognized as a leading force in driving the digital transformation. It suggests a focus on innovating educational systems to introduce new learning methods and competency development, potentially causing significant changes across the economy and society [57,58]. The acceleration of digitalization in economic activities due to the pandemic is a phenomenon already reported in various studies [59,60,61].
Online news tends to focus more on digital transformation support and the promotion of industry development through collaboration between governments and corporations. Words such as “Government”, “Corporation”, “Support”, “Field”, “Project”, and “Promotion” highlight the strategic partnerships’ vital role in supporting digital transformation and fostering innovative projects in specific industrial sectors. For small-scale service businesses, the digital transformation aims to expand competitive advantage, improve business outcomes, and achieve growth. The government’s role in this context is identified as supporting the construction of digital platforms for small-scale service businesses, enabling mobile/digital payments, providing digital education, and building a digital collaboration ecosystem [62]. In contrast, blogs, with terms like “Personal Information”, “Citizens”, “Strategy”, “Information”, “Plan”, and “Metaverse”, reflect how individuals and small communities integrate and use digital technologies, especially innovative services like artificial intelligence and the metaverse, in daily life and business, indicating experiences and impacts on these practices.
Thus, online news tends to view digital transformation from the perspective of policy, economy, and national strategy, while blogs explore it from a standpoint closer to everyday life. This difference stems from each medium’s purpose and target audience [63]. Online news aims to provide information to a broad readership, offering insights useful to policymakers and businesspeople, whereas blogs cater to personal interests, in-depth analysis, and detailed exploration of specific topics, providing customized content for the general public, particularly users and small communities interested in digital technologies [63].
Our analysis identifies several features unique to the Korean context, which significantly influence the discourse on digital transformation on Korean social media platforms. For instance, Korea’s collectivist cultural norms shape the adoption of technologies that emphasize communal benefits and organizational harmony [64]. Additionally, the country’s leading position in digital transformation fosters a progressive environment for discussing advanced digital infrastructure [65]. Economically, the interplay between large conglomerates and dynamic SMEs creates diverse viewpoints on how digital transformation can drive business growth and innovation [66]. These unique cultural, technological, and economic contexts provide a distinctive backdrop to Korea’s digital transformation discourse, offering insights into the challenges and opportunities specific to this setting.
We found that global opinion polls often focus on general technological adaptation and digital readiness, whereas our analysis dives deeper into the specific themes and concerns prevalent in Korea. For example, global surveys like those conducted by the IFRC [67] highlight varied regional responses to digital transformation, with Korea emphasizing advanced analytics and system interoperability compared to other regions. Our findings, which underscore the high centrality of innovation and artificial intelligence in Korean discourse, align with these global trends but also reveal unique local priorities and cultural influences.
In conclusion, the findings illuminate the multifaceted impacts of digital transformation, offering diverse perspectives on technological changes, social, and economic transitions as manifested through online news and blogs. The real-time feedback and variety of user content on social media are valuable for policymakers, entrepreneurs, educators, and the general public to understand the advancements in digital technology and how these can be applied to their fields and lives. The insights and user engagement provided by social media data can lead to the development of innovative approaches and strategies that guide the digital transformation era, contributing to socially meaningful conversations about upcoming technological changes.

6. Conclusions

This study leveraged text mining techniques and a semantic network analysis to extract keywords and their associations from social media data, online news, and blog content related to digital transformation. Focusing on Korean language data, it intensively collected data from major Korean portal sites using “digital transformation” and related search terms, ensuring the selection of keywords and consistency of data by exclusively targeting content in Korean.
Despite some limitations, the analysis of Korean data collected from Korean portal sites offers insights into digital transformation, contributing to a comprehensive understanding of various aspects related to the advancement of digital technologies, social changes, and economic impacts. The insights derived from this study provide essential foundational data for in-depth analysis of the continuous development of digital technologies and their impacts on individuals, corporations, and society.
Furthermore, the results can serve as an important reference for strategic planning and policy development related to digital transformation. The data and analysis will offer valuable information to policymakers, entrepreneurs, and academic researchers in integrating digital technologies, seeking social adaptation strategies, and exploring economic sustainability.
To enhance the practical relevance of these findings, we plan to incorporate feedback from industry experts through structured interviews and align our results with documented case studies. It will bridge the gap between theoretical research and practical applications, ensuring that our insights are grounded in real-world experiences and contribute to the development of actionable and effective strategies in digital transformation.

Author Contributions

Conceptualization, J.-H.S. and B.-S.S.; methodology, J.-H.S.; software, J.-H.S.; validation, J.-H.S. and B.-S.S.; formal analysis, J.-H.S.; investigation, J.-H.S.; resources, J.-H.S. and B.-S.S.; data curation, J.-H.S. and B.-S.S.; writing—original draft preparation, J.-H.S. and B.-S.S.; writing—review and editing, J.-H.S. and B.-S.S.; visualization, J.-H.S. and B.-S.S.; supervision, B.-S.S.; project administration, B.-S.S.; funding acquisition, J.-H.S. and B.-S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available from the corresponding author on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Data collection and analysis process for digital transformation.
Figure 1. Data collection and analysis process for digital transformation.
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Figure 2. CONCOR analysis of news network of the digital transformation.
Figure 2. CONCOR analysis of news network of the digital transformation.
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Figure 3. CONCOR analysis of blog network of digital transformation.
Figure 3. CONCOR analysis of blog network of digital transformation.
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Table 1. Frequencies of 50 keywords related to digital transform in online news.
Table 1. Frequencies of 50 keywords related to digital transform in online news.
RankKeywordFreq.RankKeywordFreq.
1Education297526Nation1137
2Innovation264327Strategy1068
3Corporation243728Cooperation1057
4Information212029New942
5Artificial Intelligence209330Smart937
6Project195531Human Resources852
7Data194232Operation831
8Future183033Citizens820
9Support182234Study791
10Government181635Student786
11Global177036Leading764
12Field171037Professor756
13Metaverse164438Competence745
14Policy154839Investment741
15Construction150240Training695
16Region147841Contents684
17Platform146042Institution668
18Promotion145843Tech657
19Era144444Bio646
20Center139445School639
21Service136346Finance544
22Economy127447Cloud543
23Development126748Infrastructure462
24Society125349Software439
25Plan115850Personal Information429
Table 2. Frequencies of 50 keywords related to digital transform in blogs.
Table 2. Frequencies of 50 keywords related to digital transform in blogs.
RankKeywordFreq.RankKeywordFreq.
1Artificial Intelligence297526New1137
2Corporation264327System1068
3Education243728Society1057
4Data212029Government942
5Innovation209330Market937
6Era195531Nation852
7Metaverse194232Cloud831
8Project183033Region820
9Service182234Study791
10Field181635Professor786
11Support177036Citizens764
12Future171037Corona756
13Global164438Online745
14Change154839Space741
15Information150240Human Resources695
16Development147841Personal Information684
17Platform146042Business668
18Construction145843Finance657
19Center144444Big Data646
20Strategy139445Leading639
21Economy136346Research544
22Smart127447Infrastructure543
23Promotion126748Industrial Revolution462
24Plan125349Software439
25Policy115850Science and Technology429
Table 3. Centralities of keywords related to digital transformation from news network.
Table 3. Centralities of keywords related to digital transformation from news network.
RankKeywordCd 1KeywordCb 2KeywordCc 3KeywordCe 4
1Innovation0.938776Data0.06154Innovation0.940408Innovation0.200111
2Education0.897959Education0.050155Education0.904239Artificial
Intelligence		0.195975
3Artificial
Intelligence		0.877551Innovation0.049585Artificial
Intelligence		0.887178Support0.194377
4Support0.877551Support0.035948Support0.887178Education0.192973
5Data0.857143Artificial
Intelligence		0.032176Data0.870748Corporation0.191475
6Corporation0.836735Corporation0.027202Corporation0.854917Data0.18877
7Global0.795918Global0.026213Global0.824919Global0.185881
8Project0.77551Government0.019029Project0.810697Future0.185531
9Future0.77551Future0.016818Future0.810697Project0.184892
10Government0.77551Project0.015426Government0.810697Field0.184361
11Information0.755102Information0.015374Information0.796956Government0.183366
12Field0.755102Metaverse0.015002Field0.796956Information0.18166
13Metaverse0.714286Policy0.013719Metaverse0.770826Promotion0.179538
14Policy0.714286Field0.01159Policy0.770826Policy0.176005
15Promotion0.714286Citizens0.011204Promotion0.770826Metaverse0.175198
16Construction0.693878Construction0.010303Construction0.758394Platform0.174759
17Platform0.693878Development0.009443Platform0.758394Center0.17228
18Service0.673469Platform0.008512Service0.746356Service0.172084
19Center0.653061Promotion0.007452Center0.734694Construction0.171971
20Development0.653061Service0.006708Development0.734694Nation0.170091
21Nation0.653061Society0.004792Nation0.734694Region0.166259
22Region0.632653Nation0.004159Region0.723391Society0.165359
23Society0.632653Region0.003406Society0.723391Economy0.164331
24Economy0.612245Center0.00286Economy0.71243Development0.163565
25Era0.591837Era0.001982Era0.701797Era0.159499
26Strategy0.571429Study0.001756Strategy0.691477Strategy0.156086
27Plan0.510204Economy0.001665Plan0.662259Plan0.141053
28Cooperation0.469388Human
Resources		0.001255Cooperation0.644115Cooperation0.13411
29New0.469388Strategy0.001233New0.644115New0.132824
30Citizens0.469388Leading0.001116Citizens0.644115Citizens0.127351
31Human
Resources		0.44898School0.001047Human
Resources		0.626939Human
Resources		0.122508
32Investment0.408163Student0.000984Investment0.61869Investment0.113464
33Smart0.367347Tech0.000834Smart0.602826Smart0.105321
34Operation0.326531Plan0.000646Operation0.587755Operation0.095075
35Bio0.326531Bio0.000354Bio0.587755Bio0.090109
36Leading0.265306Investment0.000338Leading0.566511Competence0.073346
37Study0.244898Training4.05 × 10−5Study0.559767Leading0.071322
38Professor0.244898New3.87 × 10−5Professor0.559767Contents0.07119
39Competence0.244898Cooperation3.15 × 10−5Competence0.559767Professor0.070857
40Contents0.244898Smart0Contents0.553181Training0.064338
41Student0.22449Operation0Student0.546749Study0.058304
42Training0.22449Professor0Training0.546749Student0.052909
43School0.204082Competence0School0.540464Institution0.049241
44Tech0.183673Contents0Institution0.534323Cloud0.048228
45Institution0.163265Institution0Tech0.534323School0.044269
46Cloud0.163265Finance0Cloud0.534323Infrastructure0.042801
47Infrastructure0.142857Cloud0Infrastructure0.528319Tech0.040939
48Finance0.081633Infrastructure0Finance0.511091Finance0.02403
49Personal
Information		0.040816Software0Personal
Information		0.470204Personal
Information		0.010226
50Software0Personal
Information		0Software0Software1.45 × 10−13
1 Cd: degree centrality. 2 Cb: betweenness centrality. 3 Cc: closeness centrality. 4 Ce: eigenvector centrality.
Table 4. Centralities of keywords related to digital transformation from blog network.
Table 4. Centralities of keywords related to digital transformation from blog network.
RankKeywordCd 1KeywordCb 2KeywordCc 3KeywordCe 4
1Artificial
Intelligence		0.918367Artificial
Intelligence		0.092258Artificial
Intelligence		0.918802Artificial
Intelligence		0.193392
2Data0.877551Data0.041167Data0.881299Data0.192609
3Corporation0.857143Education0.031264Corporation0.863673Corporation0.191275
4Innovation0.816327Corporation0.028714Innovation0.830455Service0.18964
5Service0.816327Information0.018267Service0.830455Development0.189048
6Development0.816327Innovation0.017399Development0.830455Innovation0.18838
7Education0.795918Support0.017302Education0.814786Metaverse0.187632
8Metaverse0.795918Development0.014583Metaverse0.814786Construction0.186105
9Support0.795918Service0.013366Support0.814786Promotion0.186105
10Construction0.795918Construction0.012719Construction0.814786Support0.185267
11Promotion0.795918Promotion0.012719Promotion0.814786Project0.184539
12Project0.77551Metaverse0.010805Project0.799698Smart0.184539
13Smart0.77551Center0.009584Smart0.799698Education0.18388
14Field0.755102Project0.009436Field0.785158Field0.183307
15Center0.755102Smart0.009436Center0.785158Center0.18085
16Information0.734694Citizens0.008668Information0.771137Information0.175311
17Platform0.693878Strategy0.00794Platform0.744546Platform0.174807
18Strategy0.693878Field0.006863Strategy0.744546Strategy0.171625
19System0.693878Future0.005236System0.744546System0.171384
20Global0.653061System0.005218Global0.719728Global0.163418
21Future0.632653Global0.004499Future0.707929Plan0.159692
22Plan0.632653Plan0.003899Plan0.707929Future0.157281
23Era0.571429Platform0.003363Era0.674745Government0.151141
24Government0.571429Era0.002008Government0.674745Nation0.148339
25Citizens0.571429Government0.00083Citizens0.674745Era0.147639
26Nation0.55102Change0.000604Nation0.664364Citizens0.145619
27Policy0.530612Society0.000582Policy0.654298Policy0.143094
28Economy0.510204Economy0.000514Economy0.644532Economy0.137371
29Society0.489796Nation0.000448Society0.635054Cloud0.134165
30Cloud0.489796Policy0.000377Cloud0.635054Society0.130427
31Market0.469388Cloud0.000157Market0.62585Market0.129527
32Infrastructure0.44898Market7.91 × 10−5Infrastructure0.61691Infrastructure0.124915
33Online0.428571New0Online0.608221Online0.120143
34Leading0.408163Region0Leading0.599773Leading0.114433
35Change0.387755Study0Change0.591557Region0.104145
36Region0.367347Professor0Region0.583563Change0.101043
37Study0.326531Corona0Study0.568206Study0.094029
38Big Data0.285714Online0Big Data0.553637Big Data0.082636
39New0.244898Space0New0.539796Finance0.070776
40Corona0.244898Human
Resources		0Corona0.539796Corona0.070504
41Finance0.244898Personal
Information		0Finance0.539796New0.065627
42Space0.183673Business0Space0.520285Space0.051164
43Business0.102041Finance0Business0.496364Business0.029595
44Human
Resources		0.081633Big Data0Human
Resources		0.48521Human
Resources		0.023802
45Personal
Information		0.061224Leading0Professor0.474546Personal
Information		0.016215
46Professor0.040816Research0Personal
Information		0.469388Professor0.011912
47Research0.020408Infrastructure0Research0.469388Research0.006106
48Industrial
Revolution		0Industrial
Revolution		0Industrial
Revolution		0Industrial
Revolution		3.72 × 10−15
49Software0Software0Software0Software3.72 × 10−15
50Science and Technology0Science and Technology0Science and Technology0Science and Technology3.72 × 10−15
1 Cd: degree centrality. 2 Cb: betweenness centrality. 3 Cc: closeness centrality. 4 Ce: eigenvector centrality.
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Song, J.-H.; Seo, B.-S. Analyzing Trends in Digital Transformation Korean Social Media Data: A Semantic Network Analysis. Big Data Cogn. Comput. 2024, 8, 61. https://doi.org/10.3390/bdcc8060061

AMA Style

Song J-H, Seo B-S. Analyzing Trends in Digital Transformation Korean Social Media Data: A Semantic Network Analysis. Big Data and Cognitive Computing. 2024; 8(6):61. https://doi.org/10.3390/bdcc8060061

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Song, Jong-Hwi, and Byung-Suk Seo. 2024. "Analyzing Trends in Digital Transformation Korean Social Media Data: A Semantic Network Analysis" Big Data and Cognitive Computing 8, no. 6: 61. https://doi.org/10.3390/bdcc8060061

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