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Aspect-based sentiment analysis (ABSA) aims to predict sentiment orientations towards textual aspects by extracting insights from user comments. While pretrained large language models (LLMs) demonstrate proficiency in sentiment analysis, incorporating syntactic and semantic features into ABSA remains a challenge. Additionally, employing LLMs for sentiment analysis often requires significant computational resources, rendering them impractical for use by individuals or small-scale entities. To address this, we propose the semiotic signal integration network (SSIN), which effectively combines syntactic and semantic features. The core syncretic information network leverages isomorphism and syntax to enhance knowledge acquisition. The semantically guided syntactic attention module further enables integrated semiotic representations via sophisticated attention mechanisms. Experiments on the publicly available SemEval dataset show that SSIN performs better than existing state-of-the-art ABSA baselines and LLMs such as Llama and Alpaca with high accuracy and macro-F1 scores. Moreover, our model demonstrates exceptional interpretability and the ability to discern both positive and negative sentiments, which is vitally important for real-world applications such as social media monitoring, health care, and customer service. Code is available at https://github.com/AmbitYuki/SSIN. [ABSTRACT FROM AUTHOR]

Public support is crucial for mitigating and adapting to the effects of climate change. Civil Society Organizations (CSOs) play a significant role in normative governance aimed at influencing attitudes and mobilizing citizens to take action. Civil society organizations (CSOs) use digital media to this purpose. The framing of information presented by CSOs has been shown to shape how people perceive and respond to it. This paper aims to examine how cognitive or topical framing and moral foundational framing interact in predicting public engagement with CSOs’ communication about climate change on Facebook. To this end, several computational methods – including topic modeling, semantic network analysis, and automatic text processing based on a Moral Foundations dictionary – are combined, using data gathered on Facebook. The results of the empirical analysis reveal that CSOs use different topical frames and moral foundations in order to influence policymakers and mobilize citizens. CSOs elicit most engagement on Facebook when they combine a “Climate mobilization” topical frame with the moral foundations of care and fairness pertaining to the ethics of autonomy. [ABSTRACT FROM AUTHOR]

This article examines the use of historical analogies by political leaders during foreign policy crises. Specifically, we focus on the German, Polish and Czech leaderships' reactions to Russia's invasion of Ukraine and investigate the historical analogies which leaders invoked to justify their responses to Russia's aggression. The existing scholarship recognizes the importance of historical analogies in foreign policy decision-making, but the literature focuses predominantly on several high-profile analogies (i.e. the Munich Agreement or the Vietnam War) and lacks comparative perspective. We employ a dual-method approach that combines qualitative coding with semantic network analysis, and conduct a comparative analysis of the full spectrum of analogies used by Polish, Czech and German policy-makers. We find that politicians did not focus on a single overarching analogy, but employed a wide array of historical references. We also find that even though the countries' policies largely aligned and that their leaders often invoked the same events, the meaning of analogies and the signals they sent differed substantially across cases. Analysing the full spectrum of analogies demonstrates that justifications for supporting Ukraine were shaped more by these states' own historical traumas than by their sympathy for Ukraine, support for international law, or desire to uphold the rules-based international order. [ABSTRACT FROM AUTHOR]

Our study refers to what the communication of city networks looks like regarding mentions and frames and if the digital communication activities of city networks could primarily serve as collaborative diplomacy of governance or are motivated by self-interests such as city branding. Our theoretical frame integrates Castells' (2010, 2009) networked society and communication power and combines various concepts from the public (digital) diplomacy, public relations, and strategic communication. In a semantic network analysis of Tweets, we analysed the communication strategies of three city networks according to their digital content (n = 47,226) and more specifically each network's top 50 hashtags and mentions therein. We find that city networks use three different Twitter communication strategies. We propose a model describing topic-, city/mayor-, and hybrid-oriented strategies. The analysis of the three city groups undertaken here has extended our knowledge of global urban governance communication strategies in burgeoning city diplomacy. [ABSTRACT FROM AUTHOR]

China has attached increasing importance to the overseas dissemination of traditional culture as it continues to strengthen its comprehensive national strength and develop globalization initiatives. The electronic game, as the 'ninth art', with its booming development and inherent cross-cultural communication platform attributes, has taken on the important task of taking culture abroad. This study selects 'Genshin Impact', a game highly praised and well-received globally, which contains an abundance of Chinese cultural elements and cultural elements from various regions of the world, to study cultural interaction in cross-cultural communication of electronic games. This study conducts text sentiment analysis and semantic network analysis on the comments of 'Genshin Impact' videos on the overseas video platform YouTube and studies the cultural interaction in the cross-cultural communication of the 'ninth art', telling the cultural stories of China and the world from a multicultural perspective. The study found that games and derivative videos triggered positive emotional responses from audiences both at home and abroad. While experiencing pleasure and surprise, audiences also arouse deeper emotions such as admiration, identification, and expectation. Meanwhile, in the complex art form of the 'ninth art', audiences can absorb the cultural elements of different cultures, resonate with the elements of their cultural circle, and even trigger further profound thinking. Finally, this study provides constructive suggestions on using the 'ninth art' to better 'tell China's stories and disseminate China's voice'. [ABSTRACT FROM AUTHOR]

China's construction, energy consumption, and emissions are the world's largest, making green building (GB) crucial for sustainable development and climate action. GB policies play a crucial role in promoting sustainable development and mitigating environmental impacts. This paper presents a comprehensive analysis of China's GB policies at both central and local levels using natural language processing (NLP), Latent Dirichlet Allocation (LDA), and semantic network analysis (SNA). It also integrates three theoretical frameworks: Policy Implementation Theory, Sustainable Development Theory, and Innovation Diffusion Theory. The study aims to: (1) compare the characteristics of central and local GB policies; (2) identify problems and propose solutions to improve policy effects and impacts. The results show that both levels of policies cover various sustainability aspects but with different focuses. The SNA reveals the semantic relationships and differences within the policy documents. The study suggests several strategies to enhance GB policy effectiveness and sustainability, based on the three theoretical frameworks. These strategies include providing more specific guidance, enhancing financial support and incentives, promoting technological advancements, strengthening monitoring and evaluation, adopting a more systematic approach, democratizing policy formulation, identifying key stakeholders and influencers, and considering innovation characteristics. This study contributes to the GB policy research and practice in China. It is also the first study to use NLP, LDA, and SNA to comprehensively analyze China's GB policies, and the first study to apply the three theoretical frameworks to GB policy analysis. [ABSTRACT FROM AUTHOR]

Copyright of Retos, Revista de Ciencias Administrativas y Económicas is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and dependent on expert experience. However, three main challenges hinder the versatility of semantic segmentation models. First, the targets in these images are dense and exist at varying spatial scales, which imposes higher demands on the model for accurate segmentation across scales. Second, the segmentation of small targets in the images is often overlooked, leading to a compromise between fine segmentation and model efficiency. Lastly, the data-intensive nature of remote sensing images and the resource-intensive operations of large-scale networks impose significant communication and computation burdens on edge devices, which may not have sufficient resources to handle them effectively. To address these challenges, this paper proposes a lightweight semantic segmentation method for remote sensing images to achieve high-precision segmentation for multi-scale targets while maintaining low computational complexity. The main components include: (1) embedding the inverted residual block structure to minimize the number of model parameters and computational costs; (2) introducing the parallel irregular space pyramid pooling module to efficiently aggregate multi-scale contextual information for fine-grained recognition of small targets; and (3) embedding transfer learning into the encoder-decoder structure to speed up the convergence rate and improve multi-scale feature fusion capability, thereby reducing semantic information loss. The proposed lightweight method has been extensively tested on real-world high-resolution remote sensing datasets. It achieved PA, MPA, MIoU, and FWIoU scores of 87.90%, 75.76%, 66.29%, and 78.81% on the Vaihingen dataset; 87.03%, 85.31%, 74.85%, and 77.54% on the Potsdam dataset; and 95.37%, 83.33%, 75.70%, and 91.31% on the Aeroscapes dataset. Compared to other popular semantic segmentation models, the proposed method achieved the highest values in all four evaluation indicators, demonstrating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on "sport(s)" in combination with "metaverse", "augmented reality", "virtual reality", "lifelogging", and "mixed reality". First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as "user(s)", "game(s)", "technolog(y/ies)", "experience(d)", "physical", "training", and "video", with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that "performance/scoring" was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of "performance/scoring" in sports may seem obvious; however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry. [ABSTRACT FROM AUTHOR]

Urban street space is a significant component of urban public spaces and an important aspect of people's perceptions of a city. Jinan Old City exemplifies the balance between the supply of and demand for green spaces in urban streets. The sense of comfort and the demand level of street spaces are measured via the space demand index. Open platform data, such as those from Baidu Maps and Amap, are evaluated using methods including ArcGIS network analysis and Segnet semantic segmentation. The results obtained from such evaluations indicate that, in terms of the green space supply, the overall level for Shangxin Street in Jinan is not high. Only 24% of the selected sites have an adequate green space supply. The level on Wenhua West Road is higher than that on Shangxin Street. The block on the western side of Shangxin Street has the highest green space demand, with a decreasing trend from west to east. There are several higher selection points in the middle section of Shangxin Street. The demand is lowest in the middle of Wenhua East Road. Shangxin Street's demand is higher than that of Wenhua West Road. The supply and demand are highly matched on Wenhua West Road and poorly matched on Shangxin Street, with 44.12% of the area in the "low supply, high demand" quadrant. This study proposes targeted optimization strategies based on supply and demand, thereby providing research ideas and methods for urban renewal. [ABSTRACT FROM AUTHOR]

The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users' behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules. [ABSTRACT FROM AUTHOR]

AbstractArtificial intelligence (AI) technologies, as a product of processes aimed at imitating human intelligence through computers and software, affect our daily lives in various aspects, including cultural, technological, and economic. The advent of ChatGPT, developed by OpenAI, signifies a pivotal AI advancement in human language comprehension and generation, heralding profound implications across societal, economic, and cultural dimensions. However, a notable gap exists in scholarly literature concerning the examination of perceptions and discussions surrounding ChatGPT. This study aims to address this gap by analyzing Twitter conversations, comprising 1.1 million tweets containing “ChatGPT” or “#ChatGPT” between December 1 2022 and June 1 2023, with geolocation data. Employing a text as data approach encompassing text, sentiment, and semantic network analyses, sentiment polarity and lexical patterns were explained, while semantic network analysis revealed central expressions and prominent themes in Twitter discussions. The study highlights the social effects of AI technologies from the perspective of Twitter users and reveals the sentimental tendency and themes in tweets with geographical and economic dimensions. The findings reflect the prevalence of positive sentiment and hype toward ChatGPT while there are also concerns regarding privacy, cybersecurity, and misuse of AI tools. More importantly, content generation, technical aspects, business applications, educational use, and competitors are among the main themes of ChatGPT-related discussions on Twitter. [ABSTRACT FROM AUTHOR]

Rural decline has become a global challenge threatening sustainable development. Many countries have explored rural revitalization strategies, but systematic research on policy mechanisms and optimization paths is lacking. This study collected 74 rural revitalization policy texts in China from 2018 to 2024 through stratified sampling. Combining qualitative coding and quantitative modeling, including grounded theory, LDA topic model, sentiment analysis and semantic network analysis, this study constructed an integrated analytical framework to examine policy themes, emotional expression and logical relationships. The results identified the “Five Revitalizations” framework focusing on industrial, talent, cultural, ecological and organizational revitalization, as well as the “Four Supports” of institutional innovation, input guarantee, technological support and legal protection. The policy texts exhibit an increasingly optimistic tone, with traditional themes of deepening rural reform and increasing agricultural investment coexisting with emerging themes of urban-rural integration and digital countryside construction. Semantic network analysis further revealed the intrinsic linkages among policy themes. Comparative analysis with international rural revitalization cases yielded the following optimization paths: improving urban-rural integration mechanisms, strengthening diversified input guarantee, enhancing technological and talent support, and innovating rural governance models. This study enriches the theoretical understanding and practical guidance for sustainable rural revitalization from a global perspective. [ABSTRACT FROM AUTHOR]

Addressing the 'wicked problem' of nutrient pollution requires coordinated policies spanning across diverse sectors and environmental systems. Using a case study of Canadian legislation, we apply semantic network analysis to identify thematic links across an inventory of 245 nutrient-related policies. Our analysis identifies twelve topics with unique types of connections across multiple facets of Canadian society. 'Hub' policies include broad environmental protection, land use planning, and climate-related legislation with close ties to multiple other topics. 'Bridge' policies create connections among otherwise disconnected topics in the network, representing opportunities to inform new policy proposals. Some legislation, such as food processing regulations, indirectly relates to nutrient use but could inform policy integration towards more coordinated and holistic nutrient management across the food system. A computational text network approach can be useful for addressing challenges in complex policy analysis, including by identifying unique entry points to guide more cross-cutting nutrient policy development. [ABSTRACT FROM AUTHOR]

Introduction In April 2021, the U.S. Food and Drug Administration (FDA) announced its intention to ban the sale of menthol cigarettes and cigars. Decades of research support the premise that a menthol ban will reduce initiation and disparities in tobacco-related disease among menthol smokers. The tobacco industry opposed such a policy and worked for decades to shape public opposition. Social media discourse can inform our understanding of public opinion about the proposed ban and guide communication strategies and policy implementation. Aims and Methods This research employed a mixed-methods design to explore TikTok posts discussing the announced menthol ban. Using a TikTok web scraper to extract all content in the #mentholban hashtag (n  = 171), we coded for 11 themes, characterized content with descriptive statistics, and created a semantic network of co-occurring hashtags. Results We found primarily negative attitudes towards the U.S. ban announcement and a large volume of menthol "hacks" to circumvent the bans. Our semantic network analysis revealed strong co-occurrences between #mentholban and popularity-seeking hashtags. The metadata associated with each TikTok demonstrated that most posters in #mentholban are not "influencers" in the sense of having many followers, aside from a few niche organizations with multiple posts. We found that perceived political and racial motivations shaped posters' assessments of the menthol ban. Furthermore, we uncovered how individuals and organizational actors shaped menthol ban content on TikTok. Conclusions Our study indicates targeted marketing from alternative menthol product companies and advocacy organizations. The latter of these organizations is more likely to saturate the TikTok landscape with multiple posts and strategic hashtags. Implications This study pursued an exploration of tobacco policy discussion on TikTok, specifically related to the FDA-proposed menthol ban. TikTok is a newer platform and our study provides early evidence of policy discussion emerging there, including the types of accounts creating the content and their valence toward the policy. [ABSTRACT FROM AUTHOR]

The concept of sustainable tourism development is derived from sustainable development. Building tourism with the framework of sustainable development has become a necessary effective way. This study examined the abstracts and titles of 1783 sustainable development and 692 sustainable tourism development related papers from 2012 to 2021 as textual data sources through the Web of Science core database. Using text mining and semantic network as analysis methods, this paper summarizes eight main clusters of sustainable development and sustainable tourism development in the past 10 years. Based on this, this paper compares the differences between the two researches and explores four potential research areas in sustainable tourism development: (1) threats to sustainability; (2) ecology and health; (3) stakeholder cooperation and roles; and (4) tourism development planning. In addition, the visual network knowledge structure intuitively captures the relationship between the different keywords in the clustering, which provides an important reference value for broadening the future research direction of sustainable tourism development. [ABSTRACT FROM AUTHOR]

Since the onset of COVID-19, concerns have risen regarding the environmental impact of increased waste generation. Of particular concern is the surge in plastic usage, prompting a call for enhanced recycling practices. This study investigates the impact of the COVID-19 pandemic on recycling discourse in South Korea by analyzing news media coverage on recycling and reuse based on framing theory. Employing Python and R, a total of 24,928 news articles containing keywords (i.e., recycling and reuse) both pre- and during the COVID-19 pandemic were collected and analyzed. The results of computational analyses, such as topic modeling, semantic network analysis, and framing analysis, show that while an initial spike in COVID-19 cases correlated with heightened recycling-related coverage, this trend gradually waned as the pandemic persisted. The results also revealed shifts in keywords, frames, and framing functions over time. Notably, major frames expanded during the pandemic to incorporate COVID-19-related topics and concrete recycling efforts. Throughout, the primary framing function remained prognostic. This research provides valuable insights into the evolving environmental discourse amidst a global pandemic. Plain language summary: Can the COVID-19 pandemic lead to better recycling trends? An analysis of how South Korean news media discussed recycling before and during the crisis Since the onset of COVID-19, concerns have arisen regarding the environmental impact of increased waste generation. Of particular concern is the surge in plastic usage, prompting a call for enhanced recycling practices. This research looked at how the COVID-19 pandemic changed the news media coverage of recycling and reuse in South Korea. Using computer programs, such as Python and R, this research analyzed nearly 25,000 news stories about recycling before and during the pandemic. The findings show that when COVID-19 cases first shot up, the news talked a lot more about recycling. But as time went on, even as the pandemic continued, they talked about it less. The words and themes in the news also changed over time. During the pandemic, the news started to include more about COVID-19 in stories about recycling and featured more concrete recycling efforts. Even with these changes, the primary focus stayed the same, which was solutions to the waste and recycling issue. This research provides valuable insights into how environmental conversations have changed during this global crisis. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving technological landscape, there is a growing need for educators equipped with the skills to teach computer science (CS) creatively. This paper aims to address this need by designing and implementing effective CS teacher training programs, drawing insights from the opinions of education and IT professionals. Through qualitative analysis and text mining, the paper provides detailed strategies for developing and executing these training initiatives. Participants were recruited for focus group interviews (FGI) and teacher training. FGI included 11 experts in CS and education, while teacher training involved 25 general teachers and 71 school administrators. Procedures encompassed FGI planning, questionnaire distribution, online interviews, response review, and analysis, followed by teacher training planning, implementation, satisfaction survey, and analysis. The analysis involved refining FGI data using Textom, keyword frequency analysis, semantic network analysis, and satisfaction survey analysis. Based on FGI analysis, the creative CS teacher training focused on integrating AI and EdTech, enhancing participants' digital competencies. Using text mining techniques, meaningful insights were extracted from unstructured data, guiding program design. Implementation included interactive sessions and online platforms, aligning with expert insights. Satisfaction surveys affirmed program effectiveness, highlighting the need for future enhancements like more practical exercises and networking opportunities. This paper delves into the design and implementation of teacher training programs, informed by expert FGI analysis, to meet the demands of the current era. While it sets a path for future-oriented training, the analysis primarily gauged educator satisfaction, prompting a need for further research. Future studies should assess how educator capabilities evolve post-training. Additionally, there's a call for research to pinpoint which competencies are bolstered by training and whether these enhancements directly correlate with program effectiveness. Such investigations would enrich our understanding of effective training strategies for educators in the digital age. [ABSTRACT FROM AUTHOR]

The burgeoning field of sustainable tourism in historically significant urban districts has derived increasing attention in tandem with broader urban economic development. The perceived image of a tourist destination significantly affects tourist consumption behaviors and destination choices, while negative evaluations provide destination managers with insights for targeted improvements to tourist amenities, thereby contributing to the sustainable development of these historically significant districts. This study leveraged Python 3.12.3 software to collect online textual data from popular tourism websites, specifically Ctrip 8.71.4 and Mafengwo 11.1.7. Then, ROST CM6.0 software facilitated high-frequency word analysis, network semantic analysis, and sentiment analysis to outline the perceived image and characteristics of the Macau Historic District, a UNESCO World Heritage site, from the perspective of tourists. The findings are as follows: (1) The image of the Historic Centre of Macao as a tourist destination comprises four primary dimensions: cultural (16.02%), landscape (30.85%), emotional (16.97%), and local (36.16%). (2) Tourists perceive the Historic Centre of Macao as a blend of dynamism and tranquility, tradition, and modernity. (3) Emotional evaluations of the Historic Centre of Macao as a tourist destination are primarily positive (85.45%), whereas negative sentiments constitute 5.76%, with recurring themes pertaining to overcrowding, high prices, a lack of interactive experiences at attractions, excessive commercialization, and insufficient public rest facilities. These findings offer valuable directives for the future planning and development of tourism in Macau, a city recognized for its World Heritage status. (4) In summary, the thematic image of the Historic Centre of Macao can be considered authentic, romantic, and commercialized. [ABSTRACT FROM AUTHOR]

Social media platforms can play a pivotal role in shaping public opinion during times of crisis and controversy. The COVID-19 pandemic resulted in a large amount of dubious information being shared online. In Belgium, a crisis emerged during the pandemic when a soldier (Jürgen Conings) went missing with stolen weaponry after threatening politicians and virologists. This case created further division and polarization in online discussions. In this paper, we develop a methodology to study the potential of coordinated spread of incorrect information online. We combine network science and content analysis to infer and study the social network of users discussing the case, the news websites shared by those users, and their narratives. Additionally, we examined indications of bots or coordinated behavior among the users. Our findings reveal the presence of distinct communities within the discourse. Major news outlets, conspiracy theory websites, and anti-vax platforms were identified as the primary sources of (dis)information sharing. We also detected potential coordinated behavior and bot activity, indicating possible attempts to manipulate the discourse. We used the rapid semantic similarity network for the analysis of text, but our approach can be extended to the analysis of images, videos, and other types of content. These results provide insights into the role of social media in shaping public opinion during times of crisis and underscore the need for improved strategies to detect and mitigate disinformation campaigns and online discourse manipulation. Our research can aid intelligence community members in identifying and disrupting networks that spread extremist ideologies and false information, thereby promoting a more informed and resilient society. [ABSTRACT FROM AUTHOR]

Standard learning assessments like multiple-choice questions measure what students know but not how their knowledge is organized. Recent advances in cognitive network science provide quantitative tools for modeling the structure of semantic memory, revealing key learning mechanisms. In two studies, we examined the semantic memory networks of undergraduate students enrolled in an introductory psychology course. In Study 1, we administered a cumulative multiple-choice test of psychology knowledge, the Intro Psych Test, at the end of the course. To estimate semantic memory networks, we administered two verbal fluency tasks: domain-specific fluency (naming psychology concepts) and domain-general fluency (naming animals). Based on their performance on the Intro Psych Test, we categorized students into a high-knowledge or low-knowledge group, and compared their semantic memory networks. Study 1 (N = 213) found that the high-knowledge group had semantic memory networks that were more clustered, with shorter distances between concepts—across both the domain-specific (psychology) and domain-general (animal) categories—compared to the low-knowledge group. In Study 2 (N = 145), we replicated and extended these findings in a longitudinal study, collecting data near the start and end of the semester. In addition to replicating Study 1, we found the semantic memory networks of high-knowledge students became more interconnected over time, across both domain-general and domain-specific categories. These findings suggest that successful learners show a distinct semantic memory organization—characterized by high connectivity and short path distances between concepts—highlighting the utility of cognitive network science for studying variation in student learning. [ABSTRACT FROM AUTHOR]

In what was labeled the "nut rage" incident, the vice president of Korean Air, Hyun-Ah Cho (Heather Cho), demonstrated behavior that exemplifies corporate transgression and deviation from societal moral standards toward a flight attendant aboard a flight. Such behavior instigated the public to express negative sentiment on various social media platforms. This study investigates word-of-mouth network on YouTube in response to the crisis, patterns of co-commenting activities across selected YouTube videos, as well as public responses to the incident by employing social and semantic network analysis. A total of 512 YouTube videos featuring the crisis from December 8, 2014 through November 11, 2018, and 52,772 public comments to the videos were collected. The central videos in the network successfully attracted the public's attention and engagements. The results suggest that the video network was decentralized, with multiple videos acting as hubs in the network. The public commented on various videos instead offocusing on a few. The contents of influential videos uploaded by popular news organizations revealed not only Cho's behaviors related to the nut rage crisis but also unrelated illegal behaviors and the moral violations committed by the family members of Korean Air. The public attached derogatory remarks to Cho and her family, and the comments also addressed ethical concerns, management issues of the company, and boycott intentions. The results imply that adverse public reaction was related to the long-standing problem caused by family ownership and governance in large Korean corporations. This Korean Air scandal illustrates backlash toward a leadership breakdown by the family business conglomerate prevalent in the Korean society. This study provides insights for effective handling of similar crises. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Starting on September 1, 2021, the state of Texas implemented a new abortion restriction referred to as Texas Senate Bill 8 (SB 8), which prohibits abortion after approximately 6 weeks of pregnancy. Despite legal challenges, the Supreme Court declined to intervene, leaving open the possibility for future public hearings or challenges. Methods: We collected tweets containing the hashtag #TexasAbortionBan from public Twitter accounts over a 30-day period (September 1 to September 30, 2021). A total of 104,097 tweets were analyzed during this time frame. The current project employed social network analysis to study the discourse among Twitter users in relation to the #TexasAbortionBan. Results: The current project examined the top ten dominant users within the #TexasAbortionBan network. These users were classified into four groups: journalists/news organizations, activist (political/human rights activist), Internet personality/influencer, and nonprofit and non-governmental organization. The primary topics of discussion revolved around women's access to abortion care and their rights to make decisions regarding their bodies. Conclusions: The findings suggest that the Texas Abortion Ban is the harshest "heartbeat" law among all the states that implemented similar legislation in 2021, and it triggered an uproar in the political discussion. Policy Implications: The social network analysis, a computer-mediated research method, can provide valuable insights for researchers and policymakers seeking to understand a political and controversial topic, such as abortion. The Texas Abortion Ban is seen as a pavement for eventually overturning of Roe v. Wade. Understanding the discourse surrounding abortion-related topics on Twitter holds significance for women, healthcare providers, and policymakers interested in engaging in public advocacy to maintain legal abortion rights. [ABSTRACT FROM AUTHOR]

Networks serve as a metaphor for understanding various phenomena in humanities, encompassing the circulation of ideas, people, and artifacts. This research employs network analysis, drawing from H. C. White’s concept of netdom, to capture social and language patterns within Ljubljana’s Arhitektura magazine from 1931 to 1934. Its objective was to map the socio-semantic network of the magazine, assisting in discerning relationship and expression patterns within Yugoslavian architecture discourse of the 1930s, aligned with the principles of “new architecture”. Methodologically, the research has entailed text analysis using natural language processing techniques and quantitative network analysis, revealing three types of networks: a bimodal network of authors and issues, individual semantic networks of authors, and a socio-semantic network. This has resulted in 18 networks. Despite a simplified text processing model and trivial architectural concepts, common language among contributors is detected, pointing to mutual connections in architectural expressions and actors’ social ties. Arhitektura was not the sole platform for discourse creation; however, the conducted network analysis has confirmed that the nature of the magazine embodies the concept of netdom switching, where identities are formed and altered through transitions between netdom, shaping and changing those identities over time. Furthermore, this research illustrates how network analysis can offer actionable insight into historical phenomena, as well as its applicability in understanding social and cultural dynamics.

Semantic fluency impairment has been attributed to a wide range of neurocognitive and psychiatric conditions, especially in the older population. Moderate heritability estimates on semantic fluency were obtained from both twin and family-based studies suggesting genetic contributions to the observed variation across individuals. Currently, effort in identifying the genetic variants underlying the heritability estimates for this complex trait remains scarce. Using the semantic fluency scale and genome-wide SNP genotype data from the Long Life Family Study (LLFS), we performed a genome-wide association study (GWAS) and epistasis network analysis on semantic fluency in 2289 individuals aged over 60 years from the American LLFS cohorts and replicated the findings in 1129 individuals aged over 50 years from the Danish LLFS cohort. In the GWAS, two SNPs with genome-wide significance (rs3749683, p = 2.52 × 10−8; rs880179, p = 4.83 × 10−8) mapped to the CMYAS gene on chromosome 5 were detected. The epistasis network analysis identified five modules as significant (4.16 × 10−5 < p < 7.35 × 10−3), of which two were replicated (p < 3.10 × 10−3). These two modules revealed significant enrichment of tissue-specific gene expression in brain tissues and high enrichment of GWAS catalog traits, e.g., obesity-related traits, blood pressure, chronotype, sleep duration, and brain structure, that have been reported to associate with verbal performance in epidemiological studies. Our results suggest high tissue specificity of genetic regulation of gene expression in brain tissues with epistatic SNP networks functioning jointly in modifying individual verbal ability and cognitive performance. [ABSTRACT FROM AUTHOR]

Purpose: Large-scale text-based data increasingly poses methodological challenges due to its size, scope and nature, requiring sophisticated methods for managing, visualizing, analyzing and interpreting such data. This paper aims to propose semantic network analysis (SemNA) as one possible solution to these challenges, showcasing its potential for consumer and marketing researchers through three application areas in phygital contexts. Design/methodology/approach: This paper outlines three general application areas for SemNA in phygital contexts and presents specific use cases, data collection methodologies, analyses, findings and discussions for each application area. Findings: The paper uncovers three application areas and use cases where SemNA holds promise for providing valuable insights and driving further adoption of the method: (1) Investigating phygital experiences and consumption phenomena; (2) Exploring phygital consumer and market discourse, trends and practices; and (3) Capturing phygital social constructs. Research limitations/implications: The limitations section highlights the specific challenges of the qualitative, interpretivist approach to SemNA, along with general methodological constraints. Practical implications: Practical implications highlight SemNA as a pragmatic tool for managers to analyze and visualize company-/brand-related data, supporting strategic decision-making in physical, digital and phygital spaces. Originality/value: This paper contributes to the expanding body of computational, tool-based methods by providing an overview of application areas for the qualitative, interpretivist approach to SemNA in consumer and marketing research. It emphasizes the diversity of research contexts and data, where the boundaries between physical and digital spaces have become increasingly intertwined with physical and digital elements closely integrated – a phenomenon known as phygital. [ABSTRACT FROM AUTHOR]

The historical relationship between youth subcultures and mainstream media has long been the subject of debate. Some researchers have proposed that, after mixed panic and hype, the media quickly coopts and commodifies subcultural threats. Conversely, others have argued that negative media coverage keeps subcultures coherent and thriving, while positive coverage is the 'kiss of death'. Despite its continued importance, few studies have closely examined this topic and none has quantitatively examined changes in subcultural media representation over time using archival data. This paper addresses this gap though a computer-assisted content analysis of 735 newspaper articles about punk subculture spanning three decades of publication. The results provide mixed support for both theoretical predictions, but fit neither perfectly. Overall coverage is ambivalent in tone, but becomes more positive and less radical over time. Punk takes far longer to commodify than cooptation perspectives predict. It survives positive coverage, but becomes increasingly coherent. The results call into question previous claims about media representation, highlight the need for more synthetic theories of the media's relationship to subcultures, and illustrate the utility of text-mining methods for historical youth studies. [ABSTRACT FROM AUTHOR]

Networks serve as a metaphor for understanding various phenomena in humanities, encompassing the circulation of ideas, people, and artifacts. This research employs network analysis, drawing from H. C. White's concept of netdom, to capture social and language patterns within Ljubljana's Arhitektura magazine from 1931 to 1934. Its objective was to map the socio-semantic network of the magazine, assisting in discerning relationship and expression patterns within Yugoslavian architecture discourse of the 1930s, aligned with the principles of "new architecture". Methodologically, the research has entailed text analysis using natural language processing techniques and quantitative network analysis, revealing three types of networks: a bimodal network of authors and issues, individual semantic networks of authors, and a socio-semantic network. This has resulted in 18 networks. Despite a simplified text processing model and trivial architectural concepts, common language among contributors is detected, pointing to mutual connections in architectural expressions and actors' social ties. Arhitektura was not the sole platform for discourse creation; however, the conducted network analysis has confirmed that the nature of the magazine embodies the concept of netdom switching, where identities are formed and altered through transitions between netdoms, shaping and changing those identities over time. Furthermore, this research illustrates how network analysis can offer actionable insight into historical phenomena, as well as its applicability in understanding social and cultural dynamics. [ABSTRACT FROM AUTHOR]

The fake news that circulated on social media in 2020 and 2022 affected the voters of the South Korean general and presidential elections, respectively. The political controversy, centered on the election fraud that occurred in both the elections, received significant attention from the society. This study emphasizes the Twitter discourse and compares the formation and distribution of election fraud. We conduct semantic social network analysis and structural topic modeling (STM) to represent topics and relationships among the emerging themes using Twitter texts related to both elections. Results demonstrate that discourses on the same political issue exhibited unique contents and structures, as information is formed and distributed differently depending on the type of election in South Korea. Furthermore, this study illustrates the process and analysis of large-scale text data collected from Twitter. It also includes new methods, such as political ideology, for considering an analytical dimension. [ABSTRACT FROM AUTHOR]

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process. [ABSTRACT FROM AUTHOR]

Under the threat of the novel coronavirus, people are compelled to contemplate some ultimate existential questions, such as life and death. This study collected texts related to the death psychology from Sina Weibo, and after data cleaning, a total of 3868 Weibo texts were included. Study 1 employed grounded theory from qualitative research to explore the core categories and evolutionary mechanisms of people's psychology when facing death threats in the context of the pandemic. Study 2 utilized big data mining techniques such as topic mining and semantic network analysis to validate the effectiveness of the death psychology theory developed in qualitative research. The findings demonstrate that within the "Emotion–Cognition–Behavior-Value" framework, the implications of death threats manifest in four aspects: death anxiety, death cognition, coping efficacy, and sense of meaning. As time progresses, the study of death psychology can be segmented into four distinct phases: the tranquil phase prior to lifting pandemic restrictions, the threat phase at lifting pandemic restrictions onset, the coping phase mid-lifting pandemic restrictions, and the reformative phase post-lifting pandemic restrictions. The calculated outcomes of topic mining and semantic network analysis corroborate the coding results and theories derived from the grounded theory. This reaffirms that data mining technology can be a potent tool for validating grounded theory. [ABSTRACT FROM AUTHOR]

Social media platforms have revolutionized the engagement between climate non-governmental organizations (hereafter, NGOs) and their publics on climate issues. This research diverges from the traditional use of metrics like retweets and likes as simple indicators of identical success in 'one-way' climate communication. Instead, it underscores 'two-way' interactions and their connections that may vary by specific public engagement features, such as popularity, commitment, and virality. Using semantic network analysis, we analyzed tweets and replies between high-engagement NGOs and their publics, identifying communication patterns tied to particular types of public engagement. Additionally, we investigated shared meanings in these interactions with semantic similarity metrics and assessed sentiment alignment between NGOs and their publics as potential indicators of public engagement. Our findings suggest that climate NGOs should select resonating topics, ensuring their sentiments align with those of their publics. It's also essential to tailor topics and focus points in climate communication strategies to reflect desired types of public engagement. This study offers insights into optimizing communication and engagement strategies for climate NGOs on social media. [ABSTRACT FROM AUTHOR]

The South Korean government aims to become a global leader in the hydrogen economy. However, policymakers and experts remain concerned about the gap between the institutional and technological perspectives. Based on socio-technical system theory, the transition to a hydrogen economy can be achieved through the co-evolution of both technology and institutions. Through expert surveys, this study explores core keywords and clusters using semantic network analysis. Consequently, four and six clusters are identified as vulnerable from technological and institutional perspectives, respectively. Overall, hydrogen-related institutions must develop and support the fast-growing hydrogen technologies. The South Korean case offers practical policy implications for the co-evolution of the socio-technical system toward a transition to a hydrogen economy. [Display omitted] • Experts are concerned about gaps in institutional and technological perspectives. • Hydrogen economy transition requires institution–technology co-evolution. • Keywords and clusters were objectively studied through semantic network analysis. • Institutionalised focus is needed for sustainable hydrogen technology advancement. [ABSTRACT FROM AUTHOR]

Traditional news outlets, such as newspapers and television, are no longer major sources of news. These media channels have been replaced by social platforms, which have increased in value as information distributors. This change in communication is an underlying reason for the election fraud controversies that occurred in the United States and South Korea, which hold high standards of democracy, during similar periods. This study investigates a model for sharing political disputes over social networks, especially Twitter, and illustrates the influence of political polarization. This study examines Twitter content around the presidential elections in the United States and South Korea in 2020 and 2022, respectively. It applies semantic network analysis and structural topic modeling to describe and compare the dynamics of online discourse on the issue of election fraud. The results show that online spaces such as Twitter serve as public spheres for discussion among active political participants. Social networks are key settings for forming and spreading election fraud controversies in the United States and South Korea, with differences in content. In addition, the study applies large-volume text data and new analytical methods such as the structural topic model to examine the in-depth relationships among political issues in cyberspace. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 pandemic is farreaching, encompassing the social, economic, and psychological aspects of the society. . In order to prevent the spread of COVID-19, various countries, including South Korea, have entered into long-term home care and distance learning systems. However, distance learning experiments conducted in many countries have raised whether face-to-face education can be replaced by distance learning. Therefore, this study analyzed public opinion, social perception, and field trends based on media reports on remote learning. For this purpose, 2,600 articles from 11 newspapers and four broadcasters related to remote learning were collected in this study. Based on this data, keyword trend analysis, topic modeling analysis, semantic network analysis, and emotion analysis were performed. This study provides valuable insights into the impacts and perceptions of remote learning during the COVID-19 pandemic. It highlights the complexities of remote education, from influencing educational policies to affecting students' and parents' emotional states. The findings underscore the potential of remote learning in driving educational innovation while also pointing out its role in widening educational disparities. This research offers essential perspectives for shaping future educational strategies in crisis situations, emphasizing a balanced approach to harness the benefits of remote learning while mitigating its challenges. [ABSTRACT FROM AUTHOR]

On June 24, 2022, the Supreme Court released its decision in Dobbs v. Jackson Women's Health, which officially repealed Roe v. Wade and its subsequent rulings. Employing social network analysis and semantic analysis methods, the current project reviews the public reaction among Twitter users shared from the May 2 draft leak to the June 24 official repeal, using a series of Twitter hashtags related to Roe v. Wade. The project identified the main influencers within the network, namely, journalist/news organizations, Internet celebrities, activists/activist groups, professional/non-profit organizations, and politicians/political organizations through social network analysis. Through semantic analysis, the authors found prominent themes such as legal concerns, discourse on reproductive rights, distrusting of Supreme Court's authority, and political nepotism. The results offer policy implications and communication message strategies to healthcare providers and policymakers. The authors believe that the polarizing nature of Roe v. Wade-related issues will be a crucial factor in shaping voters' decisions during the upcoming 2024 presidential election. [ABSTRACT FROM AUTHOR]

Russia's cyber-enabled influence operations (CEIO) have garnered significant public, academic and policy interest. 126 million Americans were reportedly exposed to Russia's efforts to influence the 2016 US election on Facebook. Indeed, to the extent that such efforts shape political outcomes, they may prove far more consequential than other, more flamboyant forms of cyber conflict. Importantly, CEIOs highlight the human dimension of cyber conflict. Focused on 'hacking human minds' and affecting individuals behind keyboards, as opposed to hacking networked systems, CEIOs represent an emergent form of state cyber activity. Importantly, data for studying CEIOs are often publicly available. We employ semantic network analysis (SNA) to assess data seldom analyzed in cybersecurity research – the text of actual advertisements from a prominent CEIO. We examine the content, as well as the scope and scale of the Russian-orchestrated social media campaign. While often described as 'disinformation,' our analysis shows that the information utilized in the Russian CEIO was generally factually correct. Further, it appears that African Americans, not white conservatives, were the target demographic that Russia sought to influence. We conclude with speculation, based on our findings, about the likely motives for the CEIO. [ABSTRACT FROM AUTHOR]

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Green finance cooperation is an organic component of international cooperation among the globalization, but few studies focused on the status of green finance cooperation. Previous studies have confirmed the value of green finance cooperation within countries for green development, laying the foundation for initiating discussions on green finance cooperation between countries. This paper uses quantitative methods such as word frequency statistical analysis, visualization cloud map, and semantic network analysis to analyze the development process of Sino-US green finance cooperation on the basis of selected green finance cooperation texts. The results show that multi-dimensional parties in Sino-US green finance cooperation play a role in promoting the cooperation process and resolving differences in cooperation cognition. Secondly, the auxiliary elements of Sino-US green finance cooperation have achieved value mobilization and effective integration. Thirdly, the development effect of Sino-US green finance cooperation has a relatively sensitive response to external shocks represented by political factors. [ABSTRACT FROM AUTHOR]

The knowledge produced about the third sector in one country can provide useful insights to understand the nonprofit sector in other countries. It goes without saying "Knowledge not shared is knowledge wasted." While the volume of research on the South Korean third sector has been growing in recent decades, its readership has been mostly limited to Korean language users, missing the opportunity to globalize knowledge. Recognizing an opportunity to spark conversations about how to share the third sector research done in one country with global readers, we conducted semantic network analysis to identify long-term patterns of research in South Korean third sector knowledge production, reviewing about 6500 nonprofit studies published between 1987 and 2019. With a bigram and trend analysis of key themes, we find key characteristics and discuss them in detail. This study contributes to the literature by highlighting the benefits of sharing knowledge produced in one country with scholars and practitioners in other countries. [ABSTRACT FROM AUTHOR]

This research aimed to examine overtourism-related papers published in the Web of Science and to identify research structure framework through network analysis between key keywords. Accordingly, the abstract of 110 papers related to overtourism from 2018 to 2021 was reviewed through text mining using Python. Afterwards, clusters derived through semantic network analysis were found to be Positive/Negative Impact of Tourism Development , Economic Causes , Efforts for Sustainability ," and Necessity of Policy. Through this, it was intended to present countermeasures against overtourism and directions for establishing policies. In addition, by deriving the main keywords for each cluster, basic data that can examine the relationship between overtourism phenomena in more detail were provided and contributed to the literature. [ABSTRACT FROM AUTHOR]