Prevalent Themes Research Articles

Unraveling the Cross-Cultural Differences in Online Expression of Social Anxiety in Online Support Communities.

Individuals suffering from social anxiety disorder (SAD) are increasingly turning to online support communities for self-disclosure and social support. Despite the extensive body of research on online mental health discourses, the cultural nuances within SAD-related discussions remain underexplored. In this study, we examine the cultural differences in online expression of social anxiety by analyzing individuals' self-disclosure and support-seeking behaviors in social media posts. Using two-week data (n = 1,681) from two SAD support communities on the Reddit and Douban groups, we used both qualitative thematic analysis and quantitative semantic analysis to discern prevalent themes and linguistic attributes characterizing these online expressions. Our findings not only uncover common themes such as sharing personal experiences and seeking mutual validations in both communities but also identify their divergences, as Western users primarily sought advice and information in posts, whereas Chinese users were more inclined toward networking. Cultural variations in language use were evident, particularly in individuals' affect and their expression of personal and social concerns. Western users were more likely to convey negative emotions and delve into personal matters related to SAD, whereas Chinese users tended to grapple more with workplace anxieties. This study contributes to the cultural understanding of online mental health discourses and offers insights for crafting culturally sensitive interventions and supports for people with SAD.

Augmented reality in marketing: a close look at the current landscape and future possibilities

PurposeThe purpose of this study is to consolidate the fragmented research on augmented reality (AR) as a marketing tool and provide a comprehensive understanding of its possible marketing applications.Design/methodology/approachThe study conducted a systematic review and bibliometric analysis of 103 papers on AR-marketing to identify the most prevalent topics and conceptual frameworks. Performance analysis and science mapping were utilized to examine the key marketing domains influenced by AR.FindingsThe analysis revealed that AR has had the biggest impact on marketing domains such as consumer acceptability, customer interactivity, retail, and destination marketing.Practical implicationsThe results of this study provide organizations with insights into the current state of AR-marketing, enabling them to successfully use AR to improve their marketing strategies. Furthermore, the study highlights potential areas for further research and development in AR for marketing.Originality/valueThis research offers a valuable, comprehensive overview of AR’s role in marketing by systematically reviewing and analyzing the existing literature. The findings open doors for organizations and researchers to explore AR’s potential applications in marketing strategies and future research opportunities.

University’s Official Instagram as a News Sources for The Media

Media relations play a crucial role in the public relations activities of universities, as they provide opportunities for increased media exposure. In today's journalism landscape, social media has become a prominent source of news for journalists. This includes official university accounts, which serve as valuable sources of information for journalists. Consequently, university public relations must understand the perspective of journalists when selecting and disseminating information through social media platforms. This article aims to explore how journalists utilize information from university social media accounts, specifically Instagram, as news sources. The concept of information subsidies is used to analyze the public relations activities involved in providing journalists with access to information and ensuring its quality. The research adopts a qualitative approach, utilizing interviews with journalists from the education sections of kompas.com and detik.com. The findings of this research highlight despite the prevalence of visual content, limited text, and informal language often used by university Instagram users, journalists still find value in quoting from Instagram. This suggests that the information available on social media platforms is sufficient to fulfill journalists' informational needs. Journalists also rely on university Instagram accounts as a source of news. The significance of higher education institutions acknowledging social media as a platform for distributing information to journalists. The prevalent topics covered in news articles include advice, contemporary matters, and education sector-related information. The majority of cited news items are derived from expert opinions or contributions from university lecturers. It is crucial to establish efficient strategies for social media management in order to accentuate various themes or content that showcase the expertise of university staff.

FLIPPED CLASSROOM METHODOLOGIES IN ESL EDUCATION: A BIBLIOMETRIC ANALYSIS OF RECENT TRENDS

The evolution of teaching methodologies in English as a Second Language (ESL) education has witnessed a paradigm shift with the integration of flipped classrooms. While the flipped classroom model has garnered attention for its potential to enhance language learning, there is a need for a systematic examination of the scholarly output in this domain. Identifying prolific authors, prevalent themes, and collaborative networks can provide insights into the current state of research and guide future investigations. We employed Scopus Analyzer to extract and analyze bibliographic data from publications from 2019 to 2023. Additionally, VOSviewer was utilized to map author keywords and prevalent themes in the literature. The analysis aims to spotlight key research clusters, potential collaborations, and the global impact of flipped classroom in ESL. The findings of this bibliometric analysis will contribute significantly to the ongoing discourse on flipped classroom in ESL. By unveiling trends, identifying influential authors, and mapping collaborative networks, this study provides a valuable resource for educators, researchers, and policymakers seeking to navigate the evolving landscape of ESL pedagogy. The multidimensional insights from this analysis aim to inspire further research, innovation and informed decision-making in flipped classroom methodologies in ESL education.

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

A critical discourse analysis of older women’s representation in housing and homelessness

Purpose Despite a significant evidence base illustrating the issue of housing insecurity and homelessness experienced by women over the age of 55 in Australia [Pawson et al., 2018; Australian Institute of Health and Welfare (AIHW), 2021b; Mission Australia 2022; Per Capita 2022], there continues to be a knowledge gap in the representation of older women in current Federal and New South Wales (NSW) State Government housing and homelessness policies and initiatives. This paper aims to identify the extent and ways in which older women were represented (or not represented) in the Federal and NSW State housing and homelessness policies in 2022. Design/methodology/approach Of the total primary and supplementary Federal and NSW State policy and strategy documents, 16 were collected through a systematic review and then analysed using a Critical Discourse Analysis (CDA) framework and feminist lens to explore quotes, phrases, keywords and language choices which suggested prevalent themes, rhetorical devices and dominant discourses. Findings Three significant themes were identified by the analysis (1) “relinquished responsibility”, (2) “inconsistent rhetoric” and (3) “homogenising and ideological cherry picking”. These themes presented the authors with three main discussion points to inform policy reform which we have addressed in relation to policy implications, evidence-based policy decision-making and impacts on older women. Originality/value The combination of a systematic review with CDA provides a unique approach to exploring homelessness policy for older women.

Envisioning Emerging Research Trends on Impact of COVID-19 on Indian Economy: A Bibliometric Study Applying Latent Semantic Analysis and Text Mining Technique

The COVID-19 pandemic ignited an unprecedented emergency for countries across the globe. The twin threats to lives and livelihood caused due to the pandemic brought severe ramifications to the Indian economy. The labour market faced the brunt exponentially, and the same is evident from the extensive research studies undertaken in this realm. A large volume of research has been published in this domain analysing the impact of COVID-19. This bibliometric study presents the dominant areas of current research upon analysing the extant literature on the economic impact of the pandemic. The study identifies prevalent topics and themes mapped from the existing knowledge base by applying natural language processing (NLP) and machine learning. The study proposes areas for further research. JEL Codes: I1, I3, E7 1, E2

Exploring the nutritional landscape and emerging trends in kidney stone research: visualization and bibliometric analysis

BackgroundKidney stones, or renal nephrolithiasis, are common and dangerous, increasing the risk of chronic kidney disease. Dietary manipulation is essential for the prevention and recurrence of kidney stones. Research into nutrition and kidney stones lacks library analysis, making it difficult to identify new trends. This bibliometric study was conducted to explore the current landscape of research on nutrition and kidney stones worldwide. This paper also emphasizes significant research trends in this area over the past two decades, aiming to assist researchers in understanding the current research status and identifying potential future directions.MethodsThe scholarly literature pertaining to diet and kidney stones was systematically explored utilizing SciVerse Scopus to identify pertinent research articles released from 2003 to 2022. The examination encompassed an assessment of publication patterns, key contributors, focal areas of research, prevalent themes, influential articles, and emerging research avenues. Following data extraction to Excel, analyses, including frequencies, percentages, and linear regression, were conducted. Visual exploration was facilitated through the use of the VOSviewer program version 1.6.19.ResultsBetween 2003 and 2022, 697 publications on nutrition and kidney stones were identified. There were 478 original articles (68.58%), 120 reviews (17.22%) and 99 other types of publications (14.20%). The United States is the most productive country, with significant growth in research in the fields of nutrition and kidney stones. The United States has demonstrated the strongest partnerships between researchers’ networks from various countries. Cluster analysis revealed three major research themes: sex differences in kidney stone disease, the correlation between increased dietary acid consumption and calcium oxide kidney stones, and nephrolithiasis associated with a ketogenic diet.ConclusionsThis study offers a thorough examination of nutrition and kidney stone research, encompassing key research domains, collaborative networks, and emerging patterns. The findings can aid researchers in gaining insight into the present landscape of the discipline and determining future research directions.

Public perceptions of synthetic cooling agents in electronic cigarettes on Twitter.

Amid a potential menthol ban, electronic cigarette (e-cigarette) companies are incorporating synthetic cooling agents like WS-3 and WS-23 to replicate menthol/mint sensations. This study examines public views on synthetic cooling agents in e-cigarettes via Twitter data. From May 2021 to March 2023, we used Twitter Streaming Application Programming Interface (API), to collect tweets related to synthetic cooling agents with keywords such as 'WS-23,' 'ice,' and 'frozen.' The deep learning RoBERTa (Robustly Optimized BERT-Pretraining Approach) model that can be optimized for contextual language understanding was used to classify attitudes expressed in tweets about synthetic cooling agents and identify e-cigarette users. The BERTopic (a topic modeling technique that leverages Bidirectional Encoder Representations from Transformers) deep-learning model, specializing in extracting and clustering topics from large texts, identified major topics of positive and negative tweets. Two proportion Z-tests were used to compare the proportions of positive and negative attitudes between e-cigarette users (vapers) and non-e-cigarette-users (non-vapers). Of 6,940,065 e-cigarettes-related tweets, 5,788 were non-commercial tweets related to synthetic cooling agents. The longitudinal trend analysis showed a clear upward trend in discussions. Vapers posted most of the tweets (73.05%, 4,228/5,788). Nearly half (47.87%, 2,771/5,788) held a positive attitude toward synthetic cooling agents, which is significantly higher than those with a negative attitude (19.92%,1,153/5,788) with a P-value < 0.0001. The likelihood of vapers expressing positive attitudes (60.17%, 2,544/4,228) was significantly higher (P < 0.0001) than that of non-vapers (14.55%, 227/1,560). Conversely, negative attitudes from non-vapers (30%, 468/1,560) were significantly (P < 0.0001) higher than vapers (16.2%, 685/4,228). Prevalent topics from positive tweets included "enjoyment of specific vape flavors," "preference for lush ice vapes," and "liking of minty/icy feelings." Major topics from negative tweets included "disliking certain vape flavors" and "dislike of others vaping around them." On Twitter, vapers are more likely to have a positive attitude toward synthetic cooling agents than non-vapers. Our study provides important insights into how the public perceives synthetic cooling agents in e-cigarettes. These insights are crucial for shaping future U.S. Food and Drug Administration (FDA) regulations aimed at safeguarding public health.

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices and they can take actions accordingly. The extraction of meaningful insights from the vast and dynamic stream of social media data is greatly facilitated by the implementation of topic modeling and sentiment analysis on Twitter tweets. The identification of recurring themes or subjects within a collection of tweets, which is the essence of topic modeling, serves to reveal patterns in discussions, thereby aiding in the comprehension of prevalent topics and trends. Concurrently, sentiment analysis, through the evaluation of the emotional tone of tweets, enables the discernment of whether the expressed sentiments are positive, negative, or neutral. The combination of these techniques provides researchers and businesses with valuable perspectives on public opinions, emerging issues, and user sentiments, thereby empowering them to make informed decisions and develop effective engagement strategies in the ever-evolving landscape of social media. But twitter data(tweets) are unstructured ,contains noisy data, urls , stop words, re-tweets video ,emoji etc., which need to be cleaned and preprocessed to perform proper sentiment analysis and use it to extract meaningful effective insights from it. This paper focuses on various methods of topic modeling and discovering latent topics ,text-mining approaches ,micro-blogging methods used in various researches .This paper focuses on latent dirichlet allocation method of topic modeling and text-mining to discover latent topics in tweets, micro-blogging ,text-mining approaches .A proper survey is done on previous researches on this topic in this article. Keywords:-LDA, text-mining, topic modeling, micro-blogging ,machine learning ,sentiment analysis, NLP.

Growth, trends and prevalent topics in geotechnical engineering (1998–2022) using topic modelling

Data gathered from 65 922 articles published in 30 geotechnical engineering journals between 1998 and 2022 were used to build a topic model and study the evolution of research output in the field. Over this period, the number of journal publications has grown exponentially with the number of articles published per year doubling every 6·3 years. The average citations per article (32·7) are skewed by highly cited articles, which make up 7·8% of the articles but account for 34·5 of the total number of citations. The articles’ country of origin has become increasingly centralised, with nearly half the articles published between 2018 and 2022 coming from China (39%) and the United States (10%). The topic distribution in the field has become highly specialised, with emerging topics being fuelled by: (a) new technologies and methods (i.e. data science and sensing techniques – the fastest-growing topics), and (b) rising applications seemingly related to mitigation (e.g. offshore and energy geotechnics) and adaptation (e.g. geohazards, mass movements) to climate change. As a consequence, traditional topics (e.g. soil and rock mechanics) have reduced their share in the field, but still remain among the most cited topics in geotechnical engineering.

Inclusive growth: Literature review

The paper presents a comprehensive analysis of scholarly literature within the domain of inclusive growth. It meticulously categorises diverse approaches aimed at delineating the essence of inclusive growth, encompassing the articulation of principal objectives, and identifying challenges that inclusive growth endeavours to address. Furthermore, to enhance the organization of research and attain a nuanced understanding of prevalent, well-explored, and under-researched themes, the study advocates for a supplementary bibliometric analysis of publications on inclusive growth. Utilizing data extracted from the Scopus and Web of Science databases from 1995 to 2022, this research discerns notable scholarly interest in inclusive growth, particularly among academic communities in the United States, India, and Great Britain. The findings delineate several focal points within the realm of inclusive growth, including the determinants of developmental trajectories, the constituents and metrics of economic expansion, as well as the evaluation and prognostication of its outcomes. Moreover, the study underscores the significance, characteristics, and potential avenues of inclusive growth, alongside the imperative of financial inclusivity and its role in fostering equitable economic development. Additionally, it identifies key components essential for achieving sustainable development objectives, elucidating how inclusive growth initiatives are intricately linked to the mitigation of pervasive societal challenges such as poverty, inequality, and unemployment. By identifying prominent clusters of scholarly discourse, this research facilitates a nuanced comprehension of the most salient themes and issues animating the discourse on inclusive growth, thus providing a valuable foundation for future scholars and policymakers in this crucial area.

Exploring the role of ethical experiences and psychological well-being in travel satisfaction: An animal welfare perspective in elephant-based tourism

Given growing concerns about animal welfare in tourism, this study examined the crucial role of tourists' ethical experiences and psychological well-being in travel satisfaction by comparing online reviews from two elephant-based tourism settings: a rescue center and general tour sites. Topic modeling and sentence sentiment analysis revealed more prevalent themes of ethical experiences and psychological well-being at the rescue center than at other sites. Path analysis confirmed a significant transition from ethical experiences to psychological well-being and satisfaction only at the rescue center, highlighting the significant mediator of psychological well-being in ethical tourism. This study offers an enhanced understanding of ethical tourism experiences from the animal welfare perspective, thereby expanding the literature on animal-based tourism. This study suggests an integrative text analysis to enhance research integrity. The findings can assist destination marketing organizations in developing policies and guidelines that promote ethical practices among tour operators, leading to a competitive advantage.

Bridging Bytes and Behaviors: Unraveling the Multifaceted Interplay of Technology and Employee Dynamics Over Time Through Text-mining and Systematic Literature Review

Purpose - The study is conducted to explore the complex relationship between technological integration and its consequences for employees, examining the multifaceted impacts of advanced technologies on organizational operations and workforce dynamics over the past four decades.&#x0D; Design/Methodology/Approach - To accomplish this, the study employs a robust approach involving rigorous text-mining analysis and a systematic literature review encompassing a vast dataset of 7,000 articles published from 1983 to 2022. This methodology is meticulously designed to discern the evolution of technology-related research by tracing prevalent topics and keywords throughout the extensive literature corpus.&#x0D; Findings - The study's findings indicate mixed effects of technology on employee parameters. While technology integration can lead to negative impacts such as increased turnover intention, anxiety, and health issues, recent literature also points to a paradigm shift, highlighting the contrast between technology-mediated and traditional face-to-face interactions.&#x0D; Research Implications - Organizations should consider the dual nature of technology's impact on the workforce. There is a critical need for integrating technology in a way that boosts productivity while also prioritizing employee well-being. The study suggests that redefining work-life dynamics is essential to maintain a harmonious balance in the increasingly digitalized workplace. The findings lay the groundwork for future research on understanding the complex effects of technology in organizational settings.

'They Talk about the Weather, but No One Does Anything about It': A Mixed-Methods Study of Everyday Climate Change Conversations.

Understanding everyday conversations about climate change may provide insights into framing the issue to promote climate change action. As part of a longitudinal online study in the US launched in June 2021, 805 respondents were asked if they had discussed climate change with a friend or family member in the prior month; if not, why not, and if yes, they were asked to delineate the conversation topic. Concurrent mixed methods were used to analyze the data. The majority (62.6%) of respondents reported not having a conversation about climate change in the prior month. Among those who indicated that they had discussed climate change, five themes were identified from the conversation topics, with many having reported discussing the impact of climate change on weather patterns. Very few discussed actions to address climate change, and most of these discussions focused on individual-level behaviors rather than collective actions. Among participants who had not recently discussed climate change, the most prevalent theme was that it was not a priority or an issue they cared about. Results suggest that conversations may not lead to collective actions and that policymakers and environmental organizations should provide guidance on effectively channeling climate change concerns into action.

Current patterns and trends of microplastic pollution in the marine environment: A bibliometric analysis.

Microplastics are pervasive in the natural environment and pose a growing concern for global health. Plastic waste in marine environments has emerged as a global issue, threatening not only marine biota but also human health due to its implications for the food chain. This study aims to discern the patterns and trends of research, specifically on Marine Microplastic Pollution (MMP), based on a bibliometric analysis of scientific publications from 2011 to 2022. The methodology utilized in this study comprises three stages: (a) creating a bibliographical dataset from Scopus by Elsevier and the Web of Science Core Collection by Clarivate Analytics, (b) analyzing current research (trends and patterns) using bibliometric analysis through Biblioshiny tool, and (c) examining themes and subthemes in MMP research (wastewater treatment, plastic ingestion, the Mediterranean Sea, microplastics pollution, microplastics in freshwater, microplastic ingestion, plastic pollution, and microplastic pollution in the marine environment). The findings reveal that during the studied period, the number of MMP publications amounted to 1377 articles, with an average citation per publication of 59.23 and a total citation count of 81,553. The most cited article was published in 2011, and since then, the number of publications on this topic has been increasing steadily. The author count stood at 5478, with 22 trending topics identified from the 1377 published titles. Between 2019 and 2022, the countries contributing most to the publication of MMP articles were China, the United States of America (USA), and the United Kingdom (UK). However, a noticeable shift in the origin of author countries was observed in the 2019-2022 timeframe, transitioning from a dominance by the USA and the UK to a predominance by China. In 2019, there was a substantial increase in the volume of publications addressing the topic of microplastics. The results show that the most prevalent themes and subthemes pertained to MMP in the Mediterranean Sea. The journals with the highest number of MMP articles published were the Marine Pollution Bulletin (253 articles) and Science of the Total Environment (190 articles). The analysis concludes that research on MMP remains prominent and appears to be increasing each year.

Decoding global reproductive health discourse on Reddit: themes, regions, and misinformation challenges.

This study explores dominant themes in global reproductive health discussions on Reddit over a 12-month period (January 2021 to December 2021). We collected and analyzed 50,000 posts and 100,000 comments from key subreddits, including "r/ReproductiveHealth" and "r/Parenting," using Python libraries like PRAW and BeautifulSoup for efficient data collection and preprocessing. Employing tools such as Latent Dirichlet Allocation (LDA) for topic modeling and VADER for sentiment analysis, we identified five major themes: Family Planning and Contraception, Pregnancy and Childbirth, Sexual Education and Awareness, Misinformation and Myths, and Support and Emotional Well-being. Geospatial analysis revealed regional variations, with North America and Europe emphasizing family planning, while Asia and Africa took the lead in discussions on pregnancy and childbirth. Our findings shed light on a global dispersion of misinformation discussions, emphasizing the challenges posed by misinformation in online reproductive health conversations. This research provides a nuanced understanding of prevalent themes and community engagement, contributing valuable insights to the dynamics of reproductive health discussions on Reddit.

Assessing the public discourse on Twitter: Reactions to the JUUL e-cigarettes ban in the United States.

JUUL is a high-nicotine pod-based vaping device that is popular among adolescents and young adults. On 23 June 2022, the US Food and Drug Administration (FDA) denied authorization to market JUUL, and ordered JUUL Labs to remove products from the US market. The next day, a US federal appeals court temporarily suspended the ban. The mixed public discourse surrounding the FDA ban warrants further investigation. This study examined Twitter data to describe public reaction to these announcements. Posts containing terms 'JUUL' and/or '#JUUL' (N=97548 unique tweets) were collected from 23 June to 3 July 2022, from Twitter's Streaming Application Programming Interface (API). After removing retweets, we used an inductive approach to become familiar with the data, generated a codebook, and conducted a content analysis on a random sample of n=4000 tweets. A total of 2755 (68.9%) tweets discussed JUUL in the context of the FDA ban. News (n=1425/2755; 51.7%) about the JUUL ban, government distrust (n=588; 21.3%), and individual rights (n=253; 9.2%) were the most prevalent themes. Less commonly discussed themes included inconsistencies between policies (n=174; 6.3%), mentions of switching to other products (n=162; 5.9%), smoking cessation (n=99; 3.6%), and craving for JUUL (n=94; 3.4%). Sentiment analysis of JUUL ban-related posts (n=2755) demonstrated that 1989 (72.2%) tweets were categorized as neutral, while anti-ban posts (n=566; 20.5%) were more prevalent than pro-ban posts (n=200; 7.3%). Besides straightforward announcements of the JUUL ban and its suspension, Twitter posts discussed government distrust, individual rights, and policy inconsistencies. While most posts conveyed neutral sentiments, anti-ban posts were almost three times more prevalent than pro-ban posts. Our findings suggest that text-based social media platforms like Twitter may be an effective instrument to understand opinions, attitudes, and beliefs regarding the FDA's JUUL ban.

Trends in stroke-related journals: Examination of publication patterns using topic modeling

ObjectivesThis study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. Materials and methodsOur study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. ResultsOut of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. ConclusionsThe methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata.

China deserves its hamburger: the controversy over WildAid’s Shu Shi campaign in China

ABSTRACT We analyze the responses of Chinese netizens to U.S.-based WildAid’s Shu Shi campaign, which was launched in China in 2018 with aims to reduce the nation’s meat consumption in support of climate change mitigation. We conduct an interpretive content analysis of approximately 3,000 online comments, finding that nationalist sentiment is the most prevalent theme underlying responses to the campaign. Netizens generally see the campaign as a criticism of their national identity. They define this identity by emphasizing China’s achievements, expressing a need for the population’s autonomy, recalling a collective history of humiliation by Western actors, and taking pride in Chinese food culture. Feelings among netizens of unfair treatment weakened the campaign’s effectiveness. Respondents cite national identity, nationalism, and climate justice in micro-level discourses to challenge the legitimacy of WildAid’s efforts to encourage reduced meat consumption as a climate change mitigation strategy in China. This study adds depth and nuance to our understanding of national identity, nationalism, and the perceived effectiveness of strategies encouraging behavior change to mitigate climate change.