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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association 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.)

Alzheimers disease and related dementias (ADRD) are a spectrum of disorders characterized by cognitive decline, which pose significant challenges for both affected individuals and their caregivers. Previous literature has focused on patient family surveys which do not always capture the breadth of authentic experiences of the caregiver. Online social media platforms provide a space for individuals to share their experiences and obtain advice toward caring for those with ADRD. This study leverages Reddit, a platform frequented by caregivers seeking advice for caring for a family member with advice for ADRD. To identify the topics of discussion or advice that most caregivers seek and sought after, we employed structured topic modeling techniques such as BERTopic to analyze the content of these posts and use an intertopic distance map to discern the variation in themes across different Reddit categories. In addition, we analyze the sentiment of the Reddit postings using Valence Aware Dictionary and Sentiment Reasoner to deduce the degree of negative, positive, and neutral sentiment of the discussion posts. Our findings reveal that the topics that caregivers most frequently discuss and seek advice for were related to caregiver stories, community support, and concerns ADRD. Specifically, we aimed to reproduce an organic Reddit search of caregiving of abuse on family member, financial struggles, symptoms of hallucinations, and repetition in ADRD family members. These results underscore the importance of online communities for gaining a comprehensive understanding of the multifaceted experiences and challenges faced by ADRD caregivers.

Rework remains an ever-present reality in construction projects. How rework data is defined, its format, location, and quantification contribute to the difficulty in managing its risks. This paper examines the challenges and acquired learnings encountered while attempting to create and curate a domain rework ontology in construction. An explanatory case study approach utilizing the lens of pragmatism is used to develop a rework ontology in a real-life transport infrastructure mega-project procured using a program alliancing delivery strategy. As part of an alliance's continuous improvement strategy, it aims to redress its rework problem by assessing its risk pathways and managing them using a semantic model through an ontology. We use a hybrid approach to develop an ontology. The Correlation Explanation (CorEx) topic modelling approach, a machine learning method, is used to help define our ontology's scope and generate a rework taxonomy representative of practice. The theoretical and practical implications of developing a domain rework are also discussed. Our paper's main contribution is the propagation of a knowledge repository that others can learn from when developing an ontology and addressing the problem of rework, which is often embedded in the practice of construction. [ABSTRACT FROM AUTHOR]

Social comparison theory provides valuable insights into how individuals assess themselves by comparing various facets of their lives to others. This theory offers a framework that helps in understanding the motivations behind self‐evaluation and improvement, as well as shaping consumer behaviors. The extensive body of research related to the theory has resulted in numerous publications across various domains, hindering a comprehensive overview of the research field and constraining its progress. Our multi‐disciplinary study addresses the fragmented scholarly landscape through a hierarchical bibliometric analysis, unveiling key authors, themes, and connections, and emphasizing its current state and future potential, particularly in the marketing domain. Our research indicates that interest in social comparison theory has surged, driven by social media's impact on body image and self‐esteem. Our findings also highlight the significance of themes such as body image, envy, social media, motivation, and life satisfaction, revealing the multifaceted expansion of the theory across various fields. [ABSTRACT FROM AUTHOR]

This research aims to examine the nuclear fusion technology field using the intellectual structure drawing technique. It is worth mentioning that the reason for focusing on this area, atomic fusion, is its peaceful applications and unlimited fuel to generate power. The proposed strategy in this paper includes all the keywords related to the research topic. VOS viewer and R Studio software were employed for data analysis. In addition, the principal research topics were extracted and classified using scientific maps. This analysis indicates the intellectual structure of the topic, which is grouped into four fundamental clusters using citation networks. This study additionally identifies emerging issues and future research directions implementing strategic diagrams. Another aspect of this study that reveals the intellectual foundation of the research is that it provides insights into the importance of crucial topics, institutions, and publications in terms of the field's focus and the results obtained from the analysis of the period 1991-2023. It informs that the conceptual evolution of nuclear fusion and its growth is proceeding. [ABSTRACT FROM AUTHOR]

More than a decade has passed since researchers began discussing predatory publishing, one of the most unethical practices in academia. Nevertheless, few attempts have been made to provide a comprehensive overview of the research on predatory publishing. Therefore, this study conducted bibliometric and topic analysis on 812 papers collected from the Web of Science database. The results showed that, although the annual publication volume decreased slightly in the last two years, the annual citations continued to rise. The United States and its institutions are global leaders in predatory publishing research. The most active journal was Learned Publishing, whereas the most influential was Nature. Furthermore, eleven research topics regarding predatory publishing, academic publishing, or the research community were identified and interpreted. Based on these results, this study discusses agendas such as highlighting the global interest in the issue, the need for large-scale collaboration and a sustainable research environment, promoting the issue of predatory publishing, and the importance of education for stakeholders. The findings are expected to help researchers and policymakers understand global research trends in predatory publishing. [ABSTRACT FROM AUTHOR]

In canine leishmaniosis (CanL), complex interactions between the parasites and the immunological background of the host influence the clinical presentation and evolution of infection and disease. Therefore, the potential use of nutraceuticals as immunomodulatory agents becomes of considerable interest. Some biological principles, mainly derived from plants and referred to as plant-derived nutraceuticals, are considered as supplementation for Leishmania spp. infection. This study provides a systematic review regarding the use of nutraceuticals as a treatment using a text mining (TM) and topic analysis (TA) approach to identify dominant topics of nutritional supplements in leishmaniosis-based research, summarize the temporal trend in topics, interpret the evolution within the last century and highlight any possible research gaps. Scopus® database was screened to select 18 records. Findings revealed an increasing trend in research records since 1994. TM identified terms with the highest weighted frequency and TA highlighted the main research areas, namely "Nutraceutical supports and their anti-inflammatory/antioxidant properties", "AHCC and nucleotides in CanL", "Vit. D3 and Leishmaniosis", "Functional food effects and Leishmaniosis" and "Extract effects and Leishmaniosis". Despite the existing academic interest, there are only a few studies on this issue so far, which reveals a gap in the literature that should be filled. [ABSTRACT FROM AUTHOR]

Simple Summary: The present study aimed to provide critical analysis in a systematic review of the peer-reviewed literature indexed in the Scopus database to analyze the studies on ostriches' welfare in the last forty years in order to identify knowledge gaps in the literature. A bibliometric analysis of metadata and content analysis based on text mining and topic modeling techniques has been conducted on a sample of approximately n. 987 publications, confirming the increased focus on ostriches' welfare. Ostriches can be utilized as multipurpose animals suitable for producing meat, eggs, feathers, and leather. This growing interest in ostrich farming leads to an increased demand for comprehensive information on their management. But, little attention is paid to the consequences for their welfare. The study aimed to perform a research literature analysis on ostriches' welfare using the text mining (TM) and topic analysis (TA) methods. It identifies prevailing topics, summarizes their temporal trend within the last forty years, and highlights potential research gaps. According to PRISMA guidelines, a literature exploration was achieved using the Scopus® database, retaining keywords about ostriches' welfare. Papers distributed in the English language from 1983 to 2023 were included. Descriptive statistics, TM, and TA were applied to a total of n. 122 documents included. The findings revealed an increasing trend in research records since 1994. TM recognized the terms with the highest weighted frequency and TA identified the main topics of the research area, in the following order: "health and management", "feeding and nutrition", "welfare reproduction", "egg production", and "welfare during transport". The study confirms the increased focus on ostriches' welfare but shows that further studies are required to ensure the welfare of this species. [ABSTRACT FROM AUTHOR]

Topic analysis represents each document in a text corpus in a low-dimensional latent topic space. In some cases, the desired topic representation is subject to specific requirements or guidelines constituting side information. For instance, sustainability-aware investors might be interested in automatically assessing aspects of firm sustainability based on the textual content of its corporate reports, focusing on the established 17 UN sustainability goals. The main corpus consists of the corporate report texts, while the texts containing the definitions of the 17 UN sustainability goals represent the side information. Under the assumption that both text corpora share a common low-dimensional subspace, we propose representing them in such a space via directed topic extraction using matrix co-factorization. Both the main and the side text corpora are first represented as term–context matrices, which are then jointly decomposed into word–topic and topic–context matrices. The word–topic matrix is common to both text corpora, whereas the topic–context matrices contain specific representations in the shared topic space. A nuisance parameter, which allows us to shift the focus between the error minimization of individual factorization terms, controls the extent to which the side information is taken into account. With our approach, documents from the main and the side corpora can be related to each other in the resulting latent topic space. That is, the corporate reports are represented in the same latent topic space as the descriptions of the 17 UN sustainability goals, enabling a structured automatic sustainability assessment of the textual report's content. We provide an algorithm for such directed topic extraction and propose techniques for visualizing and interpreting the results. [ABSTRACT FROM AUTHOR]

The growing interest in environmental sustainability issues and, at the same time, the advantages offered by blockchain technology have strong connections to each other. This study explores the application of blockchain technology across various environmental domains, such as air quality, climate change impacts, and resource management. The research utilised a dual approach, combining a bibliometric analysis with VOSviewer and a topic analysis using BERT models to assess the discourse within both the scientific literature extracted from Scopus and practical blockchain projects obtained from GitHub. The findings reveal that food security, energy, and sustainable agriculture are predominant topics in academic discussions, with a noticeable increase in focus from 2017 onwards. Practical projects are focused on transparent tracking and decentralised management. The overlap between academic and practical spheres is evident in the shared focus on energy and environmental management, demonstrating blockchain's growing role in addressing global environmental challenges. This study underscores the importance of integrating theoretical research with practical implementations to harness blockchain's full potential in promoting sustainable environmental practices. [ABSTRACT FROM AUTHOR]

This research aims to examine the nuclear fusion technology field using the intellectual structure drawing technique. It is worth mentioning that the reason for focusing on this area, atomic fusion, is its peaceful applications and unlimited fuel to generate power. The proposed strategy in this paper includes all the keywords related to the research topic. VOS viewer and R Studio software were employed for data analysis. In addition, the principal research topics were extracted and classified using scientific maps. This analysis indicates the intellectual structure of the topic, which is grouped into four fundamental clusters using citation networks. This study additionally identifies emerging issues and future research directions implementing strategic diagrams. Another aspect of this study that reveals the intellectual foundation of the research is that it provides insights into the importance of crucial topics, institutions, and publications in terms of the field's focus and the results obtained from the analysis of the period 1991-2023. It informs that the conceptual evolution of nuclear fusion and its growth is proceeding.

Topic analysis represents each document in a text corpus in a low-dimensional latent topic space. In some cases, the desired topic representation is subject to specific requirements or guidelines constituting side information. For instance, sustainability-aware investors might be interested in automatically assessing aspects of firm sustainability based on the textual content of its corporate reports, focusing on the established 17 UN sustainability goals. The main corpus consists of the corporate report texts, while the texts containing the definitions of the 17 UN sustainability goals represent the side information. Under the assumption that both text corpora share a common low-dimensional subspace, we propose representing them in such a space via directed topic extraction using matrix co-factorization. Both the main and the side text corpora are first represented as term–context matrices, which are then jointly decomposed into word–topic and topic–context matrices. The word–topic matrix is common to both text corpora, whereas the topic–context matrices contain specific representations in the shared topic space. A nuisance parameter, which allows us to shift the focus between the error minimization of individual factorization terms, controls the extent to which the side information is taken into account. With our approach, documents from the main and the side corpora can be related to each other in the resulting latent topic space. That is, the corporate reports are represented in the same latent topic space as the descriptions of the 17 UN sustainability goals, enabling a structured automatic sustainability assessment of the textual report’s content. We provide an algorithm for such directed topic extraction and propose techniques for visualizing and interpreting the results.

Text mining and topic analysis algorithms which group textual contents in the most efficient way, are becoming increasingly useful to summarise the main information contained in large data corpus of complex scientific fields. Using the literature about reindeer pastoralism as a case study, this methodological investigation addressed the issue related to the identification of the suitable number of topics that provide the best in-depth interpretation of a large data corpus. Two-thousand eight hundred and seventy-five documents extracted from Scopus® regarding the scientific literature of reindeer pastoralism were used. Four simulations with 8, 10, 12, and 20 topics were carried out to define the optimal number of topics that best explained the issues related to reindeer husbandry. The results showed that a reasonable trade-off between the number of articles and the number of topics, based on the reduction of the variance explained within the group, leads to an optimal choice in the search for the most meaningful simulation. The adoption of a too large number of topics, with the excessive fragmentation of the data corpus into small aggregations of documents, encourages the emergence of topics without any technical or practical meaning, solely as a result of the unsupervised iterative process.HIGHLIGHTS Text mining for insight vast and complex scientific fields: a case study on reindeer pastoralism. Optimising topic identification to strike a balance between the size of the articles corpus and the number of topics and achieve the most insightful results. Too many topics can lead to fragmentation and irrelevant results, while too few may oversimplify the complexity of the dataset.

Buffalo is a rustic animal; it can adapt and survive in various environments with different topography, climate and vegetation, and has a greater longevity than cattle. The high resistance to parasites, as well as the ability to improve the efficiency of feed consumption in case of forage shortage, means that even in marshy territories, where any other type of livestock or agricultural activity is impossible, buffalo farming can develop an economy. This review aims to present the key research topics, the evolution and geographic distribution of literature on buffalo welfare, and to underscore potentiality and existing knowledge gaps. A comprehensive search on the Scopus bibliometric database was carried out using various welfare-related keywords such as: ‘buffalo welfare’, ‘buffalo calf welfare’ and ‘buffalo heifers’ welfare’. The research identified 308 papers published in English from January 1992 to December 2023 that, after filtering, became 148 eligible records. Descriptive statistics revealed a significant exponential increase in studies on buffalo welfare since 2020, particularly in Italy, followed by Brazil. Text mining and topic analysis revealed that the most frequently mentioned terms were ‘milk’, ‘water’, ‘behaviour’, ‘farm’ and ‘stun’, confirming the role and the importance of this species in dairy production. Nine identified topics span diverse subject areas such as ‘slaughter process’, ‘dairy production’, ‘general welfare’, ‘housing system’, ‘heat stress’, ‘reproduction performance’, ‘health status’ ‘breeding management’, and ‘stress evaluation’. Although academia shows an increasing interest in buffalo behaviour and welfare, continued research is necessary to fully comprehend how to ensure the welfare of this important species.

The growing interest in environmental sustainability issues and, at the same time, the advantages offered by blockchain technology have strong connections to each other. This study explores the application of blockchain technology across various environmental domains, such as air quality, climate change impacts, and resource management. The research utilised a dual approach, combining a bibliometric analysis with VOSviewer and a topic analysis using BERT models to assess the discourse within both the scientific literature extracted from Scopus and practical blockchain projects obtained from GitHub. The findings reveal that food security, energy, and sustainable agriculture are predominant topics in academic discussions, with a noticeable increase in focus from 2017 onwards. Practical projects are focused on transparent tracking and decentralised management. The overlap between academic and practical spheres is evident in the shared focus on energy and environmental management, demonstrating blockchain’s growing role in addressing global environmental challenges. This study underscores the importance of integrating theoretical research with practical implementations to harness blockchain’s full potential in promoting sustainable environmental practices.

Gender differences have been found in the outcomes of vocational rehabilitation (VR) and in Supported Employment (SE), therefore, the purpose of this study was to explore employment specialists' (ES) social representations of gender in relation to work and VR and how these social representations influence the ES's work in the VR process according to SE. The qualitative method of focus group discussions was employed. Ten focus groups were held with 39 ESs from four categories of SE organizations in Sweden. Topic analysis was applied to the transcribed material from the focus groups. Five themes with different social representations about gender and disability in relation to VR and working life formed in the analysis: (1) differences in personal and health factors among VR-participants, (2) gender norms in society influencing VR, (3) energy-intensive environmental issues influencing VR, (4) gender-specific interactions in VR, and (5) gendered paths in the welfare system. Social representations of higher strains on women with disabilities compared to men with disabilities both in private and working life, which reflect the lived experiences of the ESs, is a possible explanation for gender differences in VR and working life for persons with disabilities. Gender differences in the outcomes of vocational rehabilitation (VR) interventions have been noticed worldwide. In this study, pervasive social representations about gender and disability in relation to VR were found, but also unconsciousness about gender among VR professionals. Therefore, awareness of gender is necessary in VR. Individualized and person-centered approaches to VR like Supported Employment, although considered best practice, might hide structures like gender, which could lead to reproducing discriminating processes and therefore not achieving gender equality in VR. Therefore, knowledge of discrimination is important in VR. Gender-sensitivity in vocational rehabilitation models with a person-centered and individualized approach is needed and there might also be a need to systematically tailor vocational rehabilitation models to reach gender equality in the outcomes of the interventions. Therefore, gender equality indicators are needed for VR models, to evaluate model effectiveness from a gender perspective. [ABSTRACT FROM AUTHOR]

Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study aims to provide a research literature analysis on BE in dogs based on text mining (TM) and topic analysis (TA) identifying dominant topics, summarizing their temporal trend, and highlighting any possible research gaps. A literature search was performed utilizing the Scopus® database, employing keywords pertaining to BE to analyze papers published in English from 1990 to 2023. The investigation followed a systematic approach based on the PRISMA guidelines. A total of 86 records were selected for analysis following screening procedures and underwent descriptive statistics, TM, and TA. The findings revealed that the number of records published per year has increased in 2007 and 2021. TM identified the words with the highest term frequency-inverse document frequency (TF-IDF), and TA highlighted the main research areas, in the following order: causative agents, clinical findings and predisposing factors, case reports on endocarditis, outcomes and biomarkers, and infective endocarditis and bacterial isolation. The study confirms the increasing interest in BE but shows where further studies are needed. [ABSTRACT FROM AUTHOR]

The main aim of this study is to conceptualize visitors' experience in heritage hotels by using organic data in the form of Tripadvisor reviews and ratings of the people who stayed in heritage hotels. The reason is that heritage hotels, a cross-section of heritage and lodging, need to be more adequately studied due to the paucity of empirical research in the field. This study comprehends the sentiments towards heritage hotels, heritage experience value and dimensions, and attribute-based performance models for heritage hotels. It is grounded on sentiment and topic analysis of 6626 online Tripadvisor reviews of heritage hotel visitors. The result defines visitor experience in heritage hotels using heritage, architecture, tradition, beauty, people, and place. The result also shows that people generally have positive sentiments towards heritage hotels. Cultural heritage value, location experience, service experience, hotel, and basic amenities determine heritage hotels' performance, among which cultural heritage value has a positive impact. Heritage hotel managers, DMOs, and local policymakers can use this contemporary study to define heritage hotels and develop programs and policies to promote them. [ABSTRACT FROM AUTHOR]

Digital twinning enables manufacturers to create digital representations of physical entities, thus implementing virtual simulations for product development. Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages, failing to cover the gap between physical and digital spaces. This work mines real-world consumer feedbacks through social media topics, which is significant to product development. We specifically analyze the prevalent time of a product topic, giving an insight into both consumer attention and the widely-discussed time of a product. The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution. Therefore, these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics. To this end, this work combines deep learning and survival analysis to predict the prevalent time of topics. We propose a specialized deep survival model which consists of two modules. The first module enriches input covariates by incorporating latent features of the time-varying text, and the second module fully captures the temporal pattern of a rumor by a recurrent network structure. Moreover, a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

ABSTRACT: The aim of the study was to analyze the aspects affecting broiler welfare with the use of Text Mining technique. This approach converts text into numerical data and analyzes word frequency distributions, enabling the extraction of useful information and the identification of relationships between elements. Text mining has limitations, i.e. ambiguity and context sensitivity, making it difficult to capture nuanced meanings. The search was carried out with Scopus using the term “Welfare” with the keywords “Chicken”, “Broiler”, “Broiler chicken”, and “Chicken meat”, to create a database of abstracts. Text Mining and Topic Analysis were performed on the abstracts (1228 documents) using the Software R 4.3.1., analyzing also the weight of bigram and trigram. Publications on broiler welfare are present in the bibliography since 1990′s, but in the last 10 years, for the interest of public opinion, the numbers of publications significantly increased (76.5% of all documents published). USA, Brazil, and Europe-27 published 60% of the documents found. The works were published in a high number of journals, but 37% of them are published in only 4 journals (Poultry Science, Animals, Applied Animal Behavior Science and Animal Welfare). Text Mining analysis identified key terms related to the slaughter phase, housing management, and environmental conditions such as light quality and quantity. Moreover, a high correlation was found between some terms, underlying the importance of the effects of rearing, slaughter phases and litter management on broiler welfare. Most of the countries focused their research on some specific topics identified by Topic Analysis, mainly genetic selection, feeding, stocking density, slaughter, and consumer perceptions. Poultry Science published the highest number of papers (18%) and the topics more investigated were growing performance, transport and slaughter, and litter management. In conclusion, the high number of publications on chicken welfare underlines the importance of broiler welfare both in Europe and in other countries, even if it is difficult to identify common research topics among the geographic areas and the evolution over the time.

Nowadays, technology has played a vital role in the advancement of the healthcare sector. Various healthcare datasets are available on the web in the form of patents, scientific papers, articles, textual feedback, chatlogs, abstracts of papers, medical reports, and social media posts. It is a tedious task for the stakeholders to find hidden crucial knowledge on the discussed topic from this content, which if utilized optimally can lead to the rapid development of the healthcare sector. Topic analysis concepts are very effective in extracting meaningful topics from the data. Here, frequently applied Topic modeling methods-Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Correlated Topics Model, and Nonnegative matrix factorization are surveyed along with their benefits and drawbacks. Insights on new innovative topic modeling techniques used in healthcare with their objective, opportunities, and challenges are provided, which can help the researchers for the enhancement of healthcare facilities. [ABSTRACT FROM AUTHOR]

In an era of rapid technological evolution and competitive pressures, Total Quality Management (TQM) requires up-to-date literature reviews to reflect its evolving nature. This study addresses this need through a comprehensive analysis of TQM&BE journal articles from 2020 to 2023 and by introducing the LitRev-GPT framework. The research data was collected from the Scopus database, specifically targeting articles published in the TQM&BE journal from 2020 to 2023. This LitRev-GPT framework employs the Generative Pretrained Transformer (GPT) for efficient categorization and summarization of academic papers, setting new standards for reproducibility and methodological soundness. We identified emergent themes such as 'Corporate Social Responsibility', intertwining TQM with ethical practices, and 'Industry 4.0', showcasing TQM's adaptability to technological advancements. Additionally, trend analysis highlighted a sustained interest in foundational TQM themes, with a growing emphasis on innovation management. The LitRev-GPT framework demonstrates significant methodological advancements, enhancing the efficiency and depth of literature reviews beyond traditional AI capabilities. [ABSTRACT FROM AUTHOR]

Time theft, especially with the shift to remote work during the pandemic, is an increasing challenge for organizations. Existing studies demonstrate that both authoritarian leadership and laissez-faire leadership can exacerbate time theft, putting leaders in a behavioral dilemma of neither being strict nor lenient. Additionally, the pervasive and covert nature of time theft diminishes the effectiveness of subsequent corrective actions. Our study aims to investigate how to prevent time theft by mitigating employees' inclinations. Based on role theory, our study examines whether supervisor developmental feedback can encourage employees to perform work roles more appropriately. To uncover the complicated internalization process of role expectation, our study incorporates perceived insider status and work passion as serial mediators and considers the boundary effect of leaders' word–deed consistency. In Study 1, a survey of 402 employees revealed that supervisor developmental feedback can negatively predict employee time theft through employees' perceived insider status and work passion. Study 2 employs the same sample to further identify three topics of supervisor developmental feedback: skill learning, attitude learning, and social learning. Moreover, serial multiple mediating effects are affirmed across topics. The findings suggest that providing feedback on employees' learning and growth is an effective approach to prevent time theft. [ABSTRACT FROM AUTHOR]

This study aimed to identify the interaction actors, experiences, and results of tourists during their travels. Latent Dirichlet Allocation theme analysis was used to identify different themes in 9254 TripAdvisor items of user-generated content (UGC) and 15600 items of Sina Weibo UGC. A content analysis of 2000 UGC items was conducted using NVivo 11 software to uncover the details of tourist interactions. The study results showed that tourists preferred to share reviews on TripAdvisor and emotions on Sina Weibo. Seven types of tourist interaction actors were found: city, attractions, natural environment, companions, other tourists, residents, and service personnel. Seven emotions were generated in positive or negative interactions: joy, happiness, missing, awe, belonging, disappointment, and anger. Our findings provide insights for tourism managers and marketeers. [ABSTRACT FROM AUTHOR]

We evaluated the research trends in ergonomics, industrial safety and health from the 1980s to the present. In the ergonomics area, keywords and abstracts from five journals were analyzed. For industrial safety and health, six journal databases were evaluated. A frequency analysis, a semantic network of keywords and a topic network of abstracts were conducted. The results of ergonomics showed that 'macro-ergonomics' and 'manual material handling' were the most popular topics, and 'ergonomic' and 'electromyography' were the most cited keywords. 'Posture' and 'biomechanics' were the most frequently used with high centrality. The results of industrial safety and health showed that 'job stress' and 'organizational safety' were the most popular topics, and 'occupational exposure' and 'occupational health' were the most cited keywords. 'Dust' and 'exposure' were frequently used with high centrality. The results would be helpful in understanding the trends of research efforts and foreseeing trends of future research. [ABSTRACT FROM AUTHOR]

How to explore fine-grained but meaningful information from the massive amount of social media data is critical but challenging. To address this challenge, we propose the TopicBubbler, a visual analytics system that supports the cross-level fine-grained exploration of social media data. To achieve the goal of cross-level fine-grained exploration, we propose a new workflow. Under the procedure of the workflow, we construct the fine-grained exploration view through the design of bubble-based word clouds. Each bubble contains two rings that can display information through different levels, and recommends six keywords computed by different algorithms. The view supports users collecting information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations. To enable the users to explore the temporal information and the hierarchical structure, we also construct the Temporal View and Hierarchical View, which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure. In addition, we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story. Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives, including event mining across levels and topics, and fine-grained mining of specific topics to capture events hidden beneath the surface.

In canine leishmaniosis (CanL), complex interactions between the parasites and the immunological background of the host influence the clinical presentation and evolution of infection and disease. Therefore, the potential use of nutraceuticals as immunomodulatory agents becomes of considerable interest. Some biological principles, mainly derived from plants and referred to as plant-derived nutraceuticals, are considered as supplementation for Leishmania spp. infection. This study provides a systematic review regarding the use of nutraceuticals as a treatment using a text mining (TM) and topic analysis (TA) approach to identify dominant topics of nutritional supplements in leishmaniosis-based research, summarize the temporal trend in topics, interpret the evolution within the last century and highlight any possible research gaps. Scopus® database was screened to select 18 records. Findings revealed an increasing trend in research records since 1994. TM identified terms with the highest weighted frequency and TA highlighted the main research areas, namely “Nutraceutical supports and their anti-inflammatory/antioxidant properties”, “AHCC and nucleotides in CanL”, “Vit. D3 and Leishmaniosis”, “Functional food effects and Leishmaniosis” and “Extract effects and Leishmaniosis”. Despite the existing academic interest, there are only a few studies on this issue so far, which reveals a gap in the literature that should be filled.

Copyright of Estudos em Comunicação is the property of Labcom / Universidade da Beira Interior 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.)

Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study aims to provide a research literature analysis on BE in dogs based on text mining (TM) and topic analysis (TA) identifying dominant topics, summarizing their temporal trend, and highlighting any possible research gaps. A literature search was performed utilizing the Scopus® database, employing keywords pertaining to BE to analyze papers published in English from 1990 to 2023. The investigation followed a systematic approach based on the PRISMA guidelines. A total of 86 records were selected for analysis following screening procedures and underwent descriptive statistics, TM, and TA. The findings revealed that the number of records published per year has increased in 2007 and 2021. TM identified the words with the highest term frequency-inverse document frequency (TF-IDF), and TA highlighted the main research areas, in the following order: causative agents, clinical findings and predisposing factors, case reports on endocarditis, outcomes and biomarkers, and infective endocarditis and bacterial isolation. The study confirms the increasing interest in BE but shows where further studies are needed.

Due to Covid-19 pandemic, one of the biggest crises occurred in the field of education. The number of students whose educational activities were interrupted because of the pandemic is an indicator of the effects of the pandemic on education. However, when the studies in the literature are examined, they mostly focus on the field of health. Studies in the field of education are relatively limited and the studies or data are unstructured. As a matter of fact, it is important to structure the data in the world created by data; to understand how the process works, to discover opportunities and the patterns that make up the world, to predict what will happen in new situations. In this context, the purpose of this study is to determine which issues are examined in the field of education in Turkey and around the world in the Covid-19 pandemic. In accordance with this purpose, analyzes were made using text mining techniques on text-based data. As a result of the research, in the international literature, more in-depth and theoretical research is included in the context of education, while in the national literature, situation determination and superficial distance education studies have been carried out.

Piano performance represents a refined artistic expression characterized by its expressive and emotive qualities. This study focuses on research papers related to piano performance indexed in the Chinese literature database, utilizing the CiteSpace visualization tool to conduct an analysis. Employing the tool’s capabilities for word frequency, thematic, and cluster analysis, we investigate the temporal and spatial distributions, key terms, research trends, and emergent themes in the literature on piano performance. This multifaceted approach enables a deeper understanding of the developmental stages and overarching trends in the field. Our findings reveal significant shifts in thematic focus and research intensity, with notable increases in themes related to the transformation of piano performance and theoretical exploration post-2017.50, achieving an impact score exceeding 0.425. Prominent emergent keywords include “piano art” (4.9948), “piano certificate” (3.8833), “style” (3.4721), “accompaniment” (3.3155), “piano technique” (2.8888), “piano teaching” (2.6997), “emotional expression” (2.6129), and “intelligence” (2.2193). These findings indicate a trend towards the integration of modern technology and intellectual approaches in piano performance. This study provides a comprehensive overview of the evolution and current trends in piano performance research, offering insights into its future trajectory.

The growth of online health communities and socially generated health-related content has the potential to provide considerable value for patients and healthcare providers alike. For example, members of the public can acquire medical knowledge and interact with others online. However, the volume of information—and the consequent 'noise' associated with large data volumes—can create difficulties for users. In this paper, we present a data-driven approach to better understand these data from multiple stakeholder perspectives. We utilise three techniques—sentiment analysis, content analysis, and topic analysis—to analyse user-generated medical content related to Lyme disease. We use a supervised feature-based model to identify sentiments, content analysis to identify concepts that predominate, and latent Dirichlet allocation strategy as an unsupervised generative model to identify topics represented in the discourse. We validate that applying three different analytic methods highlights differing aspects of the information different stakeholders will be interested in based on the goals of different stakeholders, expert opinion, and comparison with patient information leaflets. [ABSTRACT FROM AUTHOR]

Eating disorders (EDs) are a growing concern affecting millions worldwide. Early detection and treatment are crucial, but stigma can prevent people from seeking help. Primary care providers can play a critical role in early detection by coordinating care with other professionals. Understanding the research landscape on EDs, primary care, and stigma is essential for identifying knowledge gaps to direct future research and improve management. In this study, we aimed to analyze the scientific trends and patterns in research about EDs, primary care, and stigma. A bibliometric analysis was conducted using the Web of Science database to collect articles published between May 1986 and May 2023. Bibliometric indicators were utilized to examine authorship, collaboration patterns, and influential papers. Topic analysis was performed to identify stigma-related terms within the dataset. A total of 541 research articles were analyzed, and it was found that the average number of publications per year has increased linearly from nearly zero in 1986 to 41 in 2022. One of the study's main findings is that despite this linear increase over the years, the subject of stigma did not take a prominent place in the literature. Only a few stigma concepts could be identified with the topic analysis. The authors in the field are also interested in; screening, neurotic symptoms, training, adolescent, obesity-related conditions, and family. One-third of all publications were from 15 journals. However, only two of them were primary healthcare journals. Leading authors' collaborations were another critical finding from the network analysis. This may help to expand primary care related EDs research to end the mental health stigma. This study provides insights into the research trends and patterns regarding eating disorders, primary care, and stigma. Our findings highlight the need to address primary care's impact and stigma on EDs. The identified research gaps can guide future studies to improve the prevention, diagnosis, and treatment of eating disorders in primary care settings. [ABSTRACT FROM AUTHOR]

Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19. [ABSTRACT FROM AUTHOR]

Abstract Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers’ behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.

This article analyzes how political leaders communicate with their target audiences and examines whether they adopt a country-specific communication persona, or react to the global media-intensive environment by offering more universal communication. Politicians communicate through presentational (e.g., social media) and representational (e.g., press) outlets, and the compatibility between these outlets represents the leader's effectiveness in transmitting the desired messages to the audience. The authors of this study suggest a theoretical framework that classifies public figures' communication along two dimensions: universality (particular–universal) and media compatibility (low–high). The authors used language processing tools to study the sentiment, topic mixture, and use of pronouns by 61 global world leaders in more than 300,000 messages from the leaders' Twitter accounts and press articles. The results show a high level of universality across political leaders in sentiment, topic mixture, and pronoun usage. The media compatibility is high, with Twitter being slightly more positive. Most leaders fall within the categories of Cosmopolitan Antagonist (high universality, low media compatibility) and Global Icon (high universality, high media compatibility). Overall, the sentiment of their communications is positive. Popular topics include diplomacy, economy, corruption, and the Arab world. No significant relationship was found between the sentiment or communication topics and country characteristics. [ABSTRACT FROM AUTHOR]

Eating disorders (EDs) are a growing concern affecting millions worldwide. Early detection and treatment are crucial, but stigma can prevent people from seeking help. Primary care providers can play a critical role in early detection by coordinating care with other professionals. Understanding the research landscape on EDs, primary care, and stigma is essential for identifying knowledge gaps to direct future research and improve management. In this study, we aimed to analyze the scientific trends and patterns in research about EDs, primary care, and stigma. A bibliometric analysis was conducted using the Web of Science database to collect articles published between May 1986 and May 2023. Bibliometric indicators were utilized to examine authorship, collaboration patterns, and influential papers. Topic analysis was performed to identify stigma-related terms within the dataset. A total of 541 research articles were analyzed, and it was found that the average number of publications per year has increased linearly from nearly zero in 1986 to 41 in 2022. One of the study’s main findings is that despite this linear increase over the years, the subject of stigma did not take a prominent place in the literature. Only a few stigma concepts could be identified with the topic analysis. The authors in the field are also interested in; screening, neurotic symptoms, training, adolescent, obesity-related conditions, and family. One-third of all publications were from 15 journals. However, only two of them were primary healthcare journals. Leading authors’ collaborations were another critical finding from the network analysis. This may help to expand primary care related EDs research to end the mental health stigma. This study provides insights into the research trends and patterns regarding eating disorders, primary care, and stigma. Our findings highlight the need to address primary care’s impact and stigma on EDs. The identified research gaps can guide future studies to improve the prevention, diagnosis, and treatment of eating disorders in primary care settings.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development. [ABSTRACT FROM AUTHOR]

Regulatory sentiment refers to the market’s subjective evaluation of regulatory reform and is one of the most widely adopted indicators to those charged with implementing and diagnosing regulatory policies. The use of regulatory sentiment in advanced analysis has become universal, albeit it is often limited due to difficulties in articulating consistent and objective quantitative indicators that can meticulously reflect market sentiment overall. Thus, despite ample effort by scholars to read the economic impact of regulatory sentiment in the real economy, causal links are difficult to spot. To fill this gap in the literature, this study analyzes a regulatory sentiment index and economic performance indicators through a text analysis approach and by inspecting diverse tones in media articles. Using different stages of tests, the paper identifies a causal relationship between regulatory sentiment and actual economic activities as measured by private consumption, facility investment, construction investment, gross domestic investment, and employment. Additionally, as a result of analyzing one-unit impulse of regulatory perception, the initial impact on economic growth and private investment was found to be negligible; this was followed by a positive (+) response, after which it converged to zero. Construction investment showed a positive (+) response initially, which then rapidly changed to a negative (-) response and then converged to zero. Gross domestic investment as the initial effect was negligible after showing a positive (+) reaction. Unfortunately, the facility investment outcome was found to be insignificant in the impulse response test. Nevertheless, it can be concluded that it is necessary and important to increase the sensitivity to regulations to promote the economic effectiveness of regulatory reforms. Thus, instead of dealing with policies with the vague goal of merely improving regulatory sentiment, using regulatory sentiment as an indicator of major policies could be an effective approach.