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Blockchain (BC) applications in supply chain management (SCM) have recently received extensive attention. It is important to synthesise the extant literature on the field to identify key research themes and navigate potential future directions. This study thus develops an efficient, scalable data-driven review approach that uses text mining and Latent Dirichlet Allocation (LDA)-based topic modelling for automatic content analysis of full-text documents. Our method overcomes the drawbacks of traditional systematic literature reviews using either manual coding or bibliographic analysis for article classifications, which are highly time-consuming and biased when dealing with large amounts of text. 108 papers published between 2017 and 2022 were analysed which identified 10 key research themes, including revenue management, sustainability, traceability, manufacturing system, scheduling in cloud manufacturing, healthcare SCM, anti-counterfeit system, logistics and transportation, system architecture development, and food & agriculture SC. Five future directions are then suggested, including (1) integration of BC and other emerging technologies for global and scalable SCM, (2) crypto-X applications in SCM, (3) BC-enabled closed-loop SCM, (4) the environmental and social impacts of BC-based SCM and (5) decentralised autonomous organisations in SCM. [ABSTRACT FROM AUTHOR]

To what extent political parties engage in debates about the same issues and how they respond to each other is highly relevant to democratic processes. Existing research on issue engagement has uncovered several interesting patterns and factors, but has neglected one important feature of contemporary democracies: nowadays, political actors have a wide range of communication channels at their disposal with the use of ‘newer’ forms of political communication (e.g., social media) potentially transforming discursive power relations between political actors. However, it remains largely unclear whether the extent and nature of issue engagement varies between more ‘traditional’ and ‘newer’ forms of political communication. To fill this gap, I apply unsupervised topic modelling to press releases and tweets from political parties in Austria, Germany and Switzerland (January 2019–September 2021). The statistical analysis shows substantial differences in issue engagement between political parties in press releases and on Twitter, now X. First, I find a higher likelihood of issue engagement between parties in tweets. Second, Twitter appears to moderate the influence of party‐level factors on issue engagement compared to press releases. The results show that for issue engagement in tweets, the importance of party size is smaller and the role of government parties is larger than in press releases, while the role of ideological distance does not seem to change. These findings add important insights to our understanding of the potential transformative effect of new communication technologies on party competition and political discourse. [ABSTRACT FROM AUTHOR]

The media are under increasing pressure by states to adopt a regime-friendly editorial line in many countries globally, raising the importance of understanding the conditions of successful and failed media capture. Unfortunately, the standard measurement of media capture – annual expert surveys – neither distinguishes between outlets nor offers the needed resolution to assess, e.g., the immediate impact of a new gag law. To remedy these shortcomings, we develop a computational measurement of media capture based on a comparison of nominally independent outlets and regime-owned outlets regarding media agenda and tone when referring to the regime. By relying on two unsupervised measurement methods – topic modelling and sentiment classification – the new method can assess the loss of editorial independence at the level of individual outlets on a monthly or even weekly basis. The methodology is validated by applying it to recent data from Nicaragua. There the two-pronged approach shows how outlets respond differently to regime pressure. While there still are limitations to consider, if further editorial aspects of media capture such as issue framing are added to the methodology, it has the potential of transforming the future study of media capture. [ABSTRACT FROM AUTHOR]

Public administration in the recent years has become a very dynamic and vast domain owing to evolving challenges. Our study aims to comparatively evaluate the changing discourse in public administration research in general and 'Public Administration and Development' (PAD) in particular. For this purpose, we have taken research articles from nine journals of public administration domain for the period of 2010–2022. We have used the text‐mining based approach of Dynamic Topic Modelling to understand the evolution of topics in public administration research. This is followed by the Named Entity Recognition (NER) to discover the geographic importance given in public administration research vis‐à‐vis PAD. We have used the measure of 'Jaccard similarity' to compare the changing discourse of PAD with respect to other journals. We have found that topics on approaches to governance, organization performance and health were always on the priority in literature. We have also found that topic coverage in PAD is more diverse and niche compared to the overall trends in public administration research. NER findings suggest that public administration research is more Euro‐North American centric while PAD has pan‐continental presence with focus on developing countries. [ABSTRACT FROM AUTHOR]

Objective: This study aims to analyse the content of internet medical policies, grasp the inherent laws of the development of internet medical policies and provide references for improving the policy system. Methods: On the basis of web crawler technology, 436 internet medical policies issued by the central and local governments after the Healthy China Strategy was proposed were collected. The BERTopic model was used to extract topics, and a comprehensive analysis of China's internet medical policy texts was conducted through the analysis of topic content, topic hierarchy and topic clusters. Results: A total of 27 topics were identified in China's internet medical policies, mainly focussing on five topic clusters: medical service regulation, elderly nursing and children's healthcare using traditional Chinese medicine, user safety guarantees, health education and communication and infectious disease treatment and recovery guidance. Conclusions: Different topic contents play a role in promoting the comprehensive and standardized development of internet medical services. However, there is still room for further improvement in policy integrity, consideration of multiple types of users, and implementation effectiveness. Continuous efforts are needed to optimize the effectiveness of policies. [ABSTRACT FROM AUTHOR]

In the burgeoning field of wearable technology, Fitbit has emerged as an influential entity. Even the most influential companies, however, need to understand user dissatisfaction, particularly extracted from critical reviews, in order to enhance the user experience. This study analyzes a subset of 28,664 Fitbit consumer reviews from the Google Store, all giving it a rating of 1 out of 5 points, in order to identify the reasons for user dissatisfaction. Employing thematic analysis, sentiment analysis, and topic modelling, our research reveals prominent issues impacting user perception. Key findings include significant challenges with device syncing, app functionality, and customer service, alongside a spectrum of sentiments ranging from neutral to negative. Topic modelling further distinguishes various categories of user feedback, underscoring the diversity in user experiences and expectations. These insights suggest a pressing need for improvements in technical functionality, user interface design, and customer support. This study contributes to a deeper understanding of consumer experiences in wearable technology, offering valuable guidance for enhancing product design and customer engagement strategies in this dynamic market. [ABSTRACT FROM AUTHOR]

Hiking tourism is an essential means of destination development. A holistic understanding of the hiking experience could benefit destination management. This study adopted topic modelling and an online review-based approach to determine the key attributes of hiking tourism in the context of seasonality. Data were collected from TripAdvisor and a comprehensive list of the attributes, discussed by hikers when visiting Dragon's Back Trail in Hong Kong, is constructed and categorized into five aspects. Insights into the hikers' interests and concerns about the experience attributes across seasons are revealed. The results show that the concerns for some attributes are more sensitive in specific seasons compared with other attributes. Precisely, this study found that seasonality has heterogeneous effects on hikers' experience. The results are beneficial to support local hiking tourism marketing and hedging strategies for different seasons. [ABSTRACT FROM AUTHOR]

Climate change is a paramount problem for humanity, representing a substantial danger to all living organisms. Industrialization, a vital factor for economic progress, has resulted in global warming, posing a threat to the long-term viability of our ecosystem. Currently, a wide range of techniques and technologies are being used to guarantee the preservation of the environment for future generations. This study employed data from the Scopus database to do topic modeling. Authors used latent Dirichlet allocation to extract research themes related to environmental sustainability from a corpus of 4023 research articles published between 1976 and 2022. By utilizing clustering methodologies to analyze the collection of words, Authors successfully forecasted two, five, and ten study subjects, emphasizing specific domains that necessitate additional investigation by scholars. Based on coherence ratings, five subjects have been identified as prospective study areas requiring further scientific exploration. The results of our research emphasize the significance of incorporating environmentally-friendly technologies in different industries to promote a long-lasting and eco-friendly ecosystem. In addition, authors recommend prioritizing implementing sustainable and environmentally friendly technologies, improving the management of ecosystems, encouraging water conservation, promoting agricultural advancements, and advancing renewable energy resources as crucial strategies for protecting the environment and enhancing ecological conditions. This analysis illuminates current research trends in environmental sustainability and potential pathways for future investigation and intervention. [ABSTRACT FROM AUTHOR]

Public libraries and public spheres have been widely studied over the past three decades. In recent years, several studies have comprehensively reviewed past research results to reveal the main issues, concerns, and research topics in the field of public libraries and public spheres. These studies have elucidated that the research theme of public libraries as a 'meeting place' has received increasing attention. They also noted the need for quantitative approaches in this field. Addressing this requirement, we conducted quantitative topic modelling using latent Dirichlet allocation and established an overview of research topics in the literature related to public libraries as public spheres. The following four periods emerged: first period (1993–2003), second period (2004–2010), third period (2011–2015), and fourth period (2016–2022). By analysing a cross-section of research topics from all periods, we elucidated the transitions of research themes developed in the research field. In addition, our comparison of topic model maps and topics over the years revealed that research topics appear to have become increasingly complex. Research topics on meeting places in public libraries have emerged continually and changed over time, covering (1) places for individual civic group activities, (2) public spaces where patrons face diversity and pluralism, (3) community spaces where patrons foster social trust, and (4) social spaces where patrons build social capital in the community. This result indicates that the research on public libraries as public spheres has gradually developed based on these venues being meeting places and will continue to progress. [ABSTRACT FROM AUTHOR]

The learning quality of humans depends on the sense of contemplation. Textual documents are a huge part of the literature on contemplation which effortlessly creates perception. Automatic document class identification or organisation is a machine learning function to understand the psychological and emotional content of the text in a concise way. The problem of identification of documents falls in the field of library science, information science and artificial intelligence. The research progress of class identification of documents has been made in various most spoken languages. Numerous research works have been published in European and Asian languages. However, there is a gap in the literature when it comes to any less resource language, especially Bengali. Consequently, this work portrays an efficient topic modelling approach for Bengali document class identification. It proposes a Dirichlet-polynomial clustering model likelihood corpus distribution (LCD), which is based on a Bayesian numerical prototype. Experiments are done to prove the efficiency of LCD over various topic modelling algorithms, such as latent Dirichlet allocation (LDA), LDA with bag-of-words (LDA-BOW), latent semantic indexing (LSI), and hierarchical Dirichlet process (HDP). For performance evaluation, we considered five real-world datasets of Bengali corpora, such as science, sports, computer, season, and epic in this work. The coherence score of different modelling algorithms is compared to find the best model for each dataset separately. [ABSTRACT FROM AUTHOR]

Background: Current outcome measures in digital mental health lack granularity, especially for single‐session interventions. This study aimed to address this by utilising natural language processing (NLP) methods to create a clear and relevant outcome measure. This paper describes the development of the Adult Session Wants and Needs Outcome Measure (Adult SWAN‐OM), a novel outcome measure for the Qwell digital mental healthcare platform to understand service user (SU) needs engaging in single‐session therapy (SST). Methods: The research employs a multi‐phased approach combining NLP methods with the typical stages of outcome measures development as follows: (1) assumption definition and validation with SUs and clinicians; (2) transcript theme extraction using the RoBERTa large language model (LLM) in conjunction with topic modelling to extract themes from 254 single‐session transcripts from 192 SUs; (3) clinical item refinement focus group; (4) content validity with clinicians and SUs to improve the relevance and clarity of the items; and (5) outcome measure finalisation in a workshop held with clinicians to consolidate the final wording. Results: Ninety‐six potential wants and needs were generated and distilled into 12 measure items. The outcome measure was shown to be relevant and clear to both SUs and clinicians when used in the context of SST. Conclusion: This study highlights the potential of combining NLP approaches with co‐creation methods in single‐session outcome measure development. We argue that the incorporation of clinical expertise and SU experience ensures the clarity and applicability of such measures and that this approach to capturing single‐session wants and needs promises novel insights for supporting digital mental health interventions. [ABSTRACT FROM AUTHOR]

Scientific cooperation is gaining importance as worldwide trends in co-authorship indicate. While clustering is an established method in this field and several have studied scientific-cooperation dynamics within a single discipline, little literature exists on its interdisciplinary facet. This paper analyses the evolution of co-authorship amongst social scientists in Slovenia over the three decades between 1991 and 2020 using bibliographic databases. The identification of groups (clusters) of authors based on patterns in their co-authorship ties both within and across decades is carried out using network-analytical method called stochastic blockmodeling (SBM). Meanwhile, previous research used generalised blockmodeling accounting only for within-period ties. Additionally, a topic model is developed to tentatively assess whether co-authorship is driven by research interests, organisational or disciplinary affiliation. Notably, while focusing on the result of the SBM for generalised multipartite networks, the paper draw compares with other SBMs. Generally, the paper identifies clusters of authors that are larger and less cohesive than those found in previous works. Specifically, there are three main findings. First, disciplines appear to become less important over time. Second, institutions remain central, corroborating the suggestion that Slovenian R&D policy reinforces parochial research practices. Yet, whether organisational segregation is an issue remains unclear. Third, interdisciplinarity's emergence has been slow and partial, thus supporting the idea of a 'covert interdisciplinarity.' Importantly, it seems that members of different clusters lack fluency in a meta-language enabling effective communication across cognate paradigms. And this may hinder the implementation of long-term, up-to-date research policies in the country. [ABSTRACT FROM AUTHOR]

The contemporary world is marked by generation and consumption of vast volume, high velocity, and considerable diverse data, leading us to the concept of big data. In this study, a system design thinking approach was employed to identify the requirements of Iran's public sector big data system. National big data systems would help governments to support their decisions by data and answer to national problems faster. Given the complexity and time-intensive nature of traditional system requirement analysis methods, their practical application in the industry has been declined. Therefore, in this research, system design thinking as an agile alternative for identifying system requirements has been discussed. To accomplish this, the LDA machine learning method has been utilized to analyze approximately 88,000 articles, a thematic analysis on around 600 Instagram and Twitter posts has been conducted, and six experts representing targeted problem persona were interviewed. The objective of this research is to extract insights to serve as a foundation for formulating big data policies in Iran. Findings reveal that Iran big data system requirements can be classified into four categories which indicate on increasing managed access to data while considering security and privacy, encouraging private and public sectors cooperation, transformation to smart governance, and establishing national data organization which would be responsible of data ID documents. [ABSTRACT FROM AUTHOR]

Journalism has emerged as a crucial avenue for creating awareness of the increasing importance of addressing environmental risks that transcend national boundaries. This study examines the Guardian newspaper's coverage of environmental issues in Bangladesh, a country profoundly affected by climate change and natural disasters. Through analysing 345 news articles from 2011 to 2021, this research employs topic modelling to explore the international framing of climate concerns. The findings reveal thematic framing across various topics, including occupational environment, health and safety in the ready-made garment (RMG) industry, flooding along transboundary rivers, and Bangladesh's stance on the Paris Agreement and its approach to climate change. The Guardian emphasises ethical and sustainable practices in the fashion industry. It sheds light on the impact of flooding on vulnerable communities and discusses Bangladesh's energy policies in the context of global climate goals. Nevertheless, the study also highlights some limitations, such as the absence of comprehensive coverage of Bangladesh's diplomatic efforts at global climate forums and a lack of emphasis on developed nations' responsibility. [ABSTRACT FROM AUTHOR]

Special teams (i.e. power play and penalty kill) situations play an outsized role in determining the outcome of ice hockey games. Yet, quantitative methods for characterizing special teams tactics are limited. This work focuses on team structure and player deployment during in-zone special teams possessions. Leveraging player and puck tracking data from the National Hockey League (NHL), a framework is developed for describing player positioning during 5-on-4 power play and 4-on-5 penalty kill possessions. More specifically, player roles are defined directly from the player tracking data using non-negative matrix factorization, and every player is allocated a unique role at every frame of tracking data by solving a linear assignment problem. Team formations naturally arise through the combination of roles occupied in a frame. Roles that vary on a per-frame basis allow for a fine-grained analysis of team structure. This property of the roles-based representation is used to group together similar power play possessions using latent Dirichlet allocation, a topic modelling technique. The concept of assignments, which remain constant over an entire possession, is also introduced. Assignments provide a more stable measure of player positioning, which may be preferable when assessing deployment over longer periods of time. [ABSTRACT FROM AUTHOR]

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Access to justice is a significant area of legal research, especially for Socio-Legal studies. The main research topics of this area are economic or class differences, gender inequalities, or national and ethnic differences in access to justice. However, there is a less discussed aspect of access to justice: the differences between access to legal information and the differences in user groups in terms of comprehending and processing legal information. This is an important topic because there are significant differences among people's abilities to process and understand legal texts, depending on whether we are dealing with a lawyer who is an expert in the given field, a non-expert lawyer, or a citizen with a low or zero (legal) educational level. The paper argues that unsupervised machine learning solutions can help even out these differences. It presents different unsupervised solutions, mainly clustering and topic modelling, which can help to increase access to legal information. Then we present a case study in which we examine these unsupervised tools in the processing of resolutions of the Central Bank in Hungary and anonymized court decisions. The paper argues that these tools can reveal the hidden contextual regularities in unstructured legal texts, facilitating the search for legal texts even for non-legal-experts. [ABSTRACT FROM AUTHOR]

This research aims to study the psycho-social impact of video games on K12 students. For this, a probabilistic topic modelling analysis method based on text mining approach has been performed. This process is based on nodes' connectivity and it has been developed through K Means approach; by launching the Jenks-Breaks algorithm. The sex differences are calculated according to a nonlinear dynamics approach based on Hurst exponent and multifractal function and the influence of time with the application of the Sobel test. The results show which are the most used video games by K12 and their psycho-social impact on students based on four categories: (1) boredom and sadness, (2) happiness and socialization, (3) immersion, and (4) families' conflicts. There are significant differences between boys and girls depending on the games they use, a factor that increases when playing more than two hours a day. For boys, games like FIFA and Fortnite produce higher levels of immersion and family conflict, while for girls, games are perceived as sources of greater happiness and a means to reinforce friendship and camaraderie, particularly with games like Brawl Stars, Rocket League, and PKXD. [ABSTRACT FROM AUTHOR]

The ability of the system to answer the searched formal queries has become active research in recent times. However, for the wide range of data, the answer retrieval process has become complicated, which results from the irrelevant answers to the questions. Hence, the main objective of the current article is a Topic modelling Question Answering (QA) system for the web-based Job posting data. Therefore, a novel Dove-based Alex net Gibbs Modeling (DbAGM) has been implemented for accurate answer retrieval and topic modelling for web-based questions in job assistance. The initialized job post data are filtered to remove the noise, and question features are extracted at the feature analysis phase. Furthermore, in the QA module, the extracted features are matched with the trained features set to retrieve the relevant answers for the searched web-based Question. Furthermore, the Topic is selected for the displayed answer keywords. The designed system is executed in the Python platform, and performance metrics are calculated. The proposed Model gained an accuracy rate of 95.6%, which is quite better than traditional models. It proved the suitability of the implemented Model for the specific Topic modelling QA system. [ABSTRACT FROM AUTHOR]

Within the printed and online newspaper media in the UK, notions of 'decolonisation' referring to various contexts, such as historical, relating to museums and institutions, cultural decolonisation and in relation to modern independence discussions, can be traced. In this article, we have applied topic modelling and natural language processing methods to carry out a classification of, and sentiment analysis on, newspaper headlines and texts from leading British newspapers covering decolonisation over the past decade. The results show an abrupt change in the meaning of decolonisation starting in the middle of the 2010s with an increased focus on cultural and institutional matters, particularly in right-leaning media. Surprisingly, the editorial slant of broadsheets seemingly only had at best moderate effects on tone, while headlines in right-wing tabloids were significantly more negative. Articles covering cultural aspects of decolonisation were substantially more negative than those applying a more traditional, territorial definition of decolonisation. Given the influence of newspaper media on public and private opinions, we discuss the heritage implications of these findings and suggest avenues for further investigation. [ABSTRACT FROM AUTHOR]

Purpose: There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications. Design/methodology/approach: In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development. Findings: The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building. Originality/value: This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies. [ABSTRACT FROM AUTHOR]

The contemporary world is marked by generation and consumption of vast volume, high velocity, and considerable diverse data, leading us to the concept of big data. In this study, a system design thinking approach was employed to identify the requirements of Iran's public sector big data system. National big data systems would help governments to support their decisions by data and answer to national problems faster. Given the complexity and time-intensive nature of traditional system requirement analysis methods, their practical application in the industry has been declined. Therefore, in this research, system design thinking as an agile alternative for identifying system requirements has been discussed. To accomplish this, the LDA machine learning method has been utilized to analyze approximately 88,000 articles, a thematic analysis on around 600 Instagram and Twitter posts has been conducted, and six experts representing targeted problem persona were interviewed. The objective of this research is to extract insights to serve as a foundation for formulating big data policies in Iran. Findings reveal that Iran big data system requirements can be classified into four categories which indicate on increasing managed access to data while considering security and privacy, encouraging private and public sectors cooperation, transformation to smart governance, and establishing national data organization which would be responsible of data ID documents.

This study examines the landscape of Russian International Relations (IR) scholarship, offering a quantitative analysis of thematic trends in Russian IR publications using data from the OpenAlex bibliographic database. We employed Structural Topic Modeling (STM) on 13,705 articles published between January 2000 and May 2024, ensuring methodological rigor through language standardization, text preprocessing, and exclusion of irrelevant texts. While prior research on Russian IR has mostly been descriptive, often focusing on typologies or prescriptive arguments, our study uncovers several underexplored attributes. Notably, Soviet-era legacies persist thematically rather than paradigmatically, and developmental issues such as inequality and justice are disproportionately represented. The systemic approach remains dominant, with Russian scholarship striving to integrate regional perspectives into the global context. Traditional focuses on Russian foreign policy and its great-power status persist, with a shift from identity issues to reinforcing this status. Despite strained relations with Europe, it remains a key focus in Russian works. Interestingly, topics on education and culture now surpass those on conflict and security, although this may be influenced by data characteristics or OpenAlex indexing.

Abstract Objective This study aims to analyse the content of internet medical policies, grasp the inherent laws of the development of internet medical policies and provide references for improving the policy system. Methods On the basis of web crawler technology, 436 internet medical policies issued by the central and local governments after the Healthy China Strategy was proposed were collected. The BERTopic model was used to extract topics, and a comprehensive analysis of China’s internet medical policy texts was conducted through the analysis of topic content, topic hierarchy and topic clusters. Results A total of 27 topics were identified in China’s internet medical policies, mainly focussing on five topic clusters: medical service regulation, elderly nursing and children’s healthcare using traditional Chinese medicine, user safety guarantees, health education and communication and infectious disease treatment and recovery guidance. Conclusions Different topic contents play a role in promoting the comprehensive and standardized development of internet medical services. However, there is still room for further improvement in policy integrity, consideration of multiple types of users, and implementation effectiveness. Continuous efforts are needed to optimize the effectiveness of policies.

Abstract Climate change is a paramount problem for humanity, representing a substantial danger to all living organisms. Industrialization, a vital factor for economic progress, has resulted in global warming, posing a threat to the long-term viability of our ecosystem. Currently, a wide range of techniques and technologies are being used to guarantee the preservation of the environment for future generations. This study employed data from the Scopus database to do topic modeling. Authors used latent Dirichlet allocation to extract research themes related to environmental sustainability from a corpus of 4023 research articles published between 1976 and 2022. By utilizing clustering methodologies to analyze the collection of words, Authors successfully forecasted two, five, and ten study subjects, emphasizing specific domains that necessitate additional investigation by scholars. Based on coherence ratings, five subjects have been identified as prospective study areas requiring further scientific exploration. The results of our research emphasize the significance of incorporating environmentally-friendly technologies in different industries to promote a long-lasting and eco-friendly ecosystem. In addition, authors recommend prioritizing implementing sustainable and environmentally friendly technologies, improving the management of ecosystems, encouraging water conservation, promoting agricultural advancements, and advancing renewable energy resources as crucial strategies for protecting the environment and enhancing ecological conditions. This analysis illuminates current research trends in environmental sustainability and potential pathways for future investigation and intervention.

Abstract This research aims to study the psycho-social impact of video games on K12 students. For this, a probabilistic topic modelling analysis method based on text mining approach has been performed. This process is based on nodes’ connectivity and it has been developed through K Means approach; by launching the Jenks-Breaks algorithm. The sex differences are calculated according to a nonlinear dynamics approach based on Hurst exponent and multifractal function and the influence of time with the application of the Sobel test. The results show which are the most used video games by K12 and their psycho-social impact on students based on four categories: (1) boredom and sadness, (2) happiness and socialization, (3) immersion, and (4) families' conflicts. There are significant differences between boys and girls depending on the games they use, a factor that increases when playing more than two hours a day. For boys, games like FIFA and Fortnite produce higher levels of immersion and family conflict, while for girls, games are perceived as sources of greater happiness and a means to reinforce friendship and camaraderie, particularly with games like Brawl Stars, Rocket League, and PKXD.

Industrial manufacturing is a core component of a nation's economic wealth. It is driven by technology, and its paradigm has shifted from craft production to mass production to lean production to mass customisation (MC). We identify how emerging technologies offer a new way to shift MC research focusing on the scientific communities that have been driving MC research, changes in MC throughout the years, and the technological advances that influence future research trends of MC. We then identify MC paradigms. We analyse 1,947 MC papers from 1992 to 2019 and discover the research attention changes from the perspectives of the communities and themes using topic modelling. Our result indicates that MC was explained in terms of the business and marketing strategy (BMS) and the operations management (OM), in accordance with technological development. In BMS, MC topics moved from demand uncertainty to industry transformation. In OM, topics changed from assembly line for product differentiation to additive manufacturing. We discuss the future research agenda according to the technological advances in computer-aided design, additive manufacturing, machine learning, identification technologies, virtual reality, and human-robot collaboration. Not only researchers but practitioners and policymakers can utilise our approach to analyse and formulate MC strategies considering new technologies. [ABSTRACT FROM AUTHOR]

Despite a long research tradition of investigating the relationship between organisational culture and performance, the findings remain inconclusive. To bridge this gap, this study proposes a research model to investigate how the opposing attributes of organisational culture variously affect employee satisfaction and how it is related to organisational performance. The job demand and resource (JD-R) model is adopted to conceptualize these attributes, and the motivation-hygiene theory is employed to explain the impact of culture on employee satisfaction. For empirical analysis, this study collected employee review data of large R&D intensive firms in the US and combined financial data. Based on a data of 295,307 reviews for 182 firms, it performed topic modelling to measure culture and regression analysis of panel data for hypothesis testing. The findings indicate that, for resourceful cultures, collaborative and innovation cultures positively affect employee satisfaction, but fair compensation culture does not. For demanding attributes, results-oriented, overwork, and job insecurity cultures are negatively related to employee satisfaction. Finally, employee satisfaction positively affects organisational performance. This research emphasizes the motivational significance of different organisational cultures in determining employee satisfaction and organisational performance. [ABSTRACT FROM AUTHOR]

This study examines the factors that impact the purchase intention of electric vehicles (EVs) in emerging economies like India. By employing the Push, Pull, and Mooring (PPM) theory, this study contributes to the existing literature by shedding light on the factors influencing purchase intention of EVs. In the first stage of the study, twitter text mining was conducted to extract the main topics discussed about EVs. The second stage involved a focus group discussion to condense the identified topics into key factors influencing the purchase intention of EVs. Finally, choice-based conjoint analysis was employed in the third phase to comprehend the utility and prioritize the identified factors. Additionally, topic modelling can be valuable for marketing managers, product managers, and branding specialists in developing a brand image and positioning for EVs. Furthermore, the findings of this study can assist policymakers in creating regulatory frameworks to support the development of EV infrastructure. [ABSTRACT FROM AUTHOR]

The popularity of econometric analyses that include variables obtained from text mining is growing rapidly. A frequently applied approach is to identify topics from large corpora, which makes it possible to determine trends that reflect the changing relevance of topics over time. We address the question of whether such topic trends are linked to quantitative economic indicators typically used for analysing the objects described by a topic. The analysis is based on academic economic articles from Poland and Germany from 1984 to 2020. There is a specific focus on whether relationships between topic trends and indicators are similar across national economies. The connection between topic trends and indicators is analysed using vector autoregressive models and Granger causality tests.

Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.

Abstract Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products (n = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.

Rising levels of hardship since the introduction of austerity have rendered essential items unaffordable for many on low incomes with young children. Baby Banks—organisations freely providing essential items and equipment to those with, or expecting, a baby or young infant—have grown. These organisations have received little academic attention and much of what is known about them comes from news media coverage. News media plays a critical role in raising public awareness, shaping public attitudes, and influencing policy formation. Drawing on 384 news articles, this paper explores the scale and nature of news article coverage of Baby Banks between 2009 and 2022 using sentiment analysis and topic modelling. Through employing these approaches, our research adds quantitative evidence to extant work on news media coverage under austerity. Our results show that the number of articles written about Baby Banks has grown since 2009, with peaks in coverage during the COVID‐19 pandemic in 2020, potentially reflecting the growing need for these organisations. While sentiment towards Baby Banks within news articles has predominantly been positive, since 2019 there has been a rise in negative coverage due to an increase in articles critical of the growing number of people requiring Baby Banks. Alongside discussions of political changes driving rising hardship, prominent underpinning narratives in articles have included charitable appeals for donations, praise for community work, the royal family, and mothers. By focusing on the sentiment and key discussions around Baby Banks, our research extends understandings of media coverage under austerity. [ABSTRACT FROM AUTHOR]

Using machine learning, we examined customers’ opinions about the metaverse in the hospitality industry (encompassing hotels, restaurant, gaming, virtual events, tours and travel). A total of 8,855 tweets were collected from Twitter (now called X), and machine learning algorithms such as sentiment analysis and topic modeling were performed using Python libraries to capture the important topics related to metaverse applications. Nearly two thirds of the collected tweets (60.9%) contained a mostly positive general sentiment toward the use of the metaverse. Six important topics emerged from the topic modeling: gaming, virtual events, virtual sightseeing, travel, business and blockchain. Despite numerous studies on the proper integration of the metaverse, VR and AR, to the best of our knowledge, this is one of the first studies conducted to determine the customer experience of the metaverse in the hospitality industry using social media data. [ABSTRACT FROM AUTHOR]

Opposing social movements are groups that have conflicting objectives on a shared social justice issue. To maximize the probability of their movement's success, groups can strategically portray their group in a favourable manner while discrediting their opposition. One such approach involves the construction of victimization discourses. In this research, we combined topic modelling and critical discursive psychology to explore how opposing groups within the feminist movement used victimization as a lens to understand their movements in relation to transgender women. We compiled a dataset of over 40,000 tweets from 14 UK‐based feminist accounts that included transgender women as women (the pro‐inclusion group) and 13 accounts, that excluded transgender women (the anti‐inclusion group). Our results revealed differences in how victimization was employed by the opposing movements: pro‐inclusion groups drew on repertoires that created a sense of shared victimhood between cisgender women and transgender women, while anti‐inclusion groups invoked a competitive victimhood repertoire. Both groups also challenged and delegitimised their oppositions' constructions of feminism and victimhood. These findings add to our understanding of the communication strategies used by opposing movements to achieve their mobilization goals. [ABSTRACT FROM AUTHOR]

The legitimacy of public broadcasting has been under pressure for several decades. Attempts to repair and restore this legitimacy have led to an intensification of accountability instruments and measures. These instruments and measures tend to focus on consumption figures or cost-benefit analyses, requiring new ways of capturing public value. This article argues that, given the media's persistent role as an interpreter and multiplier of news via traditional distribution channels and diverse new platforms providing information and opinions, professional follow-up communication is a relevant source for the examination of broadcasters' contributions to public debate. The essay explores how the analysis of newspapers via computational methods can be used by public broadcasters to reflect on and demonstrate their role in public debates in contributing a diversity of topics and viewpoints. [ABSTRACT FROM AUTHOR]

The crucial part of the software development cycle is software maintenance. The demands included in the software management are fault fixes and request to change or bring a new feature. If priority is not given to these demands, then it may lead to customer dissatisfaction, inefficient planning, and software failure as well. Therefore, it is important to study the severity of the bug reports to maintain the efficiency of the software. Various research has been conducted in the past to predict the severity of the paper using text mining focusing only on the content of the bug reports. The sentiment of the user while reporting a bug also plays a vital role. In this study, we will be focusing on two aspects, that is, sentiment and content to improve the prediction. We propose a prediction model based on LDA to study the content aspect and emotion analysis to study the sentiment aspect. The model is validated on the datasets collected from the Eclipse project using Convolutional Neural Network (CNN). The results show that the CNN model effectively utilizes the content and sentiment aspect of the data to handle the severity prediction. CNN has weight sharing feature that decreases the number of parameters used for training. It also improves generalization and overfitting is avoided The Accuracy, Precision, Recall, and F‐measure are improved when both aspects are taken into account rather than considering only content. [ABSTRACT FROM AUTHOR]

The aim of this study is to comprehensively review the topics and themes studied in destination competitiveness research and to enrich tourism researchers' literature review toolbox with the computational literature review technique. A novel computational literature review framework that can be applied to review tourism research domains characterised by an abundance of literature is developed. Enabled by the developed computational literature review framework with topic modelling, this study offers a comprehensive review of 1,130 destination competitiveness studies. The results identified several promising research topics, themes and theoretical perspectives that can be perused in future destination competitiveness studies. [ABSTRACT FROM AUTHOR]

The study investigates the dissemination of news content from two Russian state-backed media outlets, RT and Sputnik, within digital alternative news environments in Austria, Denmark, Germany, and Sweden. We investigate these media outlets as parts of a Russian "sharp power" strategy, aimed at establishing informational influence within Western news environments. The analysis includes over 3.4 million social media posts containing URLs to RT and Sputnik content, published between January 2019 and March 2022. Drawing on a combined network analytical and computational text analysis approach, we examine the dissemination of RT and Sputnik content across a range of social media platforms (Twitter/X, Facebook, Telegram, VKontakte, Gab, Instagram, Reddit, 4chan, and YouTube). In the Danish and Swedish environments, the dissemination of content into core domestic alternative news communities is limited, primarily taking place on Facebook and, to a lesser extent, on Twitter/X. Fringe platforms such as Telegram, Gab and VKontakte facilitate a wider spread of content within domestic alternative news environments in German-speaking environments, predominantly among right-wing digital clusters. Within German-speaking alternative news environments, the shared content from RT and Sputnik primarily addresses domestic issues, such as COVID-19 policies and vaccines. In contrast, Danish- and Swedish-speaking alternative news environments emphasize foreign policy matters, including the Russian–Ukraine War, WikiLeaks, and Western military actions. These findings contribute to research on foreign state-media operations in digital information environments by focusing on both content and sharing patterns. [ABSTRACT FROM AUTHOR]

The Sziget Festival is one of Europe’s biggest music events, held every August on the Óbuda Island in Budapest. The elements of festival experiences were analysed through social media opinions of Sziget Festival attendees published between 1 January 2014 and 31 July 2022, using structural topic model (STM) computer algorithm and latent Dirichlet allocation (LDA). Our findings support existing literature, showing that festival experiences are cognitive-affective in nature with key elements of socialisation, environment, and festival value, some of which are inter-connected. Although this kind of research is rare in tourism, our paper argues for its usefulness and provides an example of application, using consumer data to facilitate decision-making and support the festival industry. The paper concludes with recommendations for future research, as well as incorporating LDA with other qualitative and quantitative research. [ABSTRACT FROM AUTHOR]

This study explores the diversity of topics in political campaign communication on social media during the 2022 Australian federal election. While political campaigns on social media are often associated with both persuasive and mobilising appeals, this research focuses on understanding the differences in persuasive content by comparing organic (non-targeted) and paid (targeted) political communication. Analysing the Australian context, which follows a Westminster system, with compulsory voting, we utilise data from the federal election 2022 to investigate how political actors employ persuasive communication strategies. Through topic modelling, we examine whether distinct themes vary in content and prevalence between organic and paid social media content disseminated by political parties and candidates. Our analysis revealed that the differences in topic diversity between paid and organic content do not seem to be substantial, despite popular concerns about higher personalisation due to advertising targeting which could lead to information fragmentation of the electorate. Both types of content predominantly focus on core political topics, aligning with party ideologies and include overall campaign information (e.g., on election procedures). However, government critique emerges as a distinct topic in both organic and paid content signalling the usage of negative campaigning to weaken opposing parties. In conclusion, this study suggests that the strategic manipulation of the electorate through social media during the Australian federal election in 2022 was limited. Nonetheless, the prevalence of negative appeals towards the government and opposing parties raises questions about the potential impact on citizens' trust in democracy and institutions. [ABSTRACT FROM AUTHOR]

We present an Natural Language Processing based analysis on the phenomenon of "Meme Stocks", which has emerged as a result of the proliferation of neo-brokers like Robinhood and the massive increase in the number of small-scale stock investors. Such investors often use specific Social Media channels to share short-term investment decisions and strategies, resulting in partial collusion and planning of investment decisions. The impact of online communities on the stock prices of affected companies has been considerable in the short term. This paper has two objectives. Firstly, we chronologically model the discourse on the most prominent platforms. Secondly, we examine the potential for using collaboratively made investment decisions as a means to assist in the selection of potential investments.. To understand the investment decision-making processes of small-scale investors, we analyze data from Social Media platforms like Reddit, Stocktwits and Seeking Alpha. Our methodology combines Sentiment Analysis and Topic Modelling. Sentiment Analysis is conducted using VADER and a fine-tuned BERT model. For Topic Modelling, we utilize LDA, NMF and the state-of-the-art BERTopic. We identify the topics and shapes of discussions over time and evaluate the potential for leveraging information of the decision-making process of investors for trading choices. We utilize Random Forest and Neural Network Models to show that latent information in discussions can be exploited for trend prediction of stocks affected by Social Network driven herd behavior. Our findings provide valuable insights into content and sentiment of discussions and are a vehicle to improve efficient trading decisions for stocks affected from short-term herd behavior. [ABSTRACT FROM AUTHOR]

This research examines migrant women’s representation in British and German news during the COVID-19 pandemic, paying particular attention to expressions of solidarity. While previous research has focused on the representation of women and migrants, studies have largely treated these groups as distinct entities. Combining topic modelling with informed grounded theory, this research compares the representation of migrant women, including women from ethnic minorities, across a diverse corpus covering 2020–21. This two-step methodology facilitates intersectional analysis at the macro and micro level, revealing the salience, topical concentration and context of migrant women’s coverage. Findings show that solidarity emerged through the platforming of migrant women’s voices, and connected to discourses of class, poverty and precarity. Representations containing empathy and pity – less likely drivers of social change – were more prevalent. This research contributes to our understanding of migrant women’s coverage during the pandemic and provides a granular and intersectional lens for analysing minoritized group representation. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is expected to transform many facets of our existence with the gradual penetration of the semantic web. One such AI-based technology is a chatbot that considerably alters numerous business verticals to enhance users and businesses. Even though companies are extensively contemplating using chatbots to supplement or even replace humans, users have exhibited anger, confusion, and customer dissatisfaction with chatbots. The current study aims to find the reasons for user frustration in the case of conversational chatbots and adopts exploratory research using a critical incident technique (CIT) survey. Results are analysed using sentiment analysis and topic modelling with Python. Results of the study indicate four significant causes of user frustration, i.e. poor semantic understanding, lack of customisation, lack of Humanisation of the bot, and poor competency. These causes have further been examined across various socio-demographic variables and industry contexts. This work would theoretically advance the bot literature and fulfil the gap by understanding the reasons for frustration. This would further help bot developers and marketers in designing and training chatbots that lead to better engagement and enhanced customer experience. [ABSTRACT FROM AUTHOR]

Objectives: Research in criminology and criminal justice is a rapidly growing interdisciplinary, international network of intersecting research topics. Quantifying the substantive content of criminology allows us to empirically disentangle this network and analyze how the interplay of research topic and international context influences knowledge production in the study of crime. Methods: In this paper we apply the topic embedding model, top2vec, mapping two decades of research in criminology and criminal justice (2001–2020). Using data generated by top2vec we enrich coauthorship network data—introducing themes and subdisciplines, and topical similarity measures—and analyze the relationship between topical and subdisciplinary overlap, geospatial distance, and publication coauthorship in a sample of mid-career researchers (N = 4068). Results: We find that these researchers disproportionately favor collaboration within their immediate network of collaborators and subdiscipline of criminology, but tend to establish new, synergistic collaborations with topically adjacent researchers. New collaborations appear to be independent of geographic distance, but US criminologists are less likely to collaborate internationally than Eurasian and Oceanic criminologists. Conclusions: Facilitating communication between researchers and organizations from adjacent subdisciplines could benefit researchers and produce new, innovative research. Encouraging comparative research would help international scholars, many of whom may rely on US collaborators, benefit more from US scholarship. Further applications for top2vec in the scientometric study of criminology, criminal justice, and legal studies are discussed. [ABSTRACT FROM AUTHOR]

Energy agendas in national parliaments are crucial when countries seek to develop their energy futures while adhering to international obligations. This article examines how energy agendas emerge and evolve in parliamentary debates using data from Finland over a period of 12 years. By relying on topic modelling, we can show how the key energy agendas relate to an overall energy solution, promoting domestic energy production and seeking carbon‐neutral energy, and how they evolve successively alongside general concerns for the country's task ahead in the field. Examining more detailed agendas, in turn, validates the first analysis and exposes some differences in the agendas of political parties. These differences were few. This further specifies the nature of Finnish energy politics, which is often considered consensual except for nuclear power and peat as sources of energy. The article demonstrates that scholars who examine parliamentary politics may benefit from the use of language‐based computational methodologies to uncover insights that have previously been difficult to attain. [ABSTRACT FROM AUTHOR]

The aim of this work is to understand the retraction phenomenon in the arts and humanities domain through an analysis of the retraction notices—formal documents stating and describing the retraction of a particular publication. The retractions and the corresponding notices are identified using the data provided by Retraction Watch. Our methodology for the analysis combines a metadata analysis and a content analysis (mainly performed using a topic modelling process) of the retraction notices. Considering 343 cases of retraction, we found that many retraction notices are neither identifiable nor findable. In addition, these were not always separated from the original papers, introducing ambiguity in understanding how these notices were perceived by the community (i.e. cited). Also, we noticed that there is no systematic way to write a retraction notice. Indeed, some retraction notices presented a complete discussion of the reasons for retraction, while others tended to be more direct and succinct. We have also reported many notices having similar text while addressing different retractions. We think a further study with a larger collection should be done using the same methodology to confirm and investigate our findings further. [ABSTRACT FROM AUTHOR]

Understanding the intellectual structure of geosciences is crucial for identifying emerging topics, which can guide future research and funding strategies. This study employs Latent Dirichlet Allocation (LDA) topic modeling and co-word network analysis to explore the intellectual landscape of highly cited geoscientific papers. Through empirical analysis, the study identifies 15 research areas, including climate change, geological processes, environmental impacts, and advancements in data analysis and remote sensing. These findings highlight the prominent role of big data analysis and machine learning methods across various geoscientific domains. Additionally, a notable gap has been identified in the integration of these computational methods with research on organic chemistry and formation processes, suggesting a potential direction for future exploration and innovation. [ABSTRACT FROM AUTHOR]