Search Results (104)

Augustine of Hippo (354–430), one of the most influential theologians of Late Antiquity, spent nearly a decade in the Manichaean sect before becoming a central figure in the shaping of Western “orthodox” Christianity. While his major works such as the Confessiones and De civitate Dei have been extensively studied for their treatment of Manichaeism, the vast collection of his ca. 300 preserved letters (Epistulae) remains an understudied source for understanding this aspect of his intellectual and theological development. This article addresses that gap by proposing a methodology to identify both anti- and crypto-Manichaean themes in his letters. Drawing on phenomenological openness, hermeneutical perspective, and close reading, the study also incorporates genuine Manichaean sources and anti-Manichaean polemics to contextualise Augustine’s rhetorical strategies. The Epistulae, unpolished and situated in specific communicative contexts, offer a unique view of Augustine’s doctrinal positioning after his conversion. Traces of his Manichaean past re-emerge in vocabulary, argumentation, and theological emphasis. This is exemplified in Epistula 137 to Volusianus (411–412), which, without naming the sect, covertly critiques key Manichaean doctrines such as Docetism and materialism. These critiques align with extant Manichaean sources and may reflect Augustine’s awareness of latent Manichaean influence in Christian communities. By bringing the Epistulae into the broader discussion of Augustine’s anti-Manichaean engagement, this study highlights their value as a window into his theological evolution and pastoral strategy in a religiously contested environment. Full article

As digital technologies and food environments evolve, ensuring children’s food safety has become a pressing public health priority. This study examines how the policy discourse on children’s dietary safety in Korea has shifted over time by applying Latent Dirichlet Allocation (LDA) topic modeling to news articles from 2010 to 2024. Using a large-scale news database (BigKinds), the analysis identifies seven key themes that have emerged across five phases of the national Comprehensive Plans for Safety Management of Children’s Dietary Life. These include experiential education, data-driven policy approaches, safety-focused meal management, healthy dietary environments, nutritional support for children’s growth, customized safety education, and private-sector initiatives. A significant increase in digital keywords—such as “big data” and “artificial intelligence”—highlights a growing emphasis on data-oriented policy tools. By capturing the evolving language and priorities in food safety policy, this study provides new insights into the digital transformation of public health governance and offers practical implications for adaptive and technology-informed policy design. Full article

In the context of green development, promoting the development of data elements is crucial for advancing the green and low-carbon transition and achieving China’s “dual-carbon” targets. This study quantitatively evaluates China’s data element policies to identify their strengths and weaknesses and to assess their alignment with green development objectives. In this study, we examine 15 representative data element policy texts, evaluating their quality by integrating the Latent Dirichlet Allocation (LDA) topic model with the PMC-Index model. The LDA analysis identifies five core themes within the policy texts: the data element industry, data resource management, data element trading systems, service platform construction, and e-governments. The evaluation results show an average PMC-Index score of 6.03 for the 15 policies, with 9 rated as “Good” and 6 as “Acceptable”. This indicates that while the overall design of the current policy system is acceptable, there remains substantial room for improvement. Based on the average scores for the primary indicators, the policies perform relatively poorly in terms of green development assessment, policy timeliness, policy nature, and policy guarantee. Drawing from these findings, we propose recommendations to enhance China’s data element policies, offering insights for policymakers. Full article

This study provides a detailed analysis of COVID-19-related social media discourse in Italy, using 535,886 tweets from 10 major cities between 30 August 2020 and 8 June 2021. The tweets were translated from Italian to English for analysis. A multifaceted methodology was employed: Latent Dirichlet Allocation (LDA) identified 20 key themes; sentiment analysis, using TextBlob, Flair, and TweetNLP, and emotion recognition using TweetNLP, revealed the emotional tone of the discourse, with 453 tweets unanimously positive across all algorithms. TextBlob was used for lexical analysis to rank the most salient positive and negative terms. The results indicated that positive sentiments centered on hope, safety measures, and vaccination progress, while negative sentiments focused on fear, death, and quarantine frustrations. This research offers valuable insights for public health officials, enabling tailored messaging, real-time strategy monitoring, and agile policymaking during the pandemic, with implications for future health crises. Full article

The gig economy, driven by flexible and platform-based work, is reshaping labor markets and employment norms. Understanding public perceptions of this shift is critical for promoting social good and informing equitable policy. This study employs big data analytics and Latent Dirichlet Allocation (LDA) topic modeling to analyze 15,259 tweets collected from the X platform. Seven key themes emerged from the data, including labor precarity, flexibility, algorithmic control, platform accountability, gender disparities, and worker rights. While some users emphasized autonomy and new income opportunities, most expressed concerns about job insecurity, lack of protections, and digital exploitation. These findings offer real-time insights into how gig work is discussed and contested in public discourse. The study highlights how social media analytics can inform labor policy, guide platform regulation, and support advocacy efforts aimed at building a fairer and more resilient gig economy. Full article

In today’s environment of increased uncertainty, firms face new challenges in aligning sustainability orientation (SO) with financial performance (FP). In this non-ergodic world, past trends offer limited insight into the future due to economic instability, geopolitical conflicts, trade wars, environmental and social disasters, sustainability policy and commitment reversals, etc. To investigate this, we conducted a systematic literature review and topic modelling with a latent Dirichlet allocation of 117 English peer-reviewed articles in management, business, economics, and finance related to SO and FP *. These articles, obtained from Scopus and Web of Science, were open-access and had reached the final publication stage. By integrating resource-based, institutional, and stakeholder theories, we aim to identify the current understanding of the SO concept and the mechanisms linking it to FP. Our findings show that sustainability-oriented firms are better equipped to achieve financial success in a non-ergodic world. However, outcomes vary widely based on context and duration, with existing literature revealing positive and negative relationships or no impact. Topic modelling identified 17 themes, such as stakeholder engagement, business performance, sustainability-oriented innovation and corporate sustainability. We propose five theoretical propositions and forward-looking research directions based on these findings. As a result, our study contributes to the existing academic literature by providing an integrated resource-based, institutional, and stakeholder theory view of the relationship between SO and FP for organisational resilience and outlining future research directions for managing this relationship in a non-ergodic world. Full article

The accelerated penetration of digital technology into natural ecosystems has led to the digital transformation of forest ecological spaces. Smart forestry, as a key pathway for digital-intelligence-enabled ecological governance, plays an important role in global sustainable development and multi-level governance. However, due to differences in functional positioning, resource capacity, and policy translation mechanisms, semantic shifts and disconnections arise between central policies, local policies, and practical implementation, thereby affecting policy execution and governance effectiveness. Fujian Province has been identified as a key pilot region for smart forestry practices in China, owing to its early adoption of informatization strategies and distinctive ecological conditions. This study employed the Latent Dirichlet Allocation (LDA) topic modeling method to construct a corpus of smart forestry texts, including central policies, local policies, and local media reports from 2010 to 2025. Seven potential themes were identified and categorized into three overarching dimensions: technological empowerment, governance mechanisms, and ecological goals. The results show that central policies emphasize macro strategy and ecological security, local policies focus on platform construction and governance coordination, and local practice features digital innovation and ecological value transformation. Three transmission paths are summarized to support smart forestry policy optimization and inform digital ecological governance globally. Full article

Aviation safety analysis increasingly relies on extracting actionable insights from narrative incident reports to support risk identification and improve operational safety. Topic modeling techniques such as Probabilistic Latent Semantic Analysis (pLSA) and BERTopic offer automated methods to uncover latent themes in unstructured safety narratives. This study evaluates the effectiveness of each model in generating coherent, interpretable, and semantically meaningful topics for aviation safety practitioners and researchers. We assess model performance using both quantitative metrics (topic coherence scores) and qualitative evaluations of topic relevance. The findings show that while pLSA provides a solid probabilistic framework, BERTopic leveraging transformer-based embeddings and HDBSCAN clustering produces more nuanced, context-aware topic groupings, albeit with increased computational demands and tuning complexity. These results highlight the respective strengths and trade-offs of traditional versus modern topic modeling approaches in aviation safety analysis. This work advances the application of natural language processing (NLP) in aviation by demonstrating how topic modeling can support risk assessment, inform policy, and enhance safety outcomes. Full article

In the face of an unprecedented public health crisis (COVID-19), despite tourist perceptions toward cultural heritage tourism having undergone significant transformation, such transitions are increasingly viewed as opportunities to enhance sustainability practices in cultural heritage tourism worldwide. This study traces the evolution of tourist perceptions at Lijiang Old Town, a UNESCO World Heritage Site, across three stages from 2017 to 2024—before the pandemic, during the pandemic, and after the pandemic. Data were collected from major tourism platforms, yielding a comprehensive dataset of 50,022 user-generated reviews. We adopt a mixed-method framework integrating TF-IDF, Social Network Analysis (SNA), and Latent Dirichlet Allocation (LDA) to identify salient terms, semantic structures, and latent themes from large-scale unstructured textual data across time. The findings indicate that cultural heritage tourism demonstrates adaptability and resilience through significant perceptual transitions. After the pandemic, visitors increasingly prioritized cultural depth and high-quality service experiences, whereas before the pandemic, tourists focused more on cultural heritage attractions and commercial experiences. Moreover, during the pandemic period, visitor narratives reflected adaptations toward quieter, safer, and more personalized experiences, highlighting the impact of safety measures on tourism patterns. These findings demonstrate the methodological potential for dynamically monitoring perception shifts and offer empirical grounding for future perception-oriented research and sustainable cultural heritage destination management practices in cultural heritage tourism toward sustainable tourism. Full article

The growing use of social media data has opened new avenues for understanding user perceptions and operational inefficiencies in transportation systems. Among the most widely adopted analytical approaches for extracting insights from these data are sentiment analysis and topic modeling, which enable researchers to capture public opinion trends and uncover latent themes in unstructured content. However, despite a rising number of individual studies, systematic reviews focusing specifically on these approaches in transportation research remain limited, particularly in addressing methodological challenges and data heterogeneity. This literature review addresses that gap by critically examining 81 open-access studies published between 2014 and 2024. The main challenges identified include handling linguistic diversity, integrating multimodal and geolocated data, managing short-text formats, and addressing regional and demographic bias. In response, this review proposes a methodological framework for study selection and bibliometric analysis, classifies the most commonly applied machine learning models for sentiment and topic extraction, and synthesizes findings regarding data sources, model performance, and application contexts in transportation. Additionally, it discusses unresolved gaps and ethical concerns related to representativeness and social media governance. This review highlights the transformative potential of combining sentiment analysis and topic modeling to support smarter, more inclusive, and sustainable transportation policies by offering an integrative and critical perspective. Full article

This study explores how auditing is evolving in the context of Artificial Intelligence (AI) by analyzing a dataset of 465 peer-reviewed publications from 1982 to 2024, sourced from Scopus and Web of Science. Using Latent Dirichlet Allocation (LDA), an unsupervised machine learning method, the study identifies ten key thematic areas reflecting how AI increasingly intersects with auditing research. The analysis suggests that topics related to integrating AI and data-driven technologies are especially prominent. The theme “AI in Auditing” emerges as the most frequently occurring topic, comprising approximately 33.4% of the discussion. In comparison, “Data Security in Auditing” follows at 21.2%, indicating sustained scholarly concern with the integrity and protection of digital audit data. Other notable themes, such as “Auditing and Accounting Technologies” (12.7%) and “AI and Machine Learning in Auditing” (11.1%), suggest a continuing interest in the development and application of advanced technologies within auditing. The analysis also points to the presence of more specialized or emerging areas, including “Ethical AI in Audit Systems”, “Image Processing in Audit”, and “Political Influence in Auditing”, though these appear less frequently. Topics related to environmental ethics and racial and ethnic disparities in auditing were identified. However, their low representation (0.4% each) may indicate that such issues remain relatively peripheral in current academic discourse. The study provides a data-driven overview of how AI-related topics are being discussed in the auditing literature. It may help identify areas of growing interest and potential research gaps. The findings could have implications for researchers, practitioners, and policymakers by offering insights into the technological and ethical priorities shaping the field. Full article

Inclusive education has become a central issue in global educational reform, advancing the agenda for educational equity and social justice. However, despite significant theoretical and policy developments, research in this field remains fragmented, and no coherent framework currently exists. This study analyzes 3663 SSCI papers published between 2000 and 2024, using Latent Dirichlet Allocation (LDA) topic modeling to identify 15 distinct research themes in inclusive education. By combining LDA with manual coding, four key research areas emerged: concept and connotation, macro needs and support, micro-level implementation, and implementation effects and challenges. These findings highlight the interconnections between policy, practice, and environmental factors shaping inclusive education. Based on these results, an integrated input–process–outcome–feedback (IPOF) framework is proposed to advance the understanding of inclusive education’s evolution, effectiveness, and implementation. This framework offers actionable insights for policymakers and educators, helping to promote inclusive education aligned with Sustainable Development Goal 4 (SDG 4) and advancing global educational equity. Full article

Hierarchy analysis of the knowledge graphs aims to discover the latent structure inherent in knowledge base data. Drawing inspiration from topic modeling, which identifies latent themes and content patterns in text corpora, our research seeks to adapt these analytical frameworks to the hierarchical exploration of knowledge graphs. Specifically, we adopt a non-parametric probabilistic model, the nested hierarchical Dirichlet process, to the field of knowledge graphs. This model discovers latent subject-specific distributions along paths within the tree. Consequently, the global tree can be viewed as a collection of local subtrees for each subject, allowing us to represent subtrees for each subject and reveal cross-thematic topics. We assess the efficacy of this model in analyzing the topics and word distributions that form the hierarchical structure of complex knowledge graphs. We quantitatively evaluate our model using four common datasets: Freebase, Wikidata, DBpedia, and WebRED, demonstrating that it outperforms the latest neural hierarchical clustering techniques such as TraCo, SawETM, and HyperMiner. Additionally, we provide a qualitative assessment of the induced subtree for a single subject. Full article

This study evaluates the competitiveness of small- and medium-sized manufacturing enterprises (PYMES) through a meta-analysis that explores the role of technology and quality. Through a comprehensive literature review, 24 eligible studies, selected after applying specific criteria in a systematic search of the Scopus database, were identified and analyzed. A random-effects model was used to combine the effect sizes of the selected studies, and the heterogeneity among them was assessed using widely accepted indicators such as I², H² and tau², which confirmed a moderate variability among the studies. The results of the meta-analysis indicated a pooled effect size of 0.53 (95% CI: [0.50, 0.55]), suggesting a significant positive relationship between innovation and competitiveness, as well as between quality and competitiveness. Quality was identified as the most relevant competitive priority, while the use of state-of-the-art technologies was highlighted as a significant risk factor in the context of digital transformation. In addition, topic analysis was performed using the latent Dirichlet allocation (LDA) model, implemented with the topicmodels package in RStudio 2024.04.2. To ensure accuracy of the analysis, the texts were preprocessed using cleaning and tokenization techniques, which included the removal of punctuation, numbers and empty words. This thematic analysis identified key patterns related to innovation management, operational strategies and integration of digital technologies. The themes generated revealed that manufacturing PYMES prioritize quality as a source of competitive advantage, facing significant challenges associated with technological adoption and digitalization. Full article

Greenwashing, the phenomenon of companies misleading consumers about their sustainability practices, is prevalent in the fashion industry. This study explores consumer opinions on greenwashing through analysis of social media discourse. Cognitive dissonance theory served as the theoretical framework, explaining how consumers reconcile conflicting information about brands’ sustainability claims. In Study 1, 446 comments on 12 Reddit posts were collected using the search term “fashion greenwashing”. Using the Latent Dirichlet Allocation (LDA) algorithm and manual review, we identified three major themes: the phenomenon of fashion greenwashing, consumer empowerment in sustainable fashion, and skepticism towards fast fashion brands’ marketing strategies. In Study 2, using the search term, “#fashiongreenwashing”, two researchers collected and analyzed 76 Instagram posts with 370 comments. A manual review was employed to extract major themes, and network graphs of caption tags within the same theme were constructed. Three major themes emerged: strategies to combat fashion greenwashing, examples of fashion greenwashing, and advocacy and regulation in sustainable fashion. Findings from Studies 1 and 2 revealed that consumers are increasingly aware of brands’ deceptive practices and advocacy for sustainable practices to resolve this dissonance when they see greenwashing information. This study underscored the need for fashion brands to provide transparent and authentic information. Full article