Benedetto Ligorio’s book delves into the role of Sephardic Jews in the Republic of Ragusa during the 16th century as key players in international trade. Drawing on social network analysis and quantitative archival data, Ligorio examines the Jews’ commercial activities and demonstrates they were integral to Ragusa’s thriving economy, connecting Venice, Ancona, and Ottoman-controlled regions. He compares the establishment of the ghetto in Ragusa in 1546 with those in Venice and Rome, and highlights the pragmatism of Ragusa’s government in allowing its Jewish merchants to continue their commercial activities despite imposed restrictions. Ligorio sheds light on the fluctuating size of the Jewish community, and the eventual concentration of their economic activities in merchant roles after the 1571 decree. Ligorio’s work contributes significantly to the understanding of the Jewish diaspora in the Early Modern Mediterranean, opening avenues for further research into the socioeconomic dynamics of the vibrant Ragusan community and beyond.

ABSTRACT This study visualizes Indonesian e-wallet tweets combining Social Network Analysis (SNA), geocoding and classifying data tweet to deepen marketing insights. SNA serves as a business intelligence tool, while geocoding with named entity recognition (NER) and ArcGIS are used to predict the point of position in data spatial. StanfordNER tools are used to build a location classification model using NER with six classes, include province, district/city, sub-district, village, road, and place names. Each tweet was also classified using two aspects using IndoBert, sentiment (positive, negative, and neutral) and categorization (features, access, service, security, and responsiveness). Thus, each tweet underwent several tasks: sentiment classification, category classification, and finally, spatial data points. The tweet data was then created into an integrated map to facilitate analysis of e-wallet usage in Indonesia. This research uses social media “X” as known “Twitter”. Neutral sentiments dominate, showing that conversations are mostly informative. E-wallet features and services are the most discussed aspects, especially in capital city, Jakarta and crowded island (Java), emphasizing the need for improvements. Negative sentiment is concentrated in West Java and Jakarta, mainly due to security and service concerns. Seasonal trends highlight engagement spikes during promotions and decline due to unmet expectations.

Analysis of intellectual and thematic progress of the Journal of Environmental Management - Section V: Environmental Policy, Economics, and Social Science.

Purpose: To present an editorial and analytical synthesis of the 2025 issue (Volume 6) of the Review of Artificial Intelligence in Education, highlighting conceptual, methodological, and thematic trends emerging across the international studies published on AI in education. Methodology: An integrative review of all articles in the edition was conducted, examining theoretical perspectives, methodological designs, explanatory models, institutional diversity, and cross-national contributions. The synthesis draws on comparative analysis of findings from Europe, Asia, Latin America, the Caribbean, Africa, and Brazil. Findings: The issue reveals four major axes: (1) AI governance and ethics, with growing emphasis on regulation, explainability, and compliance frameworks (EU; ISO 42001). (2) Pedagogical practices and real-world AI use by students and educators, showing tensions between efficiency gains and educational risks. (3) Student vulnerability and digital inequality, underscoring the need for inclusive policy frameworks. (4) Methodological diversification, marked by PLS-SEM, Social Network Analysis, PRISMA-based reviews, normative-institutional analyses, and conceptual modeling in generative AI. Conclusion: The 2025 edition reinforces the journal’s role as a global reference in AI-in-education research, combining scientific rigor, methodological plurality, and commitment to open science. The contributions deepen international dialogue on responsible AI governance, innovative teaching practices, and equitable digital transformation.

The rapid expansion of digital media has reshaped political communication in Indonesia, creating fragmented pathways through which issues diffuse across mainstream and participatory platforms. Despite this transformation, limited research has examined how public debates surrounding major government programs spread within hybrid media systems or what mechanisms determine issue centrality. This study addresses that gap by analyzing discourse dynamics related to Free Nutritious Meals (MBG) and Danantara. Using a sequential explanatory mixed-methods design, the study first mapped structural patterns quantitatively and then deepened interpretation through qualitative analysis. The dataset comprised 1,696 online news articles, 363 YouTube videos, more than 26 million user comments, and survey responses from 620 participants, offering a comprehensive representation of Indonesia’s digital discourse landscape. Structural Topic Modeling (STM) was used to identify dominant issues, while Social Network Analysis with QAP and MRQAP assessed co-occurrence patterns. Engagement metrics captured audience polarization, and thematic plus Critical Discourse Analysis (CDA) examined contrasts between institutional and participatory framing. Findings reveal that issue frequency—not semantic similarity—is the strongest predictor of diffusion. High-frequency issues consistently emerged as hubs in discourse networks. Mainstream media largely legitimized policy through socio-economic frames, whereas YouTube channels amplified criticism, satire, and counter-narratives, reflecting sharp audience polarization. Qualitative analysis reinforced these divergences, demonstrating how institutional and participatory media construct competing interpretations of the same policies. The integrated findings produced a conceptual model—”Frequency-Driven Co-occurrence”—which explains how mention intensity drives issue centrality and narrative evolution. The model advances agenda-setting and framing theories by shifting emphasis from semantic similarity to issue salience as the primary diffusion mechanism in hybrid media environments. Practical implications highlight the need for transparency, stronger digital literacy, and collaboration with credible influencers to reduce polarization, while future research should examine longitudinal trajectories, algorithmic amplification, and affective dynamics in digital discourse.

Abstract Several studies in the sociology of immigration have focussed on informal networks as the primary source of migrants’ social capital. However, the literature has largely eschewed the potential afforded by the computational analytical tools of network science that permit the reconstruction and mapping of community sociograms and the calculation of the impact of centrality metrics. In addition, studies are still scarce that would combine network-analytic approaches with rigorous investigations of refugees’ acquisition of host-country language skills, despite the proven import thereof for functioning in the new destination. We analyse the peer interaction networks of 251 Ukrainian refugees participating in an intensive Polish language course. Employing a custom-designed name-interpreter survey in conjunction with a pre-/post-test design, we leverage computational social network analysis to i) identify patterns of participants’ informal communication beyond the classroom, with particular attention to interactions within their co-national group, and ii) examine how these patterns, alongside individual background characteristics, affect their language development. Speaking Ukrainian correlated with greater centrality in the contact network. Russian-dominant speakers often concealed their use of this language, possibly because they were frequently found at the network periphery. We discuss the affective, motivational, and interactive factors identified as predictors of progress in Polish.

ABSTRACT The terraced field agricultural cultural system is a significant manifestation of the coordinated development of people and land in mountainous regions. A significant proportion of terraced fields have been included in the Agricultural Cultural Heritage Register, indicating their recognized tourist value. The present study employs public online review data from four mainstream platforms, and utilizes word frequency analysis and social network analysis methods to examine the commonalities and characteristics of mountainous agricultural heritage development from a tourist perspective across four typical rice terraced field agricultural cultural heritage sites in China, under regional differences. The results indicate three key points. (1) tourists place a high emphasis on regional characteristics, terraced field landscapes, and scenic area management. (2) For terraced fields in different regions, visitors place the highest priority on the natural scenery of these fields. Agricultural cultivation techniques and the regional ethnic characteristics of terraced fields represent pivotal focal points for visitors. (3) The emphasis on artificial landscapes and behavioral activities is closely associated with the duration of visitors’ trips to terraced field scenic areas in different regions. This research has yielded multifaceted insights into visitors’ travel feedback, providing robust support for the development, protection and management of mountain agricultural heritage.

Strategic forecasting of internet of things technologies through patent social network and innovation cluster analysis

Retraction Note: Mapping Knowledge Dynamics: Social Network Analysis of English as a Third Language Acquisition in Tibetan Classrooms

A social network analysis of the (STEM)2 Network model: bridging disciplinary and institutional silos

Factors impacting stress in financial investment due to the use of artificial intelligence: a social networking analysis approach

Purpose We compare new establishment of large mills versus revitalisation of traditional local mills in Indonesia's rice sector with respect to food security, farmer livelihoods, and rural economic linkages, asking whether efficiency gains from modernisation erode local relationships that stabilise prices and jobs. Design/methodology/approach We surveyed 55 mills, 118 farmers, and other actors (n = 281) across three Aceh regions using a convergent parallel mixed-methods design (surveys, interviews, network mapping). Spatial social network analysis (SSNA) analysed observed transactions; constrained simulation completed missing ties to characterise grain flows, actor centrality and regional connectivity. Findings New large mills lift short-run efficiency but often weaken local linkages. Revitalising small/medium mills – adding drying capacity, basic quality control and working capital – strengthens resilience and price stability. Network metrics identify collectors as central brokers; reinforcing collector–mill ties is pivotal. Public-led support better avoids destructive competition and anchors local supply networks. Research limitations/implications This single-province case uses partially observed networks with simulated ties; implications apply to Aceh, and any generalisation is conditional on similar network structures and price-policy settings. Originality/value The study integrates primary field evidence with SSNA to compare establishment versus revitalisation, links network structure to food-security and rural-economy outcomes, and translates metrics into actionable policy steps for non-technical readers.

Exploring social connections and mental well-being among members of a sober active community: A social network analysis.

Stakeholder-associated impact factors in the promotion of electric construction machinery for plateau tunnels: A social network analysis

This study examines the discrepancy between big data talent training and industry demand. The study analyzed 85 training programs and over 10,000 job postings from two job boards in China (51job and Zhaopin). Using content analysis, social network analysis, and the BERTopic-TOPSIS model, it mined implicit information from training programs and labeled key competencies in job descriptions. A key finding was a significant supply-demand misalignment: while “data application ability” was a stated goal in 52% of programs, only 11% of graduation requirements specified concrete, measurable skills to achieve it. The study identified three primary employment pathways for big data management and application majors: data management, data analysis, and data platform development. Institutions such as Peking University and Hefei University of Technology were identified as best practices. The study then delineated a cultivation path for the major by integrating the characteristics of these employment pathways, and optimised general knowledge and compulsory courses, core courses, graduation requirements, and the cultivation objectives of the major.

Mega Research Infrastructure as a Driver for High-quality Development and Innovation: Promoting Scientific Cooperation and Interdisciplinarity

Social stability risk of public-private-partnership waste-to-energy incineration projects in China: Social network analysis of stakeholders

To address global environmental and health problems, scientists must work across disciplinary boundaries. The Science of Team Science is a field of study that examines the processes by which effective scientific teams operate across disciplines. We incorporated strategies from team science into undergraduate courses to help students develop an appreciation of other disciplines and to learn how to create productive science research teams. We then explored student team interactions and interdisciplinary thinking in three course-based undergraduate research experiences (CURE) in three different disciplines that were linked by complementary research questions in the same system. Through writing prompts scored with the Interdisciplinary Science Rubric, students demonstrated an intermediate level of understanding of the importance of interdisciplinary teams. Social network analysis revealed evidence of students learning from and building trust with students in the other CUREs. This study highlights the benefits of integrating concepts across disciplines in science, technology, engineering, and mathematics (STEM) education to better prepare undergraduates for modern STEM careers.

The framework of integrating passive acoustic monitoring (PAM) and deep learning algorithms with social network analysis (SNA) presents a groundbreaking approach to understanding the complex dynamics of animal societies, especially studying the social behavior and communication of elusive species or those living in inaccessible habitats. By leveraging the non-invasive nature of PAM, we could collect long-term, high-resolution audio data of animal vocalizations, which are essential for understanding social interactions. Applying deep learning algorithms to these data has significantly enhanced our ability to identify, classify, and extract subtle patterns within vocalizations, revealing social subgroups and communication networks that were once undetectable. Furthermore, this technological advancement enables the efficient processing of vast amounts of data and the integration of multi-layered information, such as movement and environmental data, to create a comprehensive view of animal social networks. The framework proposed in this review also facilitates the comparison of social networks across different species and ecological contexts, contributing to a deeper understanding of the principles governing social behavior. As technology continues to evolve, the potential of this framework to transform our capacity to study and protect animal societies is immense, offering a promising future for behavioral ecology and conservation biology.

In the modern academic landscape, research collaboration has become a cornerstone for driving innovation and addressing complex societal challenges. The effectiveness of interdisciplinary research is increasingly recognized as a key factor in solving complex global issues. This paper explores the Faculty Research Collaboration Network (FRCN) to assess and visualize the patterns of research collaborations across different academic departments. Through network analysis, we investigate the nature of collaborations, identify interdisciplinary links, and propose ways to strengthen academic research networks. We utilize data from various faculty members, their research areas, and collaboration history to build a comprehensive collaboration network. By employing social network analysis (SNA) techniques, this research highlights key areas for enhancing faculty collaboration, fostering interdisciplinary approaches, and advancing academic output for societal benefit. The findings of this study demonstrate the importance of interdisciplinary collaboration and propose actionable steps for fostering a more cohesive and impactful research environment.