Social Network Integration Research Articles

Improving the Effectiveness of a Stock Simulation Trading Course via Blockchain and Social Networking: A Taiwanese Study

Online courses in higher education became prevalent during the COVID-19 pandemic; however, their application requires technology to be fully integrated into the curriculum. This study explores the integration of a blockchain-based platform in a private online stock simulation trading course during the COVID-19 pandemic. Using a pre–post experimental design with 142 college students, it assessed learning behaviors and outcomes. Students collaborated with teaching assistants via LINE groups, fostering discussion and engagement. They received cryptocurrency rewards, which enhanced motivation and connected the course to their career goals. The findings suggest that combining blockchain and social networking is an effective approach to improving online education. This contributes to the literature on educational technology and online learning by exploring the integration of blockchain and social networking in higher education, specifically within the context of stock simulation trading courses, and demonstrates its impact on student motivation and learning outcomes.

Concept of a medical and public health cancer genomics platform

Abstract Background This study aims to produce the concept of an open-access medical and public health cancer genomics platform aimed at aggregating and sharing documentation of ongoing initiatives and data resulting from the CAN.HEAL project, available for EU researchers, healthcare professionals, policy makers, citizens and patients. Methods The context (reference sector, stakeholders and research evidences), feasibility (privacy management and data processing), and platform’s end users were analysed; the CAN.HEAL project deliverables were listed and clustered; aspects related to the definition of the overall platform architecture and modules integration were studied. Results The project deliverables were catalogued into 8 categories which could be the platform’s cloud interface main labels: results from events, policy papers, newsletters, mapping results and reports, datasets, recommendations and guidelines, use cases, courses and training activities. To define the overall system architecture, the following key activities should be implemented: content management system choice, hosting configuration and setup of the development domain, content management system installation, development of HTML web pages, data entry, data visualisation and graphic layout, and test and debug. To the modules integration purposes the following items should be addressed: creation of a server analytics account; social network integration; activation of additional system for sending newsletters. Conclusions Cloud-based data platform allows to securely manage, integrate, analyse and share large datasets. Establishing an infrastructure to help researchers access, store and analyse large amounts of biological data is of paramount importance both to enable advances in research and support health policy making processes. The CAN.HEAL platform could help aligning clinical and population-based interventions for integrating the genome of Europe biobanking initiative into public health genomics for cancer. Key messages • Connecting healthcare data can support the challenge of accessing the relevant information needed for the policy and decision making processes to effectively promote population health and wellbeing. • The set-up of a medical and public health cancer genomics platform promotes the effective translation of genome-based knowledge and technologies into public policy and health services.

Adaptive Strategies and Sustainable Innovations of Chinese Contractors in the Belt and Road Initiative: A Social Network and Supply Chain Integration Perspective

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China’s strategic initiatives, notably the “Going Global” strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives.

Height and integration in proximity networks among Tanzanian Hadza men.

In recent years there has been much interest in investigating the extent to which social status or prestige are related to an individual's degree of integration in social networks. It has been shown that, among hunter-gatherers, social characteristics of an individual based on social status or prestige, such foraging reputation, friendship popularity, and pro-social reputation, can influence the extent to which an individual is embedded in a social network. However, little is known regarding the extent to which height, a physical trait that in Western societies is often associated with social status, is associated in integration in social networks among small-scale hunter gatherers. Here, we investigated the relationship between height and a position an individual occupies in proximity networks among Hadza men (n = 30), hunter-gatherers living in Northern Tanzania. The results of our study show that height is not related to the position an individual maintains in proximity networks. We argue that, in a relatively egalitarian small-scale hunter-gatherer societies such as the Hadza, social interactions driving proximity networks might be influenced by social traits, such as popularity and hunting reputation, rather than physical traits, such as height.

Greening the future: How social networks and media shapes youth's eco-friendly purchases

Consumers' environmental information acquisition from social networks and media facilitates the sharing of recommendations on environmentally friendly products, sustainable initiatives, and environmental protection activities. This study investigates the crucial role of consumers' information acquisition from social networks and media in shaping their intentions to purchase green products in Vietnam. A mixed-methods approach was employed, combining qualitative and quantitative research methods to comprehensively explore the relevant driving factors. The qualitative analysis involved 12 participants, who were surveyed and participated in individual interviews and group discussions, providing in-depth insights into consumer perceptions and behaviors. Quantitative data was collected through a survey questionnaire from 377 respondents, with 348 valid responses used for analysis. Statistical techniques, including Cronbach's Alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), were utilized for data analysis. The findings highlight the significant impact of consumers' environmental information acquisition from social networks and media on perceived consumer effectiveness, environmental concern, and attitudes toward green products. Importantly, these factors were found to positively influence consumers' intentions to purchase green products. The study's results offer relevant implications for the green purchasing field, encouraging businesses to take more proactive steps in acknowledging and adapting to a better environmental and climate future. The integration of social networks and media platforms and fresh updates from businesses will serve as a foundation for groundbreaking initiatives, positioning green purchasing as a behavior shaped by consumer consciousness and action.

Social Management for The Prevention of Addiction to Social Networks and Anxiety in University Students

Objective: The objective of this study was to explain from the preventative context the relationship between dependence on social networks and anxiety in university students. Theoretical benchmark: Dependence on social networks is an important factor that can negatively affect mental health, causing behavioral phenomena such as anxiety, stress, depression, among others. Psychosocial problems in university students are related to the presentation of certain behaviors that affect their personal development and academic formation. Method: The type of study is non-experimental and the design of the study was descriptive across the board. The sample was composed of 320 university students of the psychology course. The ARS Scale of Dependence on Social Networks and the EAS Social Anxiety Scale were used as instruments. Results and discussion: The evidence showed a statistically significant correlation between social network dependence and anxiety rho=0,575 (p=0,000). In relation to the factors, the existence of a statistically significant relationship between obsession with social networks, lack of personal control, excessive use and physiological, cognitive and behavioral factors rho>.100 (p=0,000) was determined. Conclusion: The indiscriminate use of networks is associated with symptoms of anxiety in university students. Value: Therefore, preventive actions are guided from a cognitive-behavioral approach to managing social awareness about the effects and repercussions on your professional training and your future, as well as emphasizing the importance of the effective integration of social networks through responsible use.

Framing openness: Exploring Similarities and Differences in Patients' and Their Social Networks' Experiences with Participating in Dialogical Network Meetings Through the Lens of Mattering.

In this qualitative study conducted at a public mental health outpatient clinic in Norway, the integration of patients' social network in treatment was examined. The aim was to explore the experiences of patients and their network during dialogical network meetings and discuss any similarities and differences between the two participant groups. Reflexive thematic analysis was performed on data obtained from fifty-three meetings, resulting in the development of five themes. For patients, there were two themes: "Enhanced trust within our relationships" and "Providing us a safe space to talk openly," and for network members there were three: "Empowered through participation," Being welcomed and taken seriously," and "Provide more clarity to enhance our ability to contribute." Mattering was employed as a conceptual framework to discuss the similarities and differences between the themes of the two participant groups. Both patients and network members placed strong emphasis on the freedom of expression and acknowledged the crucial role of the meeting leaders in facilitating discussions on important and challenging topics. Differences included network members' emphasis on feeling welcome and their need to add value, while patients emphasized strengthened relationships and feeling valued and empowered by being trusted to control the discussion content. Overall, mattering appears to be a valuable tool for understanding the relational dynamics within network meetings.\.

The Ties That Bind: The Association Between Social Network Ties, Integration, and Stress Among Incarcerated Women

Social integration is theorized to enhance psychological well-being and reduce stress among incarcerated women. However, little empirical research has examined the association between prison social integration and women’s stress during incarceration. This study expands on previous research by (a) examining the relationship between different types of prison network ties among incarcerated women and self-reported stress and (b) exploring whether women’s perceptions of social integration mediate the association between prison network ties and stress. Using ordinal logistic models that incorporate social network and survey data from two women’s prisons in Pennsylvania ( N = 223) (WO-PINS), we find that pseudo-family–based, friendship-based, and trust-based network ties formed during incarceration are associated with reduced stress among incarcerated women. However, we find limited evidence that perceptions of social integration mediate the relationship between prison network ties and stress, suggesting that prison network ties are associated with stress reduction independent of an individual’s perception of integration.

THE INFLUENCE OF TRADITIONAL AND MODERN COMMUNICATION TECHNOLOGIES ON CONSUMER ATTITUDES

Nowadays, access to various forms of information is often achieved using modern information technologies. The media is flooded with information, but it has never been harder to get the right information. In the competitive struggle, traditional media rely more and more on the possibilities that new technological solutions bring. Integrating the features of print, radio and television with the possibilities provided by modern technologies is a key factor in future business. In the conditions of intense competition on the media scene, the Internet is the basis for the survival of traditional media companies at the local and regional level. Social networks have changed the way of thinking of their audience and their users. Thanks to the development of technology, it is possible to exchange information and spread numerous contents very quickly through social networks. The work indicates that a lot of time is spent on social networks. The aim of this paper is to point out the integration of traditional media and social networks and their influence on consumer attitudes.

A typology of social network interactions in sub-Saharan Africa: Evidence from a rural population in Senegal

Social isolation/marginalization in sub-Saharan Africa is under-researched, despite increasing evidence of weakening traditional community-based social support. This paper aims to develop a typology of social networks capable of accounting for social marginalization in a rural community in Western Senegal and to describe the socio-demographic characteristics of network profiles. Building on prior qualitative work, we carry out a latent profile analysis using a unique and extensive social network data set, identifying four different network profiles: Locally integrated, Constrained relationships, Locally marginalized, and Local elites. This paper provides the first empirically supported classification of social integration and marginalization in social networks in rural sub-Saharan Africa. In doing so, it can serve as a reference for future research seeking to understand both the broader scope of social integration and marginalization and the consequences of differential access to social capital through social networks on access to health resources and well-being.

Analyzing the Possibilities of Implementation of AI and Social Networks in Teaching Foreign Language Students: Ukrainian Universities Case Study

The growing integration of artificial intelligence (AI) and social networks into the educational landscape has raised questions about their impact on teaching and learning, particularly in foreign language instruction. This study explores the didactic properties of social networks and AI as information and communication technology (ICT) tools in the context of foreign language education at the university level. Through a comprehensive literature review, the researchers analyzed global studies on the integration of AI and social networks in language learning, as well as research specific to the Ukrainian higher education context. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was conducted to systematically evaluate the potential benefits and challenges associated with the implementation of these technologies in foreign language instruction. The findings indicate that using AI-driven language learning applications, and social media platforms can significantly enhance vocabulary retention, language proficiency, and student engagement. However, the study also highlights potential threats, such as risks to academic integrity, entrenchment of discrimination, and the democratization of plagiarism. The research further emphasizes the importance of addressing teachers’ skills in navigating the ethical implications of AI integration in educational settings. The study provides valuable insights for university administrators, language instructors, and educational technology researchers. It underscores the need for a nuanced and informed approach to incorporating AI and social networks into foreign language curricula, balancing the pedagogical benefits with appropriate safeguards to ensure the quality and integrity of the educational process. The findings contribute to the evolving discourse on the role of emerging technologies in transforming higher education and language teaching.

Social Network Integration and Online Review Helpfulness: An Empirical Investigation

Social Network Integration and Online Review Helpfulness: An Empirical Investigation

User evaluation and feasibility test of an app designed for smoking cessation in Italian people who smoke: preliminary findings from an uncontrolled pre-test post-test open study

BackgroundmHealth is a public health practice that exploits the use of mobile devices, including smartphone applications. We will describe an uncontrolled pre-test post-test open pilot study concerning the feasibility evaluation of a smartphone App designed to help in smoking cessation. The aim of this study is to evaluate the feasibility of a smartphone app as a tool for smoking cessation. This study is necessary to the literature because smoking is a major public health concern and has been linked to various health issues such as cardiovascular disease, respiratory disease, and cancer. While there are several smoking cessation interventions available, the use of mobile devices to aid in smoking cessation is a relatively new and innovative approach that requires further investigation.MethodsThe App “Smoke Free” was configured on the devices of N = 30 participants who smoked combustible cigarette, 13 males and 17 females aged 18 to 55 years, with the indications to use it for 90 days, describe their experience, suggest new features, and report any critical aspect. The study consisted of an initial screening visit to select participants that reflected the inclusion criteria and 4 study visits: a baseline visit, two follow-up visits, and one final visit. We used descriptive stats to summarize results. Repeated measures ANOVA and Wilcoxon test were used to test differences in smoking consumption, self-reported craving, and measured eCO level. Statistical software Jamovi was used for analysis. Interviews were conducted via phone or in-person and analyzed using qualitative description principles.ResultsParticipants evaluated the app as having good aesthetic appeal and user-friendliness but being moderately useful, despite some quitting or reducing their smoking behavior. To improve it, participants have proposed features such as more notifications, social network integration, and damage caused by smoking to the body over time for future app updates.ConclusionsThe application was moderately useful with good feasibility, with several suggestions for future updates that could improve its effectiveness.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

Social Network and Resources Integration Within Logistics Industry in China --- With Case Study of Cainiao Network

The business network in logistics industry is essential for integrating resources and establish firms own advantage of compettitiveness in the market, as well as providing more comprehensive services that facilitate firms to gain its revenue. Construction of intermediary platforms has therefore been an effective mean. Cainiao, a subdivided branch of Chinese giant enterprise Alibaba, has seized this opportunity. Though the debate about how social networks function to boost the high-quality development of firms has been very hot over the last three decades, few studies have mentioned and worked to understand to the linkage between social network analysis (SNA) and logistics chain management. Hence, the purpose of this research is to identify the construction of a business network by the giant Chinese logistics companies and its enhancing function of collaboration among firms and embeddedness for the success of Cainiao, which constructs the whole network. This paper will be carried out in concept explaination of essential terms in SNA and the practice of them into operation of Cainiao Network, as well as some diagrams made by Ucinet software will be applied. Results of the study show that through the early alliance of giant express enterprises, Cainiao has gained a wide network map that permits it to utilize abundant resources for the firms prosperity, and boosts the conjunction between the other companies and customers.

Joint Latent Space Model for Social Networks with Multivariate Attributes.

In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation-maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore the French financial elites based on their social networks and their career, political views and social status. We observe a division in the social circles of the French elites in accordance with the differences in their attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package "jlsm" is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf .

Integrating Technology into Language Teaching Practice in the Post-COVID-19 Pandemic Digital Age: From a Korean English as a Foreign Language Context

The onset of the COVID-19 pandemic has prompted a transformation in the traditional teaching practices of language classrooms in the post-COVID-19 pandemic digital age. The surge of innovative pedagogical resources and options has played a significant role in this transformation. The integration of digital technology has been recognized as a crucial means of promoting digital literacy, which is regarded as an essential skill in the current climate. In the realm of language education, this paradigm shift has encouraged educators to adopt new technological components and incorporate them into their course contents and activities to cater to the needs of the new digital generation of learners. The purpose of this study is to identify the impact of integrating social networking services such as KakaoTalk and YouTube into mobile-assisted language learning (MALL) in supporting Korean English as a foreign language (EFL) university students’ language learning during the COVID-19 pandemic. Utilizing a mixed-methods research design, the study gathered quantitative and qualitative data through survey questionnaire, online interview and interactive presentation tool. The result of this study revealed that the integration of social networks and MALL could support EFL university students’ language learning experience and boost students’ motivation and engagement in their learning process. This study also suggested that the concept of MALL could contribute to cultivating positive dynamics of classroom interaction. The findings of the study highlight the benefits of incorporating technology in language education and the importance of adapting teaching methods to meet the evolving needs of learners in a rapidly changing post-COVID-19 pandemic digital landscape.

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning

Vehicular social networking is an emerging application of the Internet of Vehicles (IoV) which aims to achieve seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks, such as high mobility and frequent communication interruptions, make content delivery to end-users under strict delay constraints extremely challenging. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three main components: 1) the proposed social-aware graph pruning search algorithm computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. 2) the proposed traffic-aware content recommendation scheme recommends relevant content according to its social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a deep reinforcement learning (DRL) method to optimise the content provider vehicle distribution across the network. The results obtained from a real-world traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

Illusion of diffusion: A percolation perspective on social structure and segregation in Twitter networks

Some diffusion trajectories produce cohesion while others generate highly fragmented social networks. Nevertheless, extant diffusion-based theoretical frameworks often assume continuous, gradual patterns of information flow and cascades and therefore cannot explain such disparity. Drawing on the percolation theory, which asserts that information propagation occurs abruptly and radically, this study conducts an empirical investigation into such structural integration or percolation efficiency in online social networks. In addition, we examine the roles of different characteristics of nodes and links, thereby identifying the detailed mechanism. The results demonstrate that node and link types significantly influence percolation efficiency. Regarding node types, influentials and broadcasters contribute to enhancing percolation efficiency, with the latter exhibiting a more substantial impact than the former. Hidden influentials exhibit a negligible effect on percolation efficiency, while common users negatively impact the efficiency of percolation. With respect to link types, incoming, outgoing, and bridge links serve to augment percolation efficiency. Overlap and adding links do not influence the efficiency of percolation, whereas new channel links impedes effective percolation. Based on a new theoretical perspective, this study extends research on information diffusion and word-of-mouth and provides stylized insights into effective social media targeting and advertising.

Exploiting social graph networks for emotion prediction

Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person’s physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person’s physiology, we also incorporate the environment’s impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user’s social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model’s performance.