Social Links Research Articles

Trophic analysis of a historical network reveals temporal information

Trophic analysis exposes the underlying hierarchies present in large complex systems. This allows one to use data to diagnose the sources, propagation paths, and basins of influence of shocks or information among variables or agents, which may be utilised to analyse dynamics in social, economic and historical data sets. Often, the analysis of static networks provides an aggregated picture of a dynamical process and explicit temporal information is typically missing or incomplete. Yet, for many networks, particularly historical ones, temporal information is often implicit, for example in the direction of edges in a network. In this paper, we show that the application of trophic analysis allows one to use the network structure to infer temporal information. We demonstrate this on a sociohistorical network derived from the study of hadith, which are narratives about the Prophet Muhammad’s actions and sayings that cite the people that transmitted the narratives from one generation to the next before they were systematically written down. We corroborate the results of the trophic analysis with a partially specified time labelling of a subset of the transmitters. The results correlate in a manner consistent with an observed history of information transmission flowing through the network. Thus, we show that one may reconstruct a temporal structure for a complex network in which information diffuses from one agent to another via social links and thus allows for the reconstruction of an event based temporal network from an aggregated static snapshot. Our paper demonstrates the utility of trophic analysis in revealing novel information from hierarchical structure, thus showing its potential for probing complex systems, particularly those with an inherent asymmetry.

The Effect of Social Networks on Smallholder Farmers’ Decision to Join Farmer-Base Seed Producer Cooperatives (FBSc): The Case of Hararghe, Oromia, Ethiopia

The paper presents the results of analysis conducted to empirically establish the role of social network in smallholder farmers’ decision to join seed producer cooperative, in Hararghe, Oromia, Ethiopia. We used a ‘random matching within sample’ technique to generate data on social links and resources shared like information among their links. Average treatment effects were used to limit ‘non-awareness’ or ‘selection bias’ on participation rate. An econometric strategy was employed to isolate endogenous effect from correlated and contextual social network effects and analyzed by Probit model. Rainfall data was obtained from NMA for 1986–2018 years and analyzed by coefficient of variation (CV) and standardized anomalies index (Z). The result CV and Z shows intra- and inter seasonal variability of rainfall and, the severity and frequency of drought have increase which works against a single optimum seed source. Rate of membership to FBSc was 75% for exposed sub-sample against 70% for the full sample. This shows the existence of exposure bias. The size of social network, linkage with relatives and extension agents influence farmers’ decision to join FBSc and thus, the presence of endogenous effects of social networks. Thus, participation decision is more likely to be affected by characters and structure of social network. Access to off-farm income and perception on profit influences farmer participation decision where weather-indexed seed insurance can service as an effective strategy to ensure sustained membership. Therefore, any intervention in promotion of FBSc should consider the characters’ and structure of social network and emphasis on indexed-based seed insurance and climate information services as strategy to promote seed producers cooperatives.

Digital Banking : A Meta-Analysis Approach

This research paper attempted to study the behavioral intention adoption of prominent digital banking channels, such as mobile banking and wallets and online and telephone banking, in an international scenario. The behavioral intention adoption of mobile banking was studied through seven bivariate relationships: perceived usefulness, perceived ease of use, facilitating conditions, perceived security, cost, self-efficacy, and innovativeness in a global context, which have had a profound impact on the desire of people to adopt digital banking channels. The seven bivariate relationships were analyzed through a meta-analysis approach in a universal framework. The main findings were that location is a major contributor to heterogeneity in a comprehensive global framework. The main managerial implications of the paper are that the universal adoption of digital banking adoption is being significantly affected by location, and global strategists should, therefore, keep this constraint in mind while framing international policies and launching new digital products. Such products must appeal to the location to be successful. An important social link that the current research highlighted was that location has vital linkages with other social factors such as religion, caste, race, color, income, gender, education, family size, buying habits, and wealth, which need to be explored in detail for a better examination of the successful adoption of digital banking channels.

Digital Health and Learning in Speech-Language Pathology, Phoniatrics, and Otolaryngology: Survey Study for Designing a Digital Learning Toolbox App.

BackgroundThe digital age has introduced opportunities and challenges for clinical education and practice caused by infinite incoming information and novel technologies for health. In the interdisciplinary field of communication sciences and disorders (CSD), engagement with digital topics has emerged slower than in other health fields, and effective strategies for accessing, managing, and focusing on digital resources are greatly needed.ObjectiveWe aimed to conceptualize and investigate preferences of stakeholders regarding a digital learning toolbox, an app containing a library of current resources for CSD. This cross-sectional survey study conducted in German-speaking countries investigated professional and student perceptions and preferences regarding such an app’s features, functions, content, and associated concerns.MethodsAn open web-based survey was disseminated to professionals and students in the field of CSD, including speech-language pathologists (SLPs; German: Logopäd*innen), speech-language pathology students, phoniatricians, otolaryngologists, and medical students. Insights into preferences and perceptions across professions, generations, and years of experience regarding a proposed app were investigated.ResultsOf the 164 participants, an overwhelming majority (n=162, 98.8%) indicated readiness to use such an app, and most participants (n=159, 96.9%) perceived the proposed app to be helpful. Participants positively rated app functions that would increase utility (eg, tutorial, quality rating function, filters based on content or topic, and digital format); however, they had varied opinions regarding an app community feature. Regarding app settings, most participants rated the option to share digital resources through social media links (144/164, 87.8%), receive and manage push notifications (130/164, 79.3%), and report technical issues (160/164, 97.6%) positively. However, significant variance was noted across professions (H3=8.006; P=.046) and generations (H3=9.309; P=.03) regarding a username-password function, with SLPs indicating greater perceived usefulness in comparison to speech-language pathology students (P=.045), as was demonstrated by Generation X versus Generation Z (P=.04). Participants perceived a range of clinical topics to be important; however, significant variance was observed across professions, between physicians and SLPs regarding the topic of diagnostics (H3=9.098; P=.03) and therapy (H3=21.236; P<.001). Concerns included technical challenges, data protection, quality of the included resources, and sustainability of the proposed app.ConclusionsThis investigation demonstrated that professionals and students show initial readiness to engage in the co-design and use of an interdisciplinary digital learning toolbox app. Specifically, this app could support effective access, sharing, evaluation, and knowledge management in a digital age of rapid change. Formalized digital skills education in the field of CSD is just a part of the solution. It will be crucial to explore flexible, adaptive strategies collaboratively for managing digital resources and tools to optimize targeted selection and use of relevant, high-quality evidence in a world of bewildering data.

Effects of Social Control on Eating and Relational Behaviors in Romantic Relationships.

Prior research has shown that social control strategies can have either positive or negative effects on individuals' health behaviors. However, no research has examined the degree to which social control attempts enacted by romantic partners are associated with individuals' relational behaviors or whether perceptions of a partner's motivation to engage in social control moderate links between the use of social control and either health or relational behaviors. This study investigated (a) the degree to which two classes of social control strategies that romantic partners can use to improve their partners' eating behavior (autonomy-supportive and autonomy-limiting strategies) are associated with eating and relational behaviors, and (b) whether perceptions of the partner's motivation for using social control moderate associations between its use and an individual's eating and relational behavior. This study had a daily diary design. One hundred and forty-seven individuals in romantic relationships completed surveys for 14 consecutive days. On days when individuals reported that their partner used autonomy-supportive social control strategies, individuals reported healthier eating and more constructive relational behaviors. There were no effects of autonomy-limiting strategies on eating or relational behavior. Perceptions of a partner's motives did not moderate associations between reported social control and any behavioral outcomes. Moreover, there were no effects of either reported social control strategies or partner motive perceptions across time. Romantic partners' perceived use of autonomy-supportive-but not autonomy-limiting-social control has benefits for individuals' health behaviors without incurring notable costs to their romantic relationship.

Политическая роль проекта «Данбар» в развитии глобальной финансовой архитектуры

Digitalization is one of the main transformation processes of the modern system of global interactions, changing the nature of social links not only at the technical and economic, but also at the political level. Herewith, technological innovations also become instruments of political influence. The present article analyzes the management structure of one of the leading participants in the global financial market — the Bank for International Settlements (hereinafter — BIS) and highlights the important place the BIS occupies in the global financial architecture on the example of the Dunbar project, being developed in its innovation hub. In digital age the BIS has assumed a new leading role in exploring, developing and testing digital innovations in the central banking sector. It is noted that potential to use the BIS for political purposes is great, given that the Dunbar project will have far-reaching consequences for the structure of modern international relations.

Responding to Suicide Clusters in the Community: What Do Existing Suicide Cluster Response Frameworks Recommend and How Are They Implemented?

Suicide clusters involve an excessive number of suicides, suicide attempts, or both, that occur close in space or time or involve social links between cluster members. Although suicide clusters are rare, evidence documenting the implementation of suicide cluster response activities in communities is required yet remains limited. In this study, we identified the core components of existing suicide cluster response frameworks through a search of the grey literature and conducted an international survey to assess the implementation of the core components by stakeholders with experience responding to a suicide cluster. The following six core components were identified from five cluster response frameworks and were incorporated into a survey assessing stakeholders’ experiences of responding to a suicide cluster: (1) Preparing for a suicide cluster; (2) Routine monitoring of suicide, suicide attempts, and cluster detection; (3) Coordination with the media and monitoring social media; (4) Identifying and supporting individuals at risk; (5) Promoting help-seeking and building community resilience; and (6) Long-term follow-up and evaluation. Twenty-six stakeholders completed the online survey. Many of the core components were implemented by stakeholders. However, gaps in practice were reported in terms of cluster surveillance, monitoring of referral uptake among bereaved individuals, and long-term evaluation. Barriers to implementation included the perceived availability and suitability of mental health services, and availability of long-term funding. Strategic policy and planning that addresses the practice-based experiences of communities has the potential to facilitate a more coordinated and timely response to suicide clusters.

Point-of-interest recommendation model considering strength of user relationship for location-based social networks

Point of interest (POI) recommendation systems have drawn the attention of researchers in multiple domains, particularly location-based social networks (LBSNs). However, owing to barriers in data collection and information classification, most existing systems lack adaptability for users with varied relationship circles, which leads to unsatisfactory recommendation results. In this study, a model that considers user relationship strength is provided based on a data-driven method for improving the POI recommendations. It defines the user relationship according to an analysis of the user’s check-in behavior. The user’s social links are then embedded in the spatiotemporal model for POI recommendations. The effectiveness of this dynamic recommendation model is demonstrated by comparing six state-of-art POI recommendation techniques on three real-world datasets. The experiment results found significant correlations between the user relationship strength and check-in locations, which improved the model performance. Conceptually, this study supports the hypothesis that user relationship traits help explain personal preferences in LBSN usage and places visited. This study provides an intelligent social network system that provides real-time, location-aware recommendations for retailers.

The challenges of integrating signposting into general practice: qualitative stakeholder perspectives on care navigation and social prescribing in primary care

BackgroundA national policy focus in England to address general practice workforce issues has led to a commitment to employ significant numbers of non-general practitioner (GP) roles to redistribute workload. This paper focuses on two such roles: the care navigation (CN) and social prescribing link worker (SPLW) roles, which both aim to introduce ‘active signposting’ into primary care, to direct patients to the right professional/services at the right time and free up GP time. There is a lack of research exploring staff views of how these roles are being planned and operationalised into general practice and how signposting is being integrated into primary care.MethodsThe design uses in-depth qualitative methods to explore a wide range of stakeholder staff views. We generated a purposive sample of 34 respondents who took part in 17 semi-structured interviews and one focus group (service leads, role holders and host general practice staff). We analysed data using a Template Analysis approach.ResultsThree key themes highlight the challenges of operationalising signposting into general practice: 1) role perception – signposting was made challenging by the way both roles were perceived by others (e.g. among the public, patients and general practice staff) and highlighted inherent tensions in the expressed aims of the policy of active signposting; 2) role preparedness – a lack of training meant that some receptionist staff felt unprepared to take on the CN role as expected and raised patient safety issues; for SPLW staff, training affected the consistency of service offer across an area; 3) integration and co-ordination of roles – a lack of planning and co-ordination across components of the health and care system challenged the success of integrating signposting into general practice.ConclusionsThis study provides new insights from staff stakeholder perspectives into the challenges of integrating signposting into general practice, and highlights key factors affecting the success of signposting in practice. Clarity of role purpose and remit (including resolving tensions inherent the dual aims of ‘active signposting’), appropriate training and skill development for role holders and adequate communication and engagement between stakeholders/partnership working across services, are required to enable successful integration of signposting into general practice.

Contagion, a Hystorie

Faced with a multitude of variants in the psychoanalytic situation in America, is the transmission of psychoanalysis viable? And yet, even as desire is ever more commodified in late Capitalism and segregation is increasingly prevalent, something essential of psychoanalysis persists despite the best efforts to concoct an antidote. Lacan’s disturbing assertion that the analyst hystorizes only from himself points to the peculiar social link that constitutes the discourse of psychoanalysis, to the particularity of each cure that has the potential to escape the institutional hierarchies that would determine the praxis. (Author’s own translation)

Disruption and adaptation in response to the coronavirus pandemic - Assets as contextual moderators of enactment of health behaviours.

PurposeDuring the COVID‐19 UK first national lockdown (March–July 2020) enactment of healthy behaviours was fundamentally changed due to social restrictions. This study sought to understand perspectives on health behaviour change, as part of a wider study tracking reported health behaviour change over time.MethodsA purposive sample was selected. N = 40 qualitative interviews were conducted remotely (phone/video) from participants across England and Wales, and transcribed verbatim. Descriptive case studies were shared at regular analysis meetings. Inductive reflexive thematic coding was undertaken and coding was discussed using a team approach to agreeing analytical codes. A multiple lens theoretical perspective was adopted to illuminate the perceived influences and restrictions on participants' reports of health behaviour change.ResultsThere was a clear progressive narrative for all participants, through initial responses and reactions to the pandemic, framed as ‘disruption’, then, as lockdown was acclimatized to, evidence of ‘adaptation’. Adaptation was seen in terms of modification, substitution, adoption, discontinuation/cessation, stultification, maintenance and recalibration of health behaviours. An illustrative case study exemplifies the narrative encompassing these features and demonstrating the complex non‐linear interactions between context and enacted health behaviours.ConclusionsIndividuals responded to pandemic‐related social restrictions in complex ways. Those in contexts with existing social assets, community links and established patterns of healthy behaviours were able to respond positively, adapting by modifying behaviour and using technology to engage in healthy behaviours in new and innovative ways. For those in more vulnerable contexts, enacting (negative) health behaviour change was an expression of frustration at the limitations imposed by social restrictions.

A novel similarity measure of link prediction in bipartite social networks based on neighborhood structure

AbstractLink prediction is one of the methods of social network analysis. Bipartite networks are a type of complex network that can be used to model many natural events. In this study, a novel similarity measure for link prediction in bipartite networks is presented. Due to the fact that classical social network link prediction methods are less efficient and effective for use in bipartite network, it is necessary to use bipartite network-specific methods to solve this problem. The purpose of this study is to provide a centralized and comprehensive method based on the neighborhood structure that performs better than the existing classical methods. The proposed method consists of a combination of criteria based on the neighborhood structure. Here, the classical criteria for link prediction by modifying the bipartite network are defined. These modified criteria constitute the main component of the proposed similarity measure. In addition to low simplicity and complexity, this method has high efficiency. The simulation results show that the proposed method with a superiority of 0.5% over MetaPath, 1.32% over FriendLink, and 1.8% over Katz in thef-measure criterion shows the best performance.

Impacts and Lessons Learned of the First Three COVID-19 Waves on Cross-Border Collaboration in the Field of Emergency Medical Services and Interhospital Transports in the Euregio-Meuse-Rhine: A Qualitative Review of Expert Opinions.

BackgroundIn the Euregio-Meuse-Rhine (EMR), cross-border collaboration is essential for resource-saving and needs-based patient care within the emergency medical service (EMS) systems and interhospital transport (IHT). However, at the onset of the novel coronavirus SARS-COV-2 (COVID-19) pandemic, differing national measures highlighted the fragmentation within the European Union (EU) in its various approaches to combating the pandemic. To assess the consequences of the pandemic in the EMR border area, the aim of this study was to analyze the effects and “lessons learned” regarding cross-border collaboration in EMS and IHT.MethodA qualitative study with 22 semi-structured interviews was carried out. Experts from across the EMR area, including the City of Aachen, the City region of Aachen, the District of Heinsberg (Germany), South Limburg (The Netherlands), and the Province of Limburg, as well as Liège (Belgium), took part. The interviews were coded and analyzed according to changes in cross-border collaboration before and during the pandemic, as well as lessons learned and recommendations.ResultsEach EU member country within the EMR area, addressed the pandemic individually with national measures. Cross-border collaboration between regional actors was hardly or not at all addressed at the national level during political decision- or policymaking. Previous direct communication at the personal level was replaced by national procedures, which made regular cross-border collaboration significantly more difficult. The cross-border transfer regulations of patients with COVID-19 proved to be complex and led, among other things, to patients being transported to hospitals far outside the border region. Collaboration continues to be seen as valuable and Euregional emergency services including hospitals work well together, albeit to different degrees. The information and data exchange should, however, be more transparent to use resources more efficiently.ConclusionEffective Euregional collaboration of emergency services is imperative for public safety in a multi-border region with strong economic, cultural, and social cross-border links. Our findings indicate that existing (pre-pandemic) structures which included regular meetings of senior managerial staff in the region and a number of thematic working groups were helpful to deal with and to compensate for the disruptions during the crisis. Regional cross-border agreements that are currently based on mutual but more or less informal arrangements need to be formalized and better promoted and recognized also at the national and EU level to increase resilience. The continuous determination of synergies and good and best practices are further approaches to support cross-border collaboration especially in preparation for future crises.

FG-CF: Friends-aware graph collaborative filtering for POI recommendation

Collaborative filtering approach greatly promotes the development and application of personalized recommendation. In location-based social networks (LBSNs), the sparsity of check-in data is one of the main obstacles for traditional Point-of-Interest (POI) recommendation models. Graph convolutional network (GCN) is an efficient tool to overcome this kind of problems, which enhances the representational ability of embeddings by capture high-order connectivity of users and POIs. In real applications, social tie is a crucial factor for POI recommendation that ignored in most current graph-based methods. Moreover, most message aggregation functions fail to capture contextual information. To address these problems, a novel framework named Friends-aware Graph Collaborative Filtering (FG-CF) is proposed in this paper, which incorporates social information into a user-POI graph. Firstly, a user-POI correlation matrix is estimated by check-in data and social links, and then, user embedding is updated according to the user-POI correlation matrix. Secondly, interaction messages are constructed in a novel way by integrating nodes’ ego embeddings, neighbors’ embeddings and social embeddings. Thirdly, by aggregating previous state embeddings and non-linear combination of neighbor messages with interaction messages, a new message aggregation function is present to update user and POI embeddings. Fourthly, we concatenate embeddings from each additional interaction layer to get the final embeddings, and inner product is used to compute the preference score of a user to a targeted POI. Finally, extensive experiments on two large-scale LBSN datasets demonstrate the superiority of our model over several state-of-the-art approaches.

Use of Digital Tools, Social Isolation, and Lockdown in People 80 Years and Older Living at Home.

The COVID-19 crisis and associated lockdowns have exposed the extent of social isolation among older adults (OAs). Currently, the French government and medical, social, and charitable organizations are working to find means of limiting the multiple psychological and physical consequences of social isolation on the health of OAs. One proposal is to help the elderly become more comfortable using digital tools (DTs). However, the ability of DTs to reduce social isolation is disputed in the literature. This study aimed to collect the views of OAs on social isolation; to identify the determinants of the use or not of DTs, in particular in the context of a lockdown; and the role of DTs in the strategy to reduce social isolation. This qualitative study was based on 27 semi-structured individual interviews with OAs ≥ 80 years, in Côte-d’Or and Haute-Marne (French departments), from March to May 2021. A total of 96.3% of participants had already owned one or more DTs (mobile phone, tablet, or computer) for several years. The lockdown had not prompted the population to equip themselves more. The most common reason for using DTs was to maintain contact with relatives, and 63% of the participants said that DTs have a positive impact in reducing social isolation. However, there is a significant need for assistance and training in their use, especially since many services are now offered online. The participants suggested that the key to minimizing social isolation remained the maintenance of social contacts. In conclusion, DTs appear to be useful for helping the elderly maintain social links with relatives and, therefore, have a strategic place in the reduction of social isolation. However, these tools should not replace in-person interactions.

Joint Learning of User Representation With Diffusion Sequence and Network Structure

Information sharing behavior and social link building behavior have shown strong correlation on social media. The aim of this paper is to explore this correlation for simultaneously modeling and predicting sharing behavior in information diffusion sequences and linking behavior in social network, which correspond to information diffusion prediction and social link prediction problems. To achieve this goal, we propose a joint user representation learning model to characterize the two correlated behaviors in a shared latent space. The proposed model learns user representations via two maximum likelihood estimation objectives defined on observed information diffusion sequences and social network structure respectively and incorporates them in a unified framework. A multi-task learning algorithm is designed for efficient model optimization. Based on the learned representations, the model can be directly applied to predicting diffusion processes and inferring unobserved social links at the same time. We evaluate the proposed model on two real social media datasets with extensive experiments. The model consistently achieves significant improvements over the state-of-the-art approaches on diffusion prediction and link prediction tasks. The better robustness of our model in further ablation studies demonstrates that capturing the behavior correlation in the shared representation space is beneficial.

Enhancing higher education student well-being through social prescribing: a realist evaluation protocol

IntroductionUK higher education (HE) student numbers are increasing and students report higher levels of mental health and well-being issues. Social prescribing links individuals to community-based, non-medical support. It is widely...

Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes

The repeated expansion of East Asian steppe cultures was a key driver of Eurasian history, forging new social, economic, and biological links across the continent. Climate has been suggested as important driver of these poorly understood cultural expansions, but paleoclimate records from the Mongolian Plateau often suffer from poor age control or ambiguous proxy interpretation. Here, we use a combination of geochemical analyses and comprehensive radiocarbon dating to establish the first robust and detailed record of paleohydrological conditions for Lake Telmen, Mongolia, covering the past ~ 4000 years. Our record shows that humid conditions coincided with solar minima, and hydrological modeling confirms the high sensitivity of the lake to paleoclimate changes. Careful comparisons with archaeological and historical records suggest that in the vast semi-arid grasslands of eastern Eurasia, solar minima led to reduced temperatures, less evaporation, and high biomass production, expanding the power base for pastoral economies and horse cavalry. Our findings suggest a crucial link between temperature dynamics in the Eastern Steppe and key social developments, such as the emergence of pastoral empires, and fuel concerns that global warming enhances water scarcity in the semi-arid regions of interior Eurasia.

Social globalization, well‐being indicators and unaccompanied child migration from Central America

AbstractThis research investigates the effect of well‐being indicators and social globalization on the migration of Unaccompanied Alien Children (UAC) from Central America. The purpose of this study is to determine whether the surge in UAC that began in 2014 at the U.S. southern border is driven primarily by violence or whether other factors are at play. The apprehension of UAC serves as a proxy for measuring unaccompanied child migration to the United States. Unemployment rates of young adults and expected years of schooling are included as a measure of youth engagement in productive activities, while homicide rates measure the threat to leading a productive, healthy life. Global social network links are considered as possible pull factors of migration and measured through a social globalization index. The results of a fixed effects regression model reveal that youth idleness, along with increased social globalization, prove to be significant in explaining heightened UAC migration.

Classification and Detection of Amharic Language Fake News on Social Media Using Machine Learning Approach

The pervasive idea of web-based media stages brought about a lot of sight and sound information in interpersonal organizations. The transparency and unlimited way of sharing the data via online media stage encourages data spread across the organization paying little mind to its noteworthiness.The multiplication of misdirecting data in regular access news sources, for example, web-based media channels, news websites, and online papers has made it trying to recognize dependable news sources, in this way expanding the requirement for computational devices to give bits of knowledge into the unwavering quality of online substance. The broad spread of phony news contrarily affects people and society. Along these lines, counterfeit news identification via web-based media has as of late become arising research drawing in enormous consideration. Observing the possible damage caused by the rapid spread of fake news in various fields such as politics and finance, the use of language analysis to automatically identify fake news has attracted the attention of the research community. A social networking service is a platform for people with similar interests, activities,or backgrounds to form social networks or social relations. Participants who register on this site with its own expression (often a profile) and social links are generally offered a social network service.