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Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper. The current study examined the differences in brain activation when reading from a screen compared to reading from a printed paper focusing on spectral power related to attention in fifteen 6-8-year-old children. Using an electroencephalogram, children read two different age-appropriate texts, without illustrations, presented randomly on the screen and on a printed paper. Data were analyzed using spectral analyses in brain regions related to language, visual processing, and cognitive control, focusing on theta vs. beta waveforms. Results indicated that while reading from a printed paper was accompanied by higher energy in high-frequency bands (beta, gamma), reading from the screen was manifested by a higher power in the lower frequency bands (alpha, theta). Higher theta compared to the beta ratio, representing challenges in allocating attention to a given task, was found for the screen reading compared to the printed paper reading condition. Also, a significant negative correlation was found between differences in theta/beta ratio for screen vs paper reading and accuracy level in the age-normalized Sky-Search task measuring attention and a positive correlation with performance time. These results provide neurobiological support for the greater cognitive load and reduced focused attention during screen-based compared to print-based reading and suggest a different reliance on attention resources for the two conditions in children. [ABSTRACT FROM AUTHOR]

The Panama Papers comprise one of the most recent influential leaks containing detailed information on intermediary companies (such as law firms), offshore entities and company officers, and serve as a valuable source of insight into the operations of (approximately) 214,000 shell companies incorporated in tax havens around the globe over the past half century. Entities and relations in the papers can be used to construct a network that permits, in principle, a systematic and scientific study at scale using techniques developed in the computational social science and network science communities. In this paper, we propose such a study by attempting to quantify and profile the importance of entities. In particular, our research explores whether intermediaries are significantly more influential than offshore entities, and whether different centrality measures lead to varying, or even incompatible, conclusions. Some findings yield conclusions that resemble Simpson's paradox. We also explore the role that jurisdictions play in determining entity importance. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics–by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments)–using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents. [ABSTRACT FROM AUTHOR]

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field. [ABSTRACT FROM AUTHOR]

Background: Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth century, new knowledge has been generated from within an ever-growing network of investigators, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success. Objective: The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research. Methods: A bibliographic search in the Medline database containing the MeSH terms “psoriasis” or “psoriatic arthritis” was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Medline database (1942–2013), various bibliometric indicators were obtained, including the average number of authors per document and degree of multi-authorship over time. In addition, we performed a network analysis to study the evolution of certain features of the co-authorship network as a whole: average degree, size of the largest component, clustering coefficient, density and average distance. We also analyzed the evolution of the giant component to characterize the changing research patterns in the field, and we calculated social network indicators for the nodes, namely betweenness and closeness. Results: The main active research clusters in the area were identified, along with their authors of reference. Our analysis of 28,670 documents sheds light on different aspects related to the evolution of scientific collaboration in the field, including the progressive increase in the mean number of co-authors (which stood at 5.17 in the 2004–2013 decade), and the rise in multi-authored papers signed by many different authors (in the same decade, 25.77% of the documents had between 6 and 9 co-authors, and 10.28% had 10 or more). With regard to the network indicators, the average degree gradually increased up to 10.97 in the study period. The percentage of authors pertaining to the largest component also rose to 73.02% of the authors. The clustering coefficient, on the other hand, remained stable throughout the entire 70-year period, with values hovering around 0.9. Finally, the average distance peaked in the decades 1974–1983 (8.29) and 1984–2003 (8.12) then fell over the next two decades, down to 5.25 in 2004–2013. The construction of the co-authorship network (threshold of collaboration ≥ 10 co-authored works) revealed a giant component of 161 researchers, containing 6 highly cohesive sub-components. Conclusions: Our study reveals the existence of a growing research community in which collaboration is increasingly important. We can highlight an essential feature associated with scientific collaboration: multi-authored papers, with growing numbers of collaborators contributing to them, are becoming more and more common, therefore the formation of research groups of increasing depth (specialization) and breadth (multidisciplinarity) is now a cornerstone of research success. [ABSTRACT FROM AUTHOR]

For several decades, a leading paradigm of how to quantitatively assess scientific research has been the analysis of the aggregated citation information in a set of scientific publications. Although the representation of this information as a citation network has already been coined in the 1960s, it needed the systematic indexing of scientific literature to allow for impact metrics that actually made use of this network as a whole, improving on the then prevailing metrics that were almost exclusively based on the number of direct citations. However, besides focusing on the assignment of credit, the paper citation network can also be studied in terms of the proliferation of scientific ideas. Here we introduce a simple measure based on the shortest-paths in the paper's in-component or, simply speaking, on the shape and size of the wake of a paper within the citation network. Applied to a citation network containing Physical Review publications from more than a century, our approach is able to detect seminal articles which have introduced concepts of obvious importance to the further development of physics. We observe a large fraction of papers co-authored by Nobel Prize laureates in physics among the top-ranked publications. [ABSTRACT FROM AUTHOR]

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. [ABSTRACT FROM AUTHOR]

Today, due to the expansion of the Internet and social networks, people are faced with a vast amount of dynamic information. To mitigate the issue of information overload, recommender systems have become pivotal by analyzing users' activity histories to discern their interests and preferences. However, most available social image recommender systems utilize a static strategy, meaning they do not adapt to changes in user preferences. To overcome this challenge, our paper introduces a dynamic image recommender system that leverages a deep reinforcement learning (DRL) framework, enriched with a novel set of features including emotion, style, and personality. These features, uncommon in existing systems, are instrumental in crafting a user's characteristic vector, offering a personalized recommendation experience. Additionally, we overcome the challenge of state representation definition in reinforcement learning by introducing a new state representation. The experimental results show that our proposed method, compared to some related works, significantly improves Recall@k and Precision@k by approximately 7%–10% (for the top 100 images recommended) for personalized image recommendation. [ABSTRACT FROM AUTHOR]

The management and exploitation of knowledge can contribute to the competitive advantage of an organisation and hence can be a driver of its value. This paper examines knowledge management practices within two engineering organisations with an emphasis on barriers to its effectiveness and the influence of current and anticipated deployment of digital technologies. Two methods were used to gather research data across both organisations by combining a series of semi-structured interviews with a quantitative social network analysis. Examination of the acquired data provided insights into the relationship between the knowledge management culture in both organisations and their adoption of digital tools. Hudson's evolutionary model of safety culture has been modified to describe the culture of knowledge management in an organisation and the acquired data used to locate the two organisations on their knowledge management journey. It is proposed that social networks could be used as an indicator of the stage of evolution of knowledge management in engineering organisations more generally with low network densities and dispersed networks representing higher stages of evolution. [ABSTRACT FROM AUTHOR]

Objective: The overall goal of this work is to produce a set of recommendations (SoNHR–Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research. Methods: This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced. Results: The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines. Conclusions: These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas. [ABSTRACT FROM AUTHOR]

Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined. The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots. Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns. [ABSTRACT FROM AUTHOR]

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature. [ABSTRACT FROM AUTHOR]

In this paper, we concentrate on the neolithisation process in Mediterranean Iberia through a diachronic view (from 8600–6800 cal. BP), focusing on social interaction as a factor in articulating new cultural ties. To do this, we apply techniques centred on similarities in material culture by applying Social Network Analysis (SNA). For the first time, we point to the geometric projectiles, taking into account their recurrence in both Mesolithic and Neolithic groups as part of their characteristic hunting equipment. We hypothesise that patterns of cultural variability would express the changing flow of information between communities according to their mobility strategies (last hunter-gatherer groups), including economic and social behaviour, and that these relationships will be restructured with the arrival of the newcomer farmers and herders and their new spatial and social arrangement. The results obtained allow us to describe a connected and homogeneous Late Mesolithic network dramatically structured by the Neolithic arrival. Since then, a heterogenous pattern emerged, involving connected periods, network ruptures, and small-world phenomena. The emergence of this characteristic could support the flow of information when the network presents a clustered structure, the last probably due to regionalisation events. These diachronic dynamics fit well with demographic and socioecological trends observed from regional literature. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces in China from 2011 to 2019, this paper uses a two-stage DEA model to measure regional innovation efficiency, then non-parametric test is used to examine the impact of innovation network structure and government R&D investment on regional innovation efficiency. The results show that, at the provincial level, innovation efficiency of regional R&D is not necessarily in direct proportion to the innovation efficiency in the commercialization stage. Commercialization efficiency is not necessarily high in provinces with high technical R&D efficiency. At the national level, the innovation efficiency gap between our country's R&D and commercialization stage is small, indicating that the development of the national innovation efficiency is more and more balanced. Innovation network structure can promote the R&D efficiency, but has no significant effect on the commercialization efficiency. Government R&D investment helps to improve the R&D efficiency, but it is not conducive to the improvement of commercialization efficiency. The interaction between innovation network structure and government R&D investment will have compound effects on regional innovation efficiency; the region with underdeveloped innovation network structure can increase the government R&D investment to make it have a higher level of R&D. This paper provides insights into how to improve innovation efficiency in different social networks and policy environments. [ABSTRACT FROM AUTHOR]

People are obtaining more and more information from social media and other online sources, but the spread of misinformation can lead to social disruption. In particular, social networking services (SNSs) can easily spread information of uncertain authenticity and factuality. Although many studies have proposed methods that addressed how to suppress the spread of misinformation on SNSs, few works have examined the impact on society of diffusing both misinformation and its corrective information. This study models the effects of effort to reduce misinformation and the diffusion of corrective information on social disruption, and it clarifies these effects. With the aim of reducing the impact on social disruption, we show that not only misinformation but also corrective information can cause social disruption, and we clarify how to control the spread of the latter to limit its impact. We analyzed the misinformation about a toilet-paper shortage and its correction as well as the social disruption this event caused in Japan during the COVID-19 pandemic in 2020. First, (1) we analyzed the extent to which misinformation and its corrections spread on SNS, and then (2) we created a model to estimate the impact of misinformation and its corrections on the world. Finally, (3) We used our model to analyze the change in this impact when the diffusion of the misinformation and its corrections changed. Based on our analysis results in (1), the corrective information spread much more widely than the misinformation. From the model developed in (2), the corrective information caused excessive purchasing behavior. The analysis results in (3) show that the amount of corrective information required to minimize the societal impact depends on the amount of misinformation diffusion. Most previous studies concentrated on the impact of corrective information on attitudes toward misinformation. On the other hand, the most significant contribution of this study is that it focuses on the impact of corrective information on society and clarifies the appropriate amount of it. [ABSTRACT FROM AUTHOR]

Social network analysis can support quality improvement in care homes but traditional approaches to social network analysis are not always feasible in care homes. Recalling contacts and movements in a home is difficult for residents and staff and documentary and other sources of individual contacts can be unreliable. Bluetooth enabled wearable devices are a potential means of generating reliable, trustworthy, social network data in care home communities. In this paper, we explore the empirical, theoretical and real-world potential and difficulties in using Bluetooth enabled wearables with residents and staff in care homes for quality improvement. We demonstrate, for the first time, that a relatively simple system built around the Internet of Things, Bluetooth enabled wearables for residents and staff and passive location devices (the CONTACT intervention) can capture social networks and data in homes, enabling social network analysis, measures, statistics and visualisations. Unexpected variations in social network measures and patterns are surfaced, alongside "uncomfortable" information concerning staff time spent with residents. We show how technology might also help identify those most in need of social contact in a home. The possibilities of technology-enabled social network analysis must be balanced against the implementation-related challenges associated with introducing innovations in complex social systems such as care homes. Behavioural challenges notwithstanding, we argue that armed with social network information, care home staff could better tailor, plan and evaluate the effects of quality improvement with the sub-communities that make up a care home community. [ABSTRACT FROM AUTHOR]

Social networks often involve the users rating each other based on their beliefs, abilities, and other characteristics. This is particularly common in e-commerce platforms where buyers rate sellers based on their trustworthiness. However, the rating tends to vary between users due to differences in their individual scoring criteria. For example, in a transaction network, a positive user may give a high rating unless the transaction was unsatisfactory while a neutral user may give a mid-rating, consequently giving the same numeric score to different levels of satisfaction. In this paper, we propose a novel method called user tendency-based rating scaling, which adjusts the current rating (its score) based on the pattern of past ratings. We investigate whether this rating scaling method can classify between "good users" and "bad users" in online trade social networks better when compared with using the original rating scores without scaling. Classifying between good users and bad users is especially important for anonymous rating networks like Bitcoin transaction networks, where users' reputations must be recorded to preclude fraudulent and risky users. We evaluate the proposed rating scaling method by performing user classification, link prediction, and clustering tasks, using three real-world online rating network datasets. We use both the original ratings and the scaled ratings as weights of graphs and use a weighted graph embedding method to find node representations that reflect users' positive and negative information. The experimental results showed that using the proposed rating scaling method outperformed using the original (i.e., unscaled) ratings by up to 17% in classification accuracy, and by up to 2.5% in link prediction based on the AUC ROC measure, and by up to 21% in the clustering tasks based on the Dunn-index. [ABSTRACT FROM AUTHOR]

Disinformation–false information intended to cause harm or for profit–is pervasive. While disinformation exists in several domains, one area with great potential for personal harm from disinformation is healthcare. The amount of disinformation about health issues on social media has grown dramatically over the past several years, particularly in response to the COVID-19 pandemic. The study described in this paper sought to determine the characteristics of multimedia social network posts that lead them to believe and potentially act on healthcare disinformation. The study was conducted in a neuroscience laboratory in early 2022. Twenty-six study participants each viewed a series of 20 either honest or dishonest social media posts, dealing with various aspects of healthcare. They were asked to determine if the posts were true or false and then to provide the reasoning behind their choices. Participant gaze was captured through eye tracking technology and investigated through "area of interest" analysis. This approach has the potential to discover the elements of disinformation that help convince the viewer a given post is true. Participants detected the true nature of the posts they were exposed to 69% of the time. Overall, the source of the post, whether its claims seemed reasonable, and the look and feel of the post were the most important reasons they cited for determining whether it was true or false. Based on the eye tracking data collected, the factors most associated with successfully detecting disinformation were the total number of fixations on key words and the total number of revisits to source information. The findings suggest the outlines of generalizations about why people believe online disinformation, suggesting a basis for the development of mid-range theory. [ABSTRACT FROM AUTHOR]

Prior studies showed that scientists’ professional networks contribute to research productivity, but little work has examined what factors predict the formation of professional networks. This study sought to 1) examine what factors predict the formation of international ties between faculty and graduate students and 2) identify how these international ties would affect publication productivity in three East Asian countries. Face-to-face surveys and in-depth semi-structured interviews were conducted with a sample of faculty and doctoral students in life sciences at 10 research institutions in Japan, Singapore, and Taiwan. Our final sample consisted of 290 respondents (84 faculty and 206 doctoral students) and 1,435 network members. We used egocentric social network analysis to examine the structure of international ties and how they relate to research productivity. Our findings suggest that overseas graduate training can be a key factor in graduate students’ development of international ties in these countries. Those with a higher proportion of international ties in their professional networks were likely to have published more papers and written more manuscripts. For faculty, international ties did not affect the number of manuscripts written or of papers published, but did correlate with an increase in publishing in top journals. The networks we examined were identified by asking study participants with whom they discuss their research. Because the relationships may not appear in explicit co-authorship networks, these networks were not officially recorded elsewhere. This study sheds light on the relationships of these invisible support networks to researcher productivity. [ABSTRACT FROM AUTHOR]

This paper examines the fundamental problem of testimony. Much of what we believe to know we know in good part, or even entirely, through the testimony of others. The problem with testimony is that we often have very little on which to base estimates of the accuracy of our sources. Simulations with otherwise optimal agents examine the impact of this for the accuracy of our beliefs about the world. It is demonstrated both where social networks of information dissemination help and where they hinder. Most importantly, it is shown that both social networks and a common strategy for gauging the accuracy of our sources give rise to polarisation even for entirely accuracy motivated agents. Crucially these two factors interact, amplifying one another's negative consequences, and this side effect of communication in a social network increases with network size. This suggests a new causal mechanism by which social media may have fostered the increase in polarisation currently observed in many parts of the world. [ABSTRACT FROM AUTHOR]

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction. [ABSTRACT FROM AUTHOR]

With the development of economic globalization, urban agglomerations have become growth poles and core areas of economic development. By building knowledge innovation networks in urban agglomerations, we can effectively improve the strength of inter-city knowledge innovation links and better realize the integrated and synergistic development of the region. This study selected core cities in the Yangtze River Delta urban agglomeration as the study area, constructed the knowledge innovation network based on inter-city dissertation cooperation data from 2010 to 2020, and analyzed the characteristics and evolution of its knowledge network by combining social network analysis and geospatial analysis. The research results show that: (1) with changes in policies and investment in scientific research and innovation, intra-regional thesis cooperation in the Yangtze River Delta urban agglomeration has been increasing and the scale of the knowledge innovation cooperation network is growing; (2) in addition to the core cities radiating innovation resources outward to drive the development of other node cities, other cities are continuously improving their own innovation capabilities, taking the initiative to strengthen knowledge innovation cooperation with core cities and enhancing their own position in the network; (3) there are no longer isolated cities within the Yangtze River Delta urban agglomeration, and a multi-core knowledge network structure centered on Shanghai, Nanjing, Hangzhou, and Suzhou has initially formed, but the network is still spatially heterogeneous; (4) there are still problems within the Yangtze River Delta urban agglomeration such as uneven development of knowledge innovation and low participation of peripheral cities, which need to be addressed jointly by all regions. The article concludes with some suggestions for countermeasures to provide a reference for the Yangtze River Delta urban agglomeration to continuously strengthen intra-regional knowledge cooperation in the future, enhance regional competitiveness, and ultimately achieve synergistic development among cities. [ABSTRACT FROM AUTHOR]

Introduction: UniSA's Invictus Pathways Program (IPP), a service program, was originally developed to assist veterans to train for and participate in the Invictus Games. More recently, the scope of the IPP has widened to support and improve wellbeing and facilitate post traumatic growth and recovery among participants who are living with physical and mental health injuries and conditions. This paper describes the components of the IPP and reports its process evaluation. Methods: Underpinned by a pragmatic approach, data related to participant and student involvement in the IPP, the number of participant training sessions, session attendance, program activities and events, and program fidelity were compiled from process documentation that had been collected between 2017 and 2020, inclusive. Following ethics approval, semi-structured interviews were conducted with participants of the IPP, members of their family support network, and university staff to understand the operations of the IPP and satisfaction with the program. Findings: There was high fidelity for the student-led exercise training aspects of IPP; however, data collection relevant to participants' psychological outcomes, and non-training IPP events and activities did not always occur as intended. Between 2017 and 2020, 53 veterans had participated in or were still participating in the IPP, and 63 allied health students had completed placements as student trainers. Fifty-three individual training sessions were delivered in 2017, increasing to 1,024 in 2020. Seventy-one interviews were completed with key IPP stakeholders. The qualitative analysis identified four higher order themes: Implementation and fidelity of the IPP, Satisfaction with the IPP, Areas of the IPP requiring improvement and suggestions for change, and Sustainability of the IPP. Satisfaction was generally high for the IPP, although there were factors that negatively impacted the experience for some participants and their family support network. Suggestions for improvement to program components and delivery aspects were made during the interviews, and the precariousness of IPP funding and sustainability was raised as an ongoing concern. Conclusion: This process evaluation has demonstrated that the physical activity training components of the IPP were delivered with high levels of fidelity, and that satisfaction with the IPP is mostly high, although there are areas that could be improved. There is a need for a more structured approach to the ongoing evaluation of the IPP. This includes ensuring that program staff have a shared understanding of the purpose of evaluation activities and that these activities occur as intended. Beyond this is the need to secure funding to support the sustainability of the IPP, so that it can continue to contribute to the wellbeing of veterans living with physical and mental health conditions, and their families. [ABSTRACT FROM AUTHOR]

The timing of Tiwanaku's collapse remains contested. Here we present a generational-scale chronology of Tiwanaku using Bayesian models of 102 radiocarbon dates, including 45 unpublished dates. This chronology tracks four community practices: residing short- vs. long-term, constructing monuments, discarding decorated ceramics, and leaving human burials. Tiwanaku was founded around AD 100 and around AD 600, it became the region's principal destination for migrants. It grew into one of the Andes' first cities and became famous for its decorated ceramics, carved monoliths, and large monuments. Our Bayesian models show that monument building ended ~AD 720 (the median of the ending boundary). Around ~AD 910, burials in tombs ceased as violent deaths began, which we document for the first time in this paper. Ritualized murders are limited to the century leading up to ~AD 1020. Our clearest proxy for social networks breaking down is a precise estimate for the end of permanent residence, ~AD 1010 (970–1050, 95%). This major inflection point was followed by visitors who used the same ceramics until ~AD 1040. Temporary camps lasted until roughly ~AD 1050. These four events suggest a rapid, city-wide collapse at ~AD 1010–1050, lasting just ~20 years (0–70 years, 95%). These results suggest a cascading breakdown of community practices and social networks that were physically anchored at Tiwanaku, though visitors continued to leave informal burials for centuries. This generation-scale chronology suggests that collapse 1) took place well before reduced precipitation, hence this was not a drought-induced societal change and 2) a few resilient communities sustained some traditions at other sites, hence the chronology for the site of Tiwanaku cannot be transposed to all sites with similar material culture. [ABSTRACT FROM AUTHOR]

The passage of the Inflation Reduction Act has been perceived as a substantial shift away from the history of more contentious climate politics in the US. We apply social network methods to interrogate an updated dataset that assesses the degree to which recent policy outcomes are a shift away from earlier policies and positions. We empirically test for homophily, a building block of polarisation, analysing four waves of survey data collected over 12 years from the community of political elites engaged in the issue of climate politics. Using Exponential Random Graph (ERG) modeling, we provide clear evidence that the stances of the top policy actors working on climate change have not shifted substantially. Instead, we document how the policy was successful due to its ability to combine the Administration's desire to support clean energy along with fossil fuel interests' aims of expanding extraction and profiting from a transition away from fossil fuels. [ABSTRACT FROM AUTHOR]

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970–2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship. [ABSTRACT FROM AUTHOR]

With data collected from a directed social trading network, this paper investigates how social interaction affects the disposition effect. We constantly observe a negative association between them: After being exposed to social interaction, a trader's odds ratio to sell a paper gain stock decreases by 9% to 10%, depending on different model settings. We then test the mechanisms of social interaction by decomposing it into three channels: learning intensity (willingness to learn), learning quality (information advantage through learning), and public scrutinization (exposure of trading outcome to others). We find that all three channels contribute to a smaller disposition effect. Specifically, our findings support the claim that public scrutinization promotes self-consciousness and reduces disposition effect. Also, our results extend previous studies on investors' information advantage by suggesting that it could also help to mitigate the disposition effect through the reduction of uncertainty. Overall, this paper suggests a positive role of social trading platforms in helping investors make better decisions. [ABSTRACT FROM AUTHOR]

Forecasting possible future relationships between people in a network requires a study of the evolution of their links. To capture network dynamics and temporal variations in link strengths between various types of nodes in a network, a dynamic weighted heterogeneous network is to be considered. Link strength prediction in such networks is still an open problem. Moreover, a study of variations in link strengths with respect to time has not yet been explored. The time granularity at which the weights of various links change remains to be delved into. To tackle these problems, we propose a neural network framework to predict dynamic variations in weighted heterogeneous social networks. Our link strength prediction model predicts future relationships between people, along with a measure of the strength of those relationships. The experimental results highlight the fact that link weights and dynamism greatly impact the performance of link strength prediction. [ABSTRACT FROM AUTHOR]

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society. [ABSTRACT FROM AUTHOR]

Objective: To conduct a time-cost analysis of formatting in scientific publishing. Design: International, cross-sectional study (one-time survey). Setting: Internet-based self-report survey, live between September 2018 and January 2019. Participants: Anyone working in research, science, or academia and who submitted at least one peer-reviewed manuscript for consideration for publication in 2017. Completed surveys were available for 372 participants from 41 countries (60% of respondents were from Canada). Main outcome measure: Time (hours) and cost (wage per hour x time) associated with formatting a research paper for publication in a peer-reviewed academic journal. Results: The median annual income category was US$61,000–80,999, and the median number of publications formatted per year was four. Manuscripts required a median of two attempts before they were accepted for publication. The median formatting time was 14 hours per manuscript, or 52 hours per person, per year. This resulted in a median calculated cost of US$477 per manuscript or US$1,908 per person, per year. Conclusions: To our knowledge, this is the first study to analyze the cost of manuscript formatting in scientific publishing. Our results suggest that scientific formatting represents a loss of 52 hours, costing the equivalent of US$1,908 per researcher per year. These results identify the hidden and pernicious price associated with scientific publishing and provide evidence to advocate for the elimination of strict formatting guidelines, at least prior to acceptance. [ABSTRACT FROM AUTHOR]

The nodes and their connection relationships are the two main bodies for dynamic complex networks. In existing theoretical researches, the phenomena of stabilization and synchronization for complex dynamical networks are generally regarded as the dynamic characteristic behaviors of the nodes, which are mainly caused by coupling effect of connection relationships between nodes. However, the connection relationships between nodes are also one main body of a time-varying dynamic complex network, and thus they may evolve with time and maybe show certain characteristic phenomena. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. Therefore, it is important to investigate theoretically the reasons in dynamics for the occurrence. Especially, from the angle of large-scale systems, how the dynamic behaviors of nodes (such as the individuals, neurons) contribute to the connection relationships is one of worthy research directions. In this paper, according to the structural balance theory of triad proposed by F. Heider, we mainly focus on the connection relationships body, which is regarded as one of the two subsystems (another is the nodes body), and try to find the dynamic mechanism of the structural balance with the internal state behaviors of the nodes. By using the Riccati linear matrix differential equation as the dynamic model of connection relationships subsystem, it is proved under some mathematic conditions that the connection relationships subsystem is asymptotical structural balance via the effects of the coupling roles with the internal state of nodes. Finally, the simulation example is given to show the validity of the method in this paper. [ABSTRACT FROM AUTHOR]

Background: When the Zika virus outbreak became a global health emergency in early 2016, the scientific community responded with an increased output of Zika-related research. This upsurge in research naturally made its way into academic journals along with editorials, news, and reports. However, it is not yet known how or whether these scholarly communications were distributed to the populations most affected by Zika. Methodology/Principal findings: To understand how scientific outputs about Zika reached global and local audiences, we collected Tweets and Facebook posts that linked to Zika-related research in the first six months of 2016. Using a language detection algorithm, we found that up to 90% of Twitter and 76% of Facebook posts are in English. However, when none of the authors of the scholarly article are from English-speaking countries, posts on both social media are less likely to be in English. The effect is most pronounced on Facebook, where the likelihood of posting in English is between 11 and 16% lower when none of the authors are from English-speaking countries, as compared to when some or all are. Similarly, posts about papers written with a Brazilian author are 13% more likely to be in Portuguese on Facebook than when made on Twitter. Conclusions/Significance: Our main conclusion is that scholarly communication on Twitter and Facebook of Zika-related research is dominated by English, despite Brazil being the epicenter of the Zika epidemic. This result suggests that scholarly findings about the Zika virus are unlikely to be distributed directly to relevant populations through these popular online mediums. Nevertheless, there are differences between platforms. Compared to Twitter, scholarly communication on Facebook is more likely to be in the language of an author’s country. The Zika outbreak provides a useful case-study for understanding how scientific outputs are communicated to relevant populations. Our results suggest that Facebook is a more effective channel than Twitter, if communication is desired to be in the native language of the affected country. Further research should explore how local media—such as governmental websites, newspapers and magazines, as well as television and radio—disseminate scholarly publication. [ABSTRACT FROM AUTHOR]

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF's. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels. [ABSTRACT FROM AUTHOR]

This study investigate how the Urban identity(UI) influence the entrepreneurial choice of the migrants. Drawing on the identity economics theory in combination with microscopic perspective on entrepreneurship, we conclude that the UI increases the odds of self-employment of the migrants by 19.91% after solving endogenous problem in our sample of 126385 individuals from the China Migrants Dynamic Survey. We test the moderating effect of medical insurance and find that the interaction coefficient is positive. This study further reveals that the expanding social networks, improving urban integration, and increasing income are the three main mechanisms through which the UI influences the entrepreneurial choice of the migrants. So, we derive results consistent with our hypotheses. The findings have implications for both the entrepreneurship and national policy literature. [ABSTRACT FROM AUTHOR]

Study design: Single-cohort longitudinal survey design. Objectives: To identify what ongoing impact the COVID-19 pandemic has on functioning and health in individuals with SCI. Using the ICF model as a guide, outcome measures were chosen to explore potential constructs and aspects of health and functioning which may have been affected by regulations. Setting: Online, Canada. Methods: Participants provided demographic and clinical characteristics at baseline. They completed standardized online measures at three time points, each roughly one month apart (June, July, and August of 2020). The measures assessed mental health, resilience, boredom, social support, technology use, life space, and participation. Repeated measures ANOVAs were used to identify longitudinal changes for each measure. Results: We collected data from 21 participants with SCI (mean age 54 years, 12 male). We found a large effect size for participation (η2 = 0.20), which increased over time. We also found medium effect sizes in both anxiety (η2 = 0.12) and social network usage (η2 = 0.12). Anxiety decreased over time and social networking usage fluctuated slightly but with an increase from time point one to time point two. Conclusion: The results indicate that individuals with spinal cord injury appear to be staying relatively stable during the pandemic with improvements in a few key aspects, such as potentially increased participation and decreased anxiety. The results also suggest that it is important to continue fostering ways for individuals with spinal cord injury to stay connected, engaged, and informed. [ABSTRACT FROM AUTHOR]

Background: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. Methodology/Principal Findings: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of ‘steel structure’ for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations. Conclusions/Significance: Authors’ network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations. [ABSTRACT FROM AUTHOR]

This paper aims to examine the impacts of social isolation and risk perception on social networking intensity during COVID-19. Data was gathered from 345 university students. The present study empirically analyzed the data through a partial least squares methodology. The analysis showed that perceived behavioral control positively impacts social networking intensity. Affective risk perception positively affects subjective norms and perceived behavioral control. Cognitive risk perception has a significant association with both subjective norms and perceived behavioral control. Moreover, cabin fever syndrome serves as the key determinant of both sub-scales of risk perception. This study is novel in that it organically examines the effects of risk perception, social action, and closure on social networking. The current research and findings will offer useful implications for service providers in the social network industry. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that social network support plays an important role in the quality of life and illness management of breast cancer survivors. However, the factors and processes that enable and sustain such support are less well understood. This paper reports baseline findings from a prospective UK national cohort of 1,202 women with breast cancer (aged <50 years at diagnosis), recruited before starting treatment, conducted in 2016–2019. Descriptive, univariate and multivariate regression analyses explored associations between the individual, and network member characteristics, and the type of support provided. Social network members provided a substantial level of illness-related, practical and emotional support. Highest contribution was provided by friends, followed by close family members. The social network members of women who did not have a partner provided a higher level of support than those in networks with a partner. Women without higher education were more reliant on close family members than those with higher education, and this was more so for women without a partner. Women with higher education without a partner were more reliant on friends and were overall best supported. Women without higher education who did not have a partner were overall least well supported. They had much smaller networks, were highly reliant on close family members, and on high level contributions from all network members. There is a need to develop network-based interventions to support people with a cancer diagnosis, prioritising support for the groups identified as most at risk. Interventions that support engagement with existing network members during treatment, and those that help extend such networks after treatment, are likely to be of benefit. A network perspective can help to develop tailored support and interventions by recognising the interactions between network and individual level processes. [ABSTRACT FROM AUTHOR]

This paper seeks to unveil how (geospatial) connection strategies associated with business innovation, differ between geolocated social media and hyperlink company networks. Thereby, we provide a first step towards understanding connection strategies of innovative companies on social media platforms. For this purpose, we build a hyperlink and Twitter follower network for 11,892 companies in the information technology (IT) sector and compare them along four dimensions. First, underlying network structures were assessed. Second, we asserted information flow patterns between companies with the help of centrality measures. Third, geographic and cognitive proximities of companies were compared. Fourth, the influence of company characteristics was examined through linear and logistic regression models. This comparison revealed, that on a general level the basic connection patterns of the hyperlink and Twitter network differ. Nevertheless, the geospatial dimension (geographic proximity) and the knowledge base of a company (cognitive proximity) appear to have a similar influence on the decision to connect with other companies on Twitter and via hyperlinks. Further, the results suggest that innovative companies most likely align their connection strategies across hyperlink and Twitter networks. Thus, business innovation might effect connection strategies across online company networks in a comparable manner. [ABSTRACT FROM AUTHOR]

Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness. [ABSTRACT FROM AUTHOR]

This paper presents a bibliographic analysis of Nature articles based on altmetrics. We assess the concern degree of social users on the Nature articles through the coverage analysis of Twitter and Facebook by publication year and discipline. The social media impact of a Nature article is examined by evaluating the mention rates on Twitter and on Facebook. Moreover, the correlation between tweets and citations is analyzed by publication year, discipline and Twitter user type to explore factors affecting the correlation. The results show that Twitter users have a higher concern degree on Nature articles than Facebook users, and Nature articles have higher and faster-growing impact on Twitter than on Facebook. The results also show that tweets and citations are somewhat related, and they mostly measure different types of impact. In addition, the correlation between tweets and citations highly depends on publication year, discipline and Twitter user type. [ABSTRACT FROM AUTHOR]

The last several decades have witnessed a shift in the way in which news is delivered and consumed by users. With the growth and advancements in mobile technologies, the Internet, and Web 2.0 technologies users are not only consumers of news, but also producers of online content. This has resulted in a novel and highly participatory cyber-physical news awareness ecosystem that fosters digital activism, in which volunteers contribute content to online communities. While studies have examined the various components of this news awareness ecosystem, little is still known about how news media coverage (and in particular digital media) impacts digital activism. In order to address this challenge and develop a greater understanding of it, this paper focuses on a specific form of digital activism, that of the production of digital geographical content through crowdsourcing efforts. Using refugee camps from around the world as a case study, we examine the relationship between news coverage (via Google news), search trends (via Google trends) and user edit contribution patterns in OpenStreetMap, a prominent geospatial data crowdsourcing platform. In addition, we compare and contrast these patterns with user edit patterns in Wikipedia, a well-known non-geospatial crowdsourcing platform. Using Google news and Google trends to derive a measure of thematic public awareness, our findings indicate that digital activism bursts tend to take place during periods of sustained build-up of public awareness deficit or surplus. These findings are in line with two prominent mass communication theories: agenda setting and corrective action, and suggest the emergence of a novel stimulus-awareness-activism framework in today’s participatory digital age. Moreover, these findings further complement existing research examining the motivational factors that drive users to contribute to online collaborative communities. This paper brings us one step closer to understanding the underlying mechanisms that drive digital activism in particular in the geospatial domain. [ABSTRACT FROM AUTHOR]

Background: Social networks, i.e., our in-person and online social relations, are key to lifestyle behavior and health, via mechanisms of influence and support from our relations. We assessed associations between various social network aspects and practicing behavior to prevent respiratory infectious diseases. Methods: We analyzed baseline-data (2019) from the SaNAE-cohort on social networks and health, collected by an online questionnaire in Dutch community-dwelling people aged 40–99 years. Outcome was the number of preventive behaviors in past two months [range 0–4]. Associations between network aspects were tested using ordinal regression analyses, adjusting for confounders. Results: Of 5,128 participants (mean age 63; 54% male), 94% regularly washed hands with water and soap, 55% used only paper (not cloth) handkerchiefs/tissues; 19% touched their face as little as possible; 39% kept distance from people with respiratory infectious disease symptoms; median score of behaviors was 2. Mean network size was 11 (46% family; 27% friends); six network members were contacted exclusively in-person and two exclusively via phone/internet. Participants received informational, emotional, and practical support from four, six, and two network members, respectively. Independently associated with more preventive behaviors were: 'strong relationships', i.e., large share of friends and aspects related to so called 'weak relationships', a larger share of distant living network members, higher number of members with whom there was exclusively phone/internet contact, and more network members providing informational support. Club membership and a larger share of same-aged network members were inversely associated. Conclusion: Friends ('strong' relationships) may play an important role in the adoption of infection-preventive behaviors. So may 'weak relationships', e.g. geographically more distant network members, who may provide informational support as via non-physical modes of contact. Further steps are to explore employment of these types of relationships when designing infectious diseases control programs aiming to promote infection-preventive behavior in middle aged-and older individuals. [ABSTRACT FROM AUTHOR]

Scholars have offered multiple theoretical resolutions to explain inconsistent findings about the relationship of state repression and protests, but this repression-dissent puzzle remains unsolved. We simulate the spread of protest on social networks to suggest that the repression-dissent puzzle arises from the nature of statistical sampling. Even though the paper's simulations construct repression so it can only decrease protest size, the strength of repression sometimes correlates with a decrease, increase, or no change in protest size, regardless of the type of network or sample size chosen. Moreover, the results are most contradictory when the repression rate most closely matches that observed in real-world data. These results offer a new framework for understanding state and protester behavior and suggest the importance of collecting network data when studying protests. [ABSTRACT FROM AUTHOR]

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods. [ABSTRACT FROM AUTHOR]

Early childhood music education has garnered recognition for its unique contribution to cognitive, emotional, and social development in children. Nevertheless, the industry grapples with numerous challenges, including a struggle to adapt traditional educational paradigms to new curriculum reforms, and an excessive emphasis on skill training at the expense of nurturing a love for music and aesthetics in children. To navigate these challenges and explore growth strategies for the early childhood music education industry, we initiated a comprehensive approach that involved distributing surveys to practitioners and parents and engaging experts for insightful discussions. Consequently, we proposed an analytical method based on dynamic social networks in conjunction with Intuitionistic Fuzzy Sets (IFS), Analytic Hierarchy Process (AHP), and Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, collectively referred to as IFS-AHP-SWOT. This integrated methodology synergizes the capabilities of dynamic social networks, IFS, AHP, and SWOT analysis to offer a nuanced perspective on industry development strategies. The findings underscore that institutions within the early childhood music education industry need to adopt a development strategy that leverages their strengths and opportunities to foster sustainable growth. Ultimately, this research aims to provide critical decision-making support for industry practitioners, policymakers, and researchers, contributing significantly to the ongoing discourse on strategic development in the early childhood music education industry. [ABSTRACT FROM AUTHOR]