Measuring the Impact of Social Network Density on Performance

2007 ◽  
Author(s):  
Tracey E. Rizzuto ◽  
John Paul Hatala ◽  
Kara R. Jeansonne
Keyword(s):  
Social Network ◽  
Network Density ◽  
The Impact

scholarly journals Social Network Relationships between Biomass Industry Stakeholders in the Agricultural Waste Power Generation Industry—A Case of Northern Jiangsu, China

2022 ◽  
Vol 14 (1) ◽  
pp. 571
Author(s):  
Jingan Zhu ◽  
Huaxing Lin ◽  
Xinyu Yang ◽  
Xiaohui Yang ◽  
Ping Jiang ◽  
...  
Keyword(s):  
Power Generation ◽  
Social Network ◽  
Information Acquisition ◽  
Agricultural Waste ◽  
Vital Role ◽  
Biomass Energy ◽  
Network Density ◽  
The Sustainable Development ◽  
Stakeholder Relationships ◽  
The Impact

This study aimed to explore the impact of the interaction between stakeholders in the sustainable development of the biomass industry and to reveal network issues relating to material flow and information flow under the current biomass energy development model. This study focused on the agriculture and forestry waste power generation industry. Taking the biomass industry in Nanjing, Suqian, and Yancheng as examples, the study selected six stakeholder groups involved in the industry and conducted field investigations by using semi-open interviews and questionnaires. The research mainly applied social network analysis methods, combined with UCINET software, to draw a network diagram of the stakeholder relationships and to quantitatively analyze stakeholder centrality and overall network density. The results revealed that (1) the biomass enterprises had the highest centrality in the overall network, which played a vital role in the construction of the overall network; (2) the farmers were positioned at the outer fringes of the industrial social network and their information acquisition capabilities and degree of control over the network were the lowest; and (3) the overall network density was low, which showed that the connections between stakeholders were not close enough to support the circulation of material and information in the overall network.

ПРАГМАТИЧНИЙ ПОТЕНЦІАЛ НАТИВНОЇ РЕКЛАМИ: ФОРМИ, ТРЕНДИ (НА ПРИКЛАДІ НАТИВНОГО КОНТЕНТУ В СОЦІАЛЬНІЙ МЕРЕЖІ FACEBOOK ЩОДО БРЕНДА ФАКУЛЬТЕТУ ЖУРНАЛІСТИКИ ЗНУ)

2020 ◽  
pp. 113
Author(s):  
V. Kovpak ◽  
N. Trotsenko
Keyword(s):  
Social Network ◽  
Professional Training ◽  
Educational Process ◽  
Communication Strategy ◽  
Student Centered ◽  
Teacher Student ◽  
The Social ◽  
National University ◽  
High Level ◽  
The Impact

<div><p><em>The article analyzes the peculiarities of the format of native advertising in the media space, its pragmatic potential (in particular, on the example of native content in the social network Facebook by the brand of the journalism department of ZNU), highlights the types and trends of native advertising. The following research methods were used to achieve the purpose of intelligence: descriptive (content content, including various examples), comparative (content presentation options) and typological (types, trends of native advertising, in particular, cross-media as an opportunity to submit content in different formats (video, audio, photos, text, infographics, etc.)), content analysis method using Internet services (using Popsters service). And the native code for analytics was the page of the journalism department of Zaporizhzhya National University on the social network Facebook. After all, the brand of the journalism department of Zaporozhye National University in 2019 celebrates its 15th anniversary. The brand vector is its value component and professional training with balanced distribution of theoretical and practical blocks (seven practices), student-centered (democratic interaction and high-level teacher-student dialogue) and integration into Ukrainian and world educational process (participation in grant programs).</em></p></div><p><em>And advertising on social networks is also a kind of native content, which does not appear in special blocks, and is organically inscribed on one page or another and unobtrusively offers, just remembering the product as if «to the word». Popsters service functionality, which evaluates an account (or linked accounts of one person) for 35 parameters, but the main three areas: reach or influence, or how many users evaluate, comment on the recording; true reach – the number of people affected; network score – an assessment of the audience’s response to the impact, or how far the network information diverges (how many share information on this page).</em></p><p><strong><em>Key words:</em></strong><em> nativeness, native advertising, branded content, special project, communication strategy.</em></p>

Mobile-Friendly Dashboards to Display Associations Between Cited-by and Similar Journals on Mobile Health Research: A Bibliometric Study (Preprint)

2018 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Hsien-Yi Wang ◽  
Yang Shao ◽  
Willy Chou
Keyword(s):  
Social Network ◽  
Mobile Health ◽  
Health Research ◽  
Similarity Criteria ◽  
Network Density ◽  
Deep Insight ◽  
Density Indices ◽  
The Subject ◽  
The Given ◽  
Insight Into

BACKGROUND Researchers often spend a great deal of time and effort retrieving related journals for their studies and submissions. Authors often designate one article and then retrieve other articles that are related to the given one using PubMed’s service for finding cited-by or similar articles. However, to date, none present the association between cited-by and similar journals related to a given journal. Authors need one effective and efficient way to find related journals on the topic of mobile health research. OBJECTIVE This study aims (1) to show the related journals for a given journal by both cited-by and similarity criteria; (2) to present the association between cited-by and similarity journals related to a given journal; (3) to inspect the patterns of network density indices among clusters classified by social network analysis (SNA); (4) to investigate the feature of Kendall's coefficient(W) of concordance. METHODS We obtained 676 abstracts since 2013 from Medline based on the keywords of ("JMIR mHealth and uHealth"[Journal]) on June 30, 2018, and plotted the clusters of related journals on Google Maps by using MS Excel modules. The features of network density indices were examined. The Kendall coefficient (W) was used to assess the concordance of clusters across indices. RESULTS This study found that (1) the journals related to JMIR mHealth and uHealth are easily presented on dashboards; (2) a mild association(=0.14) exists between cited-by and similar journals related to JMIR mHealth and uHealth; (3) the median Impact Factor were 3.37 and 2.183 based on the representatives of top ten clusters grouped by the cited-by and similar journals, respectively; (4) all Kendall’s coefficients(i.e., 0.82, 0.89, 0.92, and 0.75) for the four sets of density centrality have a statistically significant concordance (p < 0.05). CONCLUSIONS SNA provides deep insight into the relationships of related journals to a given journal. The results of this research can provide readers with a knowledge and concept diagram to use with future submissions to a given journal in the subject category of Mobile Health Research. CLINICALTRIAL Not available

An Iranian academic social network is coming (Preprint)

2019 ◽  
Author(s):  
Mohammad Dehghani ◽  
Akhondzadeh Shahin ◽  
Mesgarpour Bita ◽  
Ferdousi Reza
Keyword(s):  
Social Network ◽  
Developing Country ◽  
The People ◽  
Academic Social Network ◽  
The Impact

UNSTRUCTURED Iran has faced severe sanctions in recent years from some countries. Due to the dependence of the Iranian health industry on government payments, the health of the people in this country has suffered a lot. One of the solutions for Iran's sanctions to reduce the impact of sanctions on health is to rely on domestic researchers, but researchers in Iran are having problems. One way to reduce researchers' problems is to use the National Academic Social Network. This article describes the steps of setting up an academic social network in a developing country in four stages.

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

2021 ◽  
pp. 193758672110137
Author(s):  
Jacqueline M. Burgette ◽  
Jacquelin Rankine ◽  
Alison J. Culyba ◽  
Kar-Hai Chu ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Best Practices ◽  
Health Outcomes ◽  
Organizational Behavior ◽  
Network Data ◽  
Egocentric Networks ◽  
Covariate Selection ◽  
Social Network Modeling ◽  
Egocentric Network ◽  
The Impact

Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health.

scholarly journals Spanish communication academia: Scientific productivity vs. Social activity

Comunicar ◽  
2013 ◽  
Vol 21 (41) ◽  
pp. 61-70 ◽  
Author(s):  
Cristóbal Casanueva-Rocha ◽  
Francisco-Javier Caro-González
Keyword(s):  
Social Network ◽  
Social Factors ◽  
Social Activity ◽  
Scientific Productivity ◽  
Scientific Activity ◽  
Redes Sociales ◽  
International Scope ◽  
The Social ◽  
La Red ◽  
The Impact

At a time when academic activity in the area of communication is principally assessed by the impact of scientific journals, the scientific media and the scientific productivity of researchers, the question arises as to whether social factors condition scientific activity as much as these objective elements. This investigation analyzes the influence of scientific productivity and social activity in the area of communication. We identify a social network of researchers from a compilation of doctoral theses in communication and calculate the scientific production of 180 of the most active researchers who sit on doctoral committees. Social network analysis is then used to study the relations that are formed on these doctoral thesis committees. The results suggest that social factors, rather than individual scientific productivity, positively influence such a key academic and scientific activity as the award of doctoral degrees. Our conclusions point to a disconnection between scientific productivity and the international scope of researchers and their role in the social network. Nevertheless, the consequences of this situation are tempered by the nonhierarchical structure of relations between communication scientists. En un momento en que la actividad académica en el ámbito de la comunicación se valora principalmente por el impacto de las revistas y los medios de comunicación científica y por la productividad de los investigadores, surge la cuestión de si los factores sociales pueden condicionar la actividad científica con la misma fuerza que estos elementos objetivos. Esta investigación analiza la influencia de la productividad científica y de la actividad social en el ámbito de la comunicación. Se ha identificado la red social de los investigadores de comunicación a partir de las tesis doctorales. Para los 180 investigadores más activos en los tribunales de tesis se ha calculado su producción científica. Se utiliza el análisis de redes sociales para estudiar las relaciones que se producen en los tribunales de tesis doctorales. Los resultados muestran que los factores sociales influyen positivamente en una actividad académica y científica tan relevante como la obtención del grado de doctor, mientras que la productividad científica individual no lo hace. Como conclusiones cabe señalar que existe una desconexión entre la productividad científica y la proyección internacional de los investigadores y su papel en la red social. Las implicaciones de este hecho están matizadas por una estructura no jerarquizada de las relaciones entre los científicos de comunicación.

The impact of the social network, stigma and empowerment on the quality of life in patients with schizophrenia

2011 ◽  
Vol 26 (1) ◽  
pp. 28-33 ◽  
Author(s):  
I. Sibitz ◽  
M. Amering ◽  
A. Unger ◽  
M.E. Seyringer ◽  
A. Bachmann ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Social Network ◽  
Structural Equation ◽  
Internalized Stigma ◽  
Stigma Reduction ◽  
Equation Modelling ◽  
The Social ◽  
Recovery Approach ◽  
The Impact

Abstract:Objective:The quality of life (QOL) of patients with schizophrenia has been found to be positively correlated with the social network and empowerment, and negatively correlated with stigma and depression. However, little is known about the way these variables impact on the QOL. The study aims to test the hypothesis that the social network, stigma and empowerment directly and indirectly by contributing to depression influence the QOL in patients with schizophrenia and schizoaffective disorders.Method:Data were collected on demographic and clinical variables, internalized stigma, perceived devaluation and discrimination, empowerment, control convictions, depression and QOL. Structural equation modelling (SEM) was applied to examine the impact of the above-mentioned constructs on QOL.Results:The influences of the social network, stigma, empowerment and depression on QOL were supported by the SEM. A poor social network contributed to a lack of empowerment and stigma, which resulted in depression and, in turn, in poor QOL. Interestingly, however, the social network and stigma did not show a direct effect on QOL.Conclusions:Following a recovery approach in mental health services by focusing on the improvement of the social network, stigma reduction and especially on the development of personal strength has the potential to reduce depression in patients with psychosis and improving their QOL.

The Impact of Social Network Marketing on University Students' Purchase Intention and How It Is Affected of Consumer Engagement

2020 ◽  
Vol 6 (2) ◽  
pp. 87-91
Author(s):  
Hartiwi Prabowo ◽  
Rini Kurnia Sari ◽  
Stephanie Bangapadang
Keyword(s):  
Social Network ◽  
Data Collection ◽  
University Students ◽  
Purchase Intention ◽  
Research Method ◽  
Quantitative Research ◽  
Consumer Engagement ◽  
Network Marketing ◽  
Consumer Purchase Intention ◽  
The Impact

The research conducted is to know the impact of social network marketing on consumer purchase intention and consumers who become research are active students at private universities in Jakarta, and how social network marketing also affect consumer engagement (as moderate variable). The research method used in this research is quantitative research method. A method of data collection used in this research is a questionnaire distributed to 119 university students. The results of this study showed that social network marketing has a strong and significant impact oncustomer engagement, customer engagementhas a strong and significant impact on consumer purchase intention, social network marketing has a strong and significant impact consumer purchase intention, and also there is a significant impact from social network marketing on consumer purchase intention through consumer engagement.

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

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