Topic
Social network analysis
About: Social network analysis is a research topic. Over the lifetime, 8616 publications have been published within this topic receiving 210651 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: Wang et al. as discussed by the authors analyzed the characteristics and evolution of project-based collaborative networks between owners and contractors in the construction industry by using social network analysis (SNA) and network motif analysis (NMA) method.
Abstract: In the project-based construction industry, organizations build collaborative relationships through specific projects. The owners and contractors who are the key project stakeholders have gradually formed a complex project-based industry-level collaborative network in many different projects, closely related to knowledge exchange and industry development. Based on the data set of the National Quality Engineering Award (NQEA) projects in China from 2013 to 2021, we empirically analyze the characteristics and evolution of project-based collaborative networks between owners and contractors in the construction industry by using social network analysis (SNA) and network motif analysis (NMA) method. The results show that (1) the owner–contractor collaborative network exhibits small-world network characteristics. The island effect caused by small groups in the network makes the overall connectivity of the network low. During the study period, the collaborative network became more compact. (2) State-owned construction companies, such as China Construction Third Engineering Bureau Corporation Limited, China Construction Eighth Engineering Bureau Corporation Limited, and China Construction Second Engineering Bureau Corporation Limited, with high degree centrality and betweenness centrality, are the core companies in the collaborative network. In China, state-owned construction enterprises are favored by owners and have established collaborative relationships with many owners and contractors. (3) There are two local collaborative patterns in the collaborative network: motif and anti-motif. Motifs include some triangle-based tight collaborative patterns, while anti-motifs involve some loose binary collaborative patterns. The results help understand the structure and evolution of the industry-level collaborative relationship network between owners and contractors and can provide references for owners and contractors to develop relationship cultivation strategies more effectively.
TL;DR: A scoping review of published research was conducted using Medline, ABI Inform and PsycInfo databases in 2022 as mentioned in this paper , which systematically mapped the social network analysis research conducted both within (intra) and between (inter) organizations in healthcare settings, to understand the prevalence of the network intervention types enacted thus far and to identify existing gaps in the literature.
Abstract:
Objectives
Social network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social networks to embrace new practices that influence their lives. Social network theory centers on the role of relationships in the creation, spread, and utilization of knowledge. The aims of this scoping review were to systematically map the social network analysis research conducted both within (intra) and between (inter) organizations in healthcare settings, to understand the prevalence of the network intervention types enacted thus far, and to identify existing gaps in the literature. As social network analysis could be conceptualized and operationalized in a variety of ways within health research, a scoping review of social network analysis is warranted.Method
A scoping review of published research was conducted using Medline, ABI Inform and PsycInfo databases in 2022. We searched terms synonymous with social network analysis, knowledge transfer and organization. Studies eligible for inclusion included theoretical or conceptual papers and quantitative, qualitative and mixed-methods studies in order to consider different measures and aspects of social network analysis and knowledge transfer. Articles were excluded if they did not address the transfer of knowledge in a healthcare organizational context, focused on the spread of disease, discussed the use of social media in public spheres or focused on molecular networks. Data was abstracted on article characteristics, study design, study location, setting of the intervention, professional role(s) observed, how social network theory/analysis was utilized, whether it observed inter and/or intra organizational knowledge transfer, what knowledge was transferred, the type(s) of network intervention observed or proposed, and theories, models or frameworks mentioned.Results
We included 95 studies in this review: 8 were theoretical studies, 43 were quantitative, 15 were qualitative and 29 utilized mixed methods. The use of sociometric surveys or questionnaires was the most common method used. Most of the studies were set in hospitals and its units. Although most studies looked at multiple professions as they aimed to conduct whole network analyses of the settings, the professionals most focused on were physicians. The types of knowledge studied by the researchers included primarily practitioner knowledge, process knowledge and resource knowledge. There was a relatively even split between studies that focused on networks between organizations (inter), within organizations (intra) and studies that looked at both. Most researchers incorporated social network theories and analysis in the conceptual framework, data collection, data analysis, and interpretation of findings phases. Network interventions utilizing segmentation approaches were less prevalent in the healthcare context.Conclusions
This review contributes to the management and knowledge translation literatures as it provides an overview of the existing healthcare focused publications that have referred to social network analysis in the organizational context. It also outlines gaps in where further research may be conducted. A future study may include the studies that were excluded at the full text stage as they were not situated in healthcare settings to provide a more comprehensive overview of the influence of social networks on knowledge transfer within and amongst organizations. Few studies used social network analysis to study inter-professional collaboration even though many observed interactions between different professions, thus indicating an area of further study. The review also outlined the diversity in the ways social network theories and analysis may be utilized in research and moving knowledge whether it be as a theoretical framework, a method, to guide analysis or interpretation or as an intervention.12 Dec 2022
31 Oct 2022
TL;DR: Wang et al. as discussed by the authors used a long short-term memory network (LSTM) model to analyze Chinese sentiment in social media reviews using a web crawler and was cleaned with Pandas.
Abstract: Network public opinion analysis is obtained by a combination of natural language processing (NLP) and public opinion supervision, and is crucial for monitoring public mood and trends. Therefore, network public opinion analysis can identify and solve potential and budding social problems. This study aims to realize an analysis of Chinese sentiment in social media reviews using a long short-term memory network (LSTM) model. The dataset was obtained from Sina Weibo using a web crawler and was cleaned with Pandas. First, Chinese comments regarding the legal sentencing in of Tangshan attack and Jiang Ge Case were segmented and vectorized. Then, a binary LSTM model was trained and tested. Finally, sentiment analysis results were obtained by analyzing the comments with the LSTM model. The accuracy of the proposed model has reached approximately 92%.