Results Of Social Network Analysis Research Articles

Actors, the Actor Network and Their Impact on Public Space Management: Social Network Analysis as a Method

Public space is an essential element of cities as it offers several benefits. Due to its significance, there is much discussion of its design, development and use. Although equally important, the management of public space lacks attention in academia and policy. This is regrettable given concerns about the quality of public space and calls for substantial changes in public space management. Moreover, while multi-actor involvement in public space management has become popular, its impact has been less studied. This paper attempts to fill the research gap by presenting an empirical study on Görlitzer Park in Berlin, Germany. Thereby, it focuses on multi-actor involvement in public space management. Based on the results of social network analysis, the paper provides a valuable insight into the actors involved, the actor network and their impact on public space management. Most importantly, the paper argues that the structure of the actor network matters for managing public space. This suggests that improving the actor network can be a key to enhancing the quality of public space. The paper also discusses how to improve communication between actors to better manage public space.

Exploring Current and Future Research Trends on Safety Climate in Construction Projects

Clients, consultants, and contractors in the construction industry can benefit from a safety climate by obtaining knowledge of attitudes and perceptions for better safety outcomes. However, the current and future research trends for safety climate in construction projects have not yet been fully examined. In order to solve this problem, a systematic review of previous research findings using a social network approach must be conducted, and future trends for the safety climate in building projects must be identified. Sixty-three peer-reviewed articles were gathered to study the literature on safety climate in construction projects. A social network approach was employed using VOS viewer to look at the connections between the researchers and the article's keywords. Based on the frequency of researcher collaboration and the connections between the keywords used in the publications, five research groups and four keyword themes were identified using the social network analysis results. The scholars received substantial implications and insights on the state of the research and future directions, which helped create a focused development strategy for safety climate in construction projects. The scope of this study is limited to publications on the subject matter from 2000 to 2021, which provided the base for the current and future research trends on safety climate in construction projects. The findings from this social network analysis revealed that studies on safety climate are more skewed to the organizational level than the project level. In developing countries like Tanzania, studies are scanty. Also, different levels of decision-making within the organization and within construction projects, call for different safety factors whereby the current studies have slightly considered such a fact. Furthermore, it is the right time now for researchers to focus on developing a safety climate maturity framework for construction projects.

Exploring Current and Future Research Trends on Safety Climate in Construction Projects

Both clients, consultants, and contractors in the construction industry can benefit from a safety climate by obtaining knowledge of attitudes and perceptions for better safety outcomes. However, the current and future research trends for safety climate in construction projects have not yet been fully examined. In order to solve this problem, a systematic review of previous research findings using a social network approach must be conducted, and future trends for the safety climate in building projects must be identified. Sixty-three peer-reviewed articles were gathered to study the literature on safety climate in construction projects. A social network approach was employed using VOS viewer to look at the connections between the researchers and the article's keywords. Based on the frequency of researcher collaboration and the connections between the keywords used in the publications, five research groups and four keyword themes were identified using the social network analysis results. The scholars received substantial implications and insights on the state of the research and future directions, which helped create a focused development strategy for safety climate in construction projects. The scope of this study is limited to publications on the subject matter from 2000 to 2021, which provided the base for the current and future research trends on safety climate in construction projects. The findings from this social network analysis revealed that studies on safety climate are more skewed to the organizational level than the projects level. In developing countries like Tanzania, studies are scanty. Also, different levels of decision-making within the organization and within construction projects, call for different safety factors whereby the current studies have slightly considered such a fact. Furthermore, it is the right time now for researchers to focus on developing a safety climate maturity framework for construction projects.

Public Opinion Disclaimer on Enforcement of Public Activity Restrictions (PPKM): A Content Analysis

The high increase in Covid-19 in Indonesia is a concern for many parties. Thus, the policy taken by the government is the Imposition of Emergency Community Activity Restrictions (PPKM). The policy reaped the pros and cons in the community. This study aims to analyze the community's perspective (netizens) expressed through social media in assessing the implementation of emergency PPKM. This study utilizes qualitative descriptive methods and uses secondary data through social media in the form of Twitter and is analyzed using a content analysis approach. This study focuses on analyzing the trend of public perception (citizens) regarding the topic of conversation about rejection and demonstration (Tolak & Demo) of PPKM through the results of Social Network Analysis (SNA) obtained through Drone Emprit. The results show that most PPKM policies get widespread rejection in the community because the impacts are mostly negative, such as getting poorer people because of limited access to work. The domination of the denial was driven by pro-opposition accounts that campaigned against PPKM in the form of demonstrations. The contribution of this research shows that Twitter can be a space for public participation in responding to government policies (democracy). This research has limitations because it only focuses on Twitter, so further studies need to look at the use of other social media such as Instagram and YouTube.

Research on the relevant characteristics of occupational disease diagnostic criteria in China from the perspective of social network analysis

Objective: To analyze the normative references in occupational disease diagnostic criteria and explore the relevant characteristics of occupational disease diagnostic criteria in China, aiming to provide the basis for improving the system of occupational disease diagnostic criteria. Methods: From January to March 2021, the text and normative references of occupational disease diagnostic criteria were retrieved, and the database was established with Excel 2013, and the data were analyzed with SPSS 22.0 software and Ucinet 6.0 software. The distributions of normative references were statistically analyzed by frequency analysis. The network density and centrality of occupational disease diagnostic criteria were analyzed by social network analysis. The network topology of relevant relationship network of occupational disease diagnostic criteria was drawn. Results: Among 115 occupational disease diagnostic criteria, 105 criteria cited 146 normative references, a total of 431 times. The normative documents cited more frequently were GB/T 16180 (91 times), GBZ 76 (37 times) and GBZ 73 (23 times). The number of normative references in GBZ 188 and GBZ/T 238 was large, 87 and 13 respectively. The results of social network analysis showed that the network density was 0.0281. The relative degree centrality was 3.089, and the top three criteria were GB/T 16180 (55.152), GBZ 188 (53.333) and GBZ 76 (23.636). The closeness centrality was 7.773 and the top three criteria were GB/T 16180 (8.735), GBZ 188 (8.730) and GBZ 76 (8.333). The betweenness centrality was 0.824 and the top three criteria were GBZ 188 (38.836), GB/T 16180 (38.301) and GBZ/T 238 (10.407). And GB/T 16180, GBZ 188, GBZ 76, GBZ 73 and GBZ 59 were at the core of the network topology of relationship network of occupational disease diagnostic criteria. Conclusion: The network density of occupational disease diagnostic criteria in China is low, and the relevant relationship is not close. GB/T 16180, GBZ 188, and GBZ 76 are node standards and should be the focus of the next standard implementation tracking.

Developing a plugged-in class observation protocol in high-school blended STEM classes: Student engagement, teacher behaviors and student-teacher interaction patterns

To investigate how high school students engaged in blended STEM courses, this study developed a new digital observation protocol, Classroom Observation Protocol for Interactive Engagement in STEM (COPIE-STEM) to observe patterns of behaviors between teachers and students. We also distinguished the data according to two common types of STEM programs (i.e., mainly hands-on versus mainly lecture). In the present study, a case study design was used to validate this COPIE-STEM. It was able to code eight teacher behaviors classified into three categories: Student-Centered, Teacher-Centered, and Transitional activities; and 23 student behaviors of Interactive, Constructive, Active, and Passive engagement based on the ICAP framework (Chi, 2009). Interrater reliability was satisfactory. Observational data collected from 25 classes showed that students were most engaged in Active activities and less in Constructive and Interactive activities. When students engaged in Interactive, Constructive and Active activities, teachers' activities were mostly Student-Centered. In contrast, when students showed Passive Engagement behaviors, teachers were mostly engaged in Teacher-Centered educational activities. Using social network analysis to process each student's data, we created graphs of student-teacher associations and found four types of student-teacher behavior associations: Constructive with Extensive Interaction (C-EI), Constructive with Few Interactions (C-FI), Constructive Engagement (CE) and Active Engagement (AE). In hands-on courses, there were more C-EI patterns, while in lecture courses, CE and C-FI patterns dominated. The results of social network analysis showed there was more and diverse co-occurrence of teacher-student behaviors in the C-EI patterns, and students' behavioral nodes were more closely related to teachers' behavioral nodes in the C-EI patterns in both hands-on and lecture courses. Finally, student interaction was limited and constrained in the lecture courses, whereas in the hands-on courses, student interaction was more frequent and less constrained by seating arrangement. The findings suggest that high school teachers should design tasks that allow students to stay engaged in deeper learning.

Application of social network analysis for determining the suitable social media influencers

Virtual Hotel Operator (VHO) trend is growing rapidly, especially in Indonesia. Two of the most popular VHO in Indonesia are OYO and RedDoorz, both have been competing to attain the first position. Both OYO and RedDoorz have their own social media marketing strategies. For example, OYO persuades other conventional hotels to collaborate and use the OYO platform in their businesses. On the other hand, RedDoorz was recorded as the most visited Virtual Hotel Operator Platform in 2019, based on the data of Konsumen Jakpat 2019. OYO and RedDoorz also utilize social media to promote their services such as Instagram and Twitter. For advertising their businesses in social media, OYO and RedDoorz often use some social media influencers or known as influencer social media marketing. Influencers should be able to effectively deliver the messages and influence people’s decisions to use the products or services they advertise. This study aims to further explore the social media marketing strategy employed by OYO and RedDoorz. The results of Social Network Analysis by using “oyoindonesia” and ‘reddoorz’ as keywords in social media Twitter showed that RedDoorz has a bigger social network and more users involved in spreading their information than OYO. On the other hand, OYO's official account on Twitter is more efficient in performing its function as marketing media.

Social network analysis in pigs: impacts of significant dyads on general network and centrality parameters

In general, one animal is considered dominant over another animal if it has won more fights than its opponent. Whether this difference in won and lost fights is significant is neglected in most studies. Thus, the present study evaluates the impact of two different calculation methods for dyadic interactions with a significant asymmetric outcome on the results of social network analysis regarding agonistic interactions of pigs in three different mixing events (weaned piglets, fattening pigs and gilts). Directly after mixing, all animals were video recorded for 17 (fattening pigs, gilts) and 28 h (weaned piglets), documenting agonistic interactions. Two calculation methods for significant dyads, that is, dyadic interactions with a clear dominant subordinate relationship in which one animal has won significantly more fights than its encounter, were proposed: pen individual limits were calculated by a sign test considering the differences of won and lost fights of all dyadic interactions in each pen; dyad individual limits were determined by a one-sided sign test for each individual dyad. For all data sets (ALL, including all dyadic interactions; PEN or DYAD, including only significant dyads according to pen or dyad individual limits), networks were built based on the information of initiator and receiver with the pigs as nodes and the edges between them illustrating attacks. General network parameters describing the whole network structure and centrality parameters describing the position of each animal in the network were calculated. Both pen and dyad individual limits revealed only a small percentage of significant dyads for weaned piglets (12.4% or 8.8%), fattening pigs (4.2% or 0.6%) and gilts (3.6% or 0.4%). The comparison between the data sets revealed only high Spearman’s rank correlation coefficients (rS) for the density, that is, percentage of possible edges that were actually present in the network, whereas the centrality parameters showed only moderate rS values (0.37 to 0.75). Thus, the rank order of the animals changed due to the exclusion of insignificant dyads, which shows that the results obtained from social network analysis are clearly influenced if insignificant dyads are excluded from the analyses. Due to the fact that the pen individual limits consider the overall level of agonistic interactions within each pen, this calculation method should be preferred over the dyad individual limits. Otherwise, too many animals in the group became isolated nodes with zero centrality for which no statement about their position within the network can be made.

Winning the war on terror: using social networking tools and GTD to analyse the regularity of terrorism activities

In order to study the spatiotemporal characteristics and activity patterns of terrorism attacks in China, so as to make effective counter-terrorism strategies, two kinds of different intelligence sources were analysed by means of social network analysis and mathematical statistics. Firstly, using the social network analysis tool ORA, we build a terrorists' activity meta-network for the text information, extracting the four categories of person, places, organisations and time, and analyse the characteristics of the key nodes of the network, then the meta-network is decomposed into person-organisation and person-location, organisation-location, organisation-time four binary subnets to analyse the temporal and spatial characteristics of terrorist activities. Then, using GTD data set to analyse the characteristics of China's terrorist attacks from 1989 to 2015, the geo-spatial distribution and time distribution of terrorist events are summarised. Combined with the data visualisation method, the previous results of social network analysis using open source text are verified and compared; finally we put forward some suggestions on counter-terrorism prevention strategy in China.

Social Networks of Children With Developmental Language Disorder in Inclusive Preschool Programs.

This study takes an ecological approach to examine how children with developmental language disorder (DLD) interact with their classmates within early childhood special education (ECSE) inclusive classrooms. Participants were 124 children with DLD, 56 children with other disabilities, and 247 typically developing children (Mage =52.42months, SD=6.27) from 56 ECSE inclusive classrooms. Results of social network analysis showed that children with DLD had significantly smaller peer social networks and were more likely to be isolated. Children tended to interact with peers with the same DLD status. These effects of children's DLD status were above and beyond the effects of children's social pragmatics skills.

Trends in the Korean Government Support for Marine Biotechnology R&D Investment and Its Implication

This study seeks to analyze trends in Korean government support for marine biotechnology R&D investment. The quantitative analysis proposed in this paper provides useful data to inform government policy making decisions in relation to R&D investment in the marine biotechnology. Data used in this study is from NTIS (National Science & Technology Information Service) DB. Empirical results indicate that national R&D investment in the area of marine biotechnology has recently been increased in terms of the number of R&D programs and the amount of R&D investment. The results also show that the R&D programs in the area of marine biotechnology involve various Ministries of the government of Korea, although the Ministry of Oceans and Fisheries (MOF) performed a key role in the R&D investment in the marine biotechnology. Finally, the results of social network analysis in this study reveals that most of the R&D programs in the area of marine biotechnology seeks the development of new materials using unique features of marine bioresources.

Face-to-face Contacts, Facebook Connections and Academic Support: Adolescents’ Networks between and across Gender and Culture in Finland

The purpose of the present study is to investigate gender and cultural background similarities among adolescents’ patterns of networking within a multicultural Finnish school. The participants consisted of 109 seventh to ninth graders who represented three cultural groups: major-culture ( n = 50; 46%), bi-culture ( n = 26; 24%) and minor-culture ( n = 33; 30%). A social networking questionnaire was administered to trace face-to-face, Facebook and academic support networks within each classroom. Results of social network analysis revealed that bi-culture students were culturally the most open to network with their classmates in face-to-face contacts, Facebook connections and peer academic support. Female students were more inclined to use Facebook to connect with other female classmates, whereas males were equally likely to connect with either gender via Facebook. No gender similarities were found in academic support network although female students were more apt to engage in academic support than their male counterparts.

Peer selection and influence on children’s reading skills in early primary grades: a social network approach

This longitudinal study from Grades 1 to 4 investigated (a) the extent to which children select peers based on similarity in reading skills and (b) the extent to which children are influenced by the level of their peers’ reading skills. The sample consisted of 1003 Finnish children in Grades 1–4, for whom reading fluency and comprehension were assessed. The same children were interviewed about their self-concept of reading ability at the end of kindergarten. The results of social network analysis showed that children had a tendency to choose new friends based on earlier similarity in reading fluency but not in reading comprehension. Furthermore, children became more similar to their friends in reading fluency and reading comprehension. Children with a high self-concept of reading ability and high reading fluency were particularly likely to be positively influenced by peers with high reading fluency.

A Social Network Approach to Examine K-12 Educational Leaders’ Influence on Information Diffusion on TWitter

This study investigated the relationship between the leader's gender, leadership position. Twitter use, and influence on information diffusion in the communication network on Twitter. We collected the 30,200 latest tweets of 151 active Twitter users who held educational leadership positions. Results of social network analysis and multiple regression analyses suggest a gender inequality in the leader's influence on information diffusion in the network. Findings also indicate no significant relationship between leadership position (district vs. building) and a leader's influence in the network. Moreover, Twitter following was positively associated with the leader's influence in the network, whereas the number of followers, weekly tweets, and the time of Twitter account creation did not predict the leader's influence. Practical implications on how leaders use Twitter to disseminate information are discussed.

Social network analysis and supply chain management

<p>This paper deals with social network analysis and how it could be integrated within supply chain management from a decision-making point of view. Even though the benefits of using social analysis have are widely accepted at both academic and industry/services context, there is still a lack of solid frameworks that allow decision-makers to connect the usage and obtained results of social network analysis – mainly both information and knowledge flows and derived results- with supply chain management objectives and goals. This paper gives an overview of social network analysis, the main social network analysis metrics, supply chain performance and, finally, it identifies how future frameworks could close the gap and link the results of social network analysis with the supply chain management decision-making processes. </p>

Evaluating change in interagency collaboration of a state-level interagency education team: a social network approach within a utilization-focused framework

Improving collaboration among organizations is a goal of interagency educational teams, but collaboration is typically thought of as an ambiguous concept that cannot be quantified. To assess the collaborative strength of a state-level interagency team in a Midwestern state, team members completed the Levels of Collaboration Scale (Frey et al., American Journal of Evaluation 27(3):383, 2006) for three consecutive years. The team members then utilized the results of the social network analysis to support and sustain collaborative efforts. Findings revealed that the number of partnerships increased and the depth of collaboration expanded across the 3 years. Social network analysis results were utilized to develop collaboration strategies, thus influencing the expansion of the network across the 3-year period.

Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain

Social network analysis was used in combination with techniques for detection of temporal-spatial clusters to identify operations at high risk of receiving or dispatching pigs, from January through December 2005, in the Spanish province of Salamanca. The temporal-spatial structure of the network was explicitly analyzed to estimate the statistical significance of observed clusters. Significant (P<0.01) temporal-spatial clusters identified were grouped into two compartments based on the nature and extent of the contacts among operations within the clusters. One of the compartments was identified from January through April, included a high proportion of extensive farms (0.39), and was likely to be related with the production and trade of Iberian pigs. The other compartment encompassed a smaller proportion of extensive farms (0.11: P<0.01), took place from May through December, and was probably related to intensive production systems. Analysis of a sub-section of the network, which was selected based on the administrative division of Spain, yielded to the identification of a different set of clusters, showing that results of social network analysis may be sensitive to the extension of the information used in the analysis. The approach presented here will be useful for the implementation of differential surveillance, prevention, and control strategies at specific times and locations, which will aid in the optimization of human and financial resources.