Social Network Analysis for Analyzing Groups as Complex Systems

Quinn, Andrew; Woehle, Ralph; Tiemann, Kathleen A.

doi:10.1080/01488376.2012.721278

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The NodeXL pro program [34][35][36][37] was used for data collection. This tool was used to download, on 26 September 2022, the tweets and interactions generated from 19 September to 25 September 2022.…”

Section: Methodsmentioning

confidence: 99%

International Youth Movements for Climate Change: The #FridaysForFuture Case on Twitter

Castillo

Hernández

2022

Sustainability

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Agenda 2030 and Sustainable Development Goals (SDGs) are critical pieces of climate change communication. #FridaysForFuture (FFF) is one of the movements with the most coverage. This paper analyzes the network structure generated in Twitter by the interactions created by its users about the 23 September 2022 demonstrations, locates the most relevant users in the conversation based on multiple measures of intermediation and centrality of Social Network Analysis (SNA), identifies the most important topics of conversation regarding the #FridaysForFuture movement, and checks if the use of audio-visual content or links associated with the messages have a direct influence on the engagement. The NodeXL pro program was used for data collection and the different structures were represented using the Social Network Analysis method (SNA). Thanks to this methodology, the most relevant centrality measures were calculated: eigenvector centrality, betweenness centrality as relative measures, and the levels of indegree and outdegree as absolute measures. The network generated by the hashtag #FridaysforFuture consisted of a total of 12,136 users, who interacted on a total of 37,007 occasions. The type of action on the Twitter social network was distributed in five categories: 16,420 retweets, 14,866 mentions in retweets, 3151 mentions, 1584 tweets, and 986 replies. It is concluded that the number of communities is large and geographically distributed around the world, and the most successful accounts are so because of their relevance to those communities; the action of bots is tangible and is not demonized by the platform; some users can achieve virality without being influencers; the three languages that stood out are English, French, and German; and climate activism generates more engagement from users than the usual Twitter engagement average.

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Section: Methodsmentioning

confidence: 99%

International Youth Movements for Climate Change: The #FridaysForFuture Case on Twitter

Castillo

Hernández

2022

Sustainability

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“…The whole network approach of our study meets the call for a complex systems approach in combination with social network analysis to examine the functioning of the network as a whole, and especially in the field of child and youth services (Benham-Hutchins and Clancy, 2010; Colvin and Miller, 2020; Kitson et al. , 2018; Mischen and Jackson, 2008; Morçöl and Wachhaus, 2009; Quinn et al. , 2012; Stevens and Cox, 2008; Stevens and Hassett, 2007).…”

Section: Introductionmentioning

confidence: 95%

“…By examining and comparing the integration of the networks, we gain more insight in which organizations and sectors do and do not collaborate, and whether organizations with a gatekeeper function are able to refer clients between organizations in the network due to their structural position in the network. The whole network approach of our study meets the call for a complex systems approach in combination with social network analysis to examine the functioning of the network as a whole, and especially in the field of child and youth services (Benham-Hutchins and Clancy, 2010;Colvin and Miller, 2020;Kitson et al, 2018;Mischen and Jackson, 2008;Morç€ ol and Wachhaus, 2009;Quinn et al, 2012;Stevens and Cox, 2008;Stevens and Hassett, 2007). Indeed, by examining the multilateral relations rather than focusing on individual organizations and their direct relations only, it is possible to understand how processes such as client referral generate collective outcomes (Berthod et al, 2017;Colvin and Miller, 2020;Provan et al, 2007;Van der Ham et al, 2020).…”

Section: Introductionmentioning

confidence: 98%

Cross-sectoral collaboration: comparing complex child service delivery systems

Blanken

Mathijssen

Nieuwenhuizen

et al. 2022

JHOM

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PurposeTo help ensure that children with social and behavioral health problems get the support services they need, organizations collaborate in cross-sectoral networks. In this article, the authors explore and compare the structure of these complex child service delivery networks in terms of differentiation (composition) and integration (interconnection). In particular, the authors investigate the structure of client referral and identify which organizations are most prominent within that network structure and could therefore fulfill a coordinating role.Design/methodology/approach The authors used a comparative case study approach and social network analysis on three interorganizational networks consisting of 65 to 135 organizations within the Dutch child service delivery system. Semi-structured interviews with the network managers were conducted, and an online questionnaire was sent out to the representatives of all network members.Findings The networks are similarly differentiated into 11 sectors with various tasks. Remarkably, network members have contact with an average of 20–26 organizations, which is a fairly high number to be handled successfully. In terms of integration, the authors found a striking diversity in the structures of client referral and not all organizations with a gatekeeper task hold central positions.Originality/value Due to the scarcity of comparative whole network research in the field, the strength of this study is a deeper understanding of the differentiation and integration of complex child service delivery systems. These insights are crucial in order to deliver needed services and to minimize service silos and fragmentation.

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“…Pow et al [20] suggests usage of SNA in nursing to analyze why some people are open to organizational changes while the others are not. Similarly, SNA is used and applied further by many other authors such as Quinn et al [21], Mainas [13], Oshima et al [19] and Cserpes [7].…”

Section: Introductionmentioning

confidence: 99%

Social network analysis of scientific collaborations across different subject fields

Bozdogan

Akbilgiç

2013

ISU

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This paper analyzes the level of scientific collaboration and interaction in different subject fields using a novel social network analysis (SNA) on a data set provided by the Department of Energy (DOE) Office of Scientific and Technologic Information (OSTI) in Oak Ridge, Tennessee. This paper not only determines the level of scientific collaboration between different disciplines, but it also analyzes the trends among the subject fields considered. The results in this paper show clear pattern recognition discovery on the social network model among key subject fields using weighted networks.

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Social Network Analysis for Analyzing Groups as Complex Systems

Cited by 8 publications

References 26 publications

International Youth Movements for Climate Change: The #FridaysForFuture Case on Twitter

International Youth Movements for Climate Change: The #FridaysForFuture Case on Twitter

Cross-sectoral collaboration: comparing complex child service delivery systems

Social network analysis of scientific collaborations across different subject fields

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