Integrated twitter analysis to distinguish systems thinkers at various levels: a case study of COVID-19

Applied Network Science

Research

Mohammad Nagahisarchoghaei¹  Morteza Nagahi²  Harun Pirim³  Oumaima Larif⁴  Raed Jaradat⁴ 

[1]Computer Science and Engineering, Mississippi State University, 39762, Mississippi State, MS, USA

[2]DiversityInc Media LLC, 33405, West Palm Beach, FL, USA

[3]Industrial and Manufacturing Engineering, North Dakota State University, 58108, Fargo, ND, USA

[4]Industrial and Systems Engineering, Mississippi State University, 39762, Mississippi State, MS, USA

关键词: Systems thinking;    Systems skills;    COVID-19;    Pandemic;    Social networks;    Twitter analysis;    Follower network;    Network clustering;   

DOI  :  10.1007/s41109-022-00520-9

 received in 2022-03-23, accepted in 2022-11-10,  发布年份 2022

来源: Springer

【 摘 要 】

Systems Thinking (ST) has become essential for practitioners and experts when dealing with turbulent and complex environments. Twitter medium harbors social capital including systems thinkers, however there are limited studies available in the extant literature that investigate how experts' systems thinking skills, if possible at all, can be revealed within Twitter analysis. This study aims to reveal systems thinking levels of experts from their Twitter accounts represented as a network. Unraveling of latent Twitter network clusters ensues the centrality analysis of their follower networks inferred in terms of systems thinking dimensions. COVID-19 emerges as a relevant case study to investigate the relationship between COVID-19 experts’ Twitter network and their systems thinking capabilities. A sample of 55 trusted expert Twitter accounts related to COVID-19 has been selected for the current study based on the lists from Forbes, Fortune, and Bustle. The Twitter network has been constructed based on the features extracted from their Twitter accounts. Community detection reveals three distinct groups of experts. In order to relate system thinking qualities to each group, systems thinking dimensions are matched with the follower network characteristics such as node-level metrics and centrality measures including degree, betweenness, closeness and Eigen centrality. Comparison of the 55 expert follower network characteristics elucidates three clusters with significant differences in centrality scores and node-level metrics. The clusters with a higher, medium, lower scores can be classified as Twitter accounts of Holistic thinkers, Middle thinkers, and Reductionist thinkers, respectfully. In conclusion, systems thinking capabilities are traced through unique network patterns in relation to the follower network characteristics associated with systems thinking dimensions.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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Fig. 1