اثرات همه گیری فعال کردن مکرر در شبکه های اجتماعی
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اثرات همه گیری فعال کردن مکرر در شبکه های اجتماعی

عنوان فارسی مقاله: اثرات همه گیری فعال کردن مکرر در شبکه های اجتماعی
عنوان انگلیسی مقاله: The contagion effects of repeated activation in social networks
مجله/کنفرانس: شبکه های اجتماعی – Social Networks
رشته های تحصیلی مرتبط: مدیریت، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: مدیریت فناوری اطلاعات
کلمات کلیدی فارسی: وابستگی متقابل، دینامیک زمانی، تناسب، آستانه، جرم بحرانی، اشباع، اقدام جمعی، شبیه سازی مبتنی بر عامل
کلمات کلیدی انگلیسی: Interdependence، Temporal dynamics، Coordination، Thresholds، Critical mass، Diffusion، Collective action، Agent-based simulation
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journal List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.socnet.2017.11.001
دانشگاه: Institute for Biocomputation and Physics of Complex Systems and Department of Theoretical Physics – University of Zaragoza – Spain
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: ۲٫۵۳۰ در سال ۲۰۱۷
شاخص H_index: ۷۹ در سال ۲۰۱۸
شاخص SJR: ۲٫۱۴۷ در سال ۲۰۱۸
شناسه ISSN: 0378-8733
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E10554
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Coordination as a two-step selection process


3- Model and mechanisms


4- The dynamics of repeated activation


5- Social context as communication networks


6- The effects of network topology


7- The effects of actor heterogeneity


8- Discussion


9- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Demonstrations, protests, riots, and shifts in public opinion respond to the coordinating potential of communication networks. Digital technologies have turned interpersonal networks into massive, pervasive structures that constantly pulsate with information. Here, we propose a model that aims to analyze the contagion dynamics that emerge in networks when repeated activation is allowed, that is, when actors can engage recurrently in a collective effort. We analyze how the structure of communication networks impacts on the ability to coordinate actors, and we identify the conditions under which large-scale coordination is more likely to emerge.


Introduction


Recent years have seen the emergence of massive events coordinated through large, decentralized networks. These include political protests and mobilizations like the Occupy movement of 2011 (Conover et al., 2013; González-Bailón and Wang, 2016), the Gezi Park demonstrations of 2013 (Barberá et al., 2015), or the growth of the #BlackLivesMatter campaign during the 2014 protests in Ferguson (Freelon et al., 2016). These collective events offer examples ofthe coordinating potential of communication networks − which, increasingly, emerge through the use of online technologies. This paper pays attention to the coordination dynamics that allow a small movement, a new campaign, or an unknown hashtag to rise to prominence. We present a formal model that allows us to answer the following question: How do coordination dynamics unfold to make individual actions (e.g. using an emerging hashtag, endorsing a mobilization) converge over time? Our model aims to disentangle the mechanisms that drive the emergence of decentralized, large-scale coordination. The goal is to identify the conditions under which coordination is more likely to arise from networks that are constantly pulsating with information. Threshold models have become the standard for how we think about interdependence and the collective effects of social influence (Granovetter, 1978; Granovetter and Soong, 1983; Schelling, 1978). As originally formulated, the activation of individual thresholds responds to global information: the group of reference is assumed to be the same for all actors. In later developments of the basic model, networks were introduced to add local variance to social influence: the group of reference was now determined by connectivity in the network, which changed from actor to actor (Valente, 1996; Watts, 2002). These different variations of the threshold model share two important elements: first, activation is modelled as a step function that goes from 0 to 1 when thresholds are reached; and second, thresholds can only be reached once, that is, activation is assumed to be a one-off event. Our model aims to relax these assumptions and allow actors to repeatedly activate as a function of the dynamics unfolding in the rest of the network. We argue that this modification aligns our model of contagion more closely with what is observed in many empirical networks − in particular, with the communication dynamics observed in online networks and the temporal autocorrelation that results from those dynamics.

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