社交媒体的影响:对2011年埃及革命期间推文的调查

MIS Q. Pub Date : 2021-10-14 DOI:10.25300/misq/2021/15297
S. Venkatesan, Rohit Valecha, Niam Yaraghi, Onook Oh, H. Rao
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引用次数: 7

摘要

本文通过社会运动理论的视角,研究了2011年埃及革命期间Twitter上个人用户社会影响的驱动因素。根据这一视角,我们提出了一个持续社会影响力的扩展模型(将转发作为用户影响力的衡量标准),作为Twitter个人用户社会行为和潜在促进Twitter网络结构的二元性的函数。基于对这一社会运动的有机大规模Twitter数据的分析,我们研究了个人社会行为的特征,即Twitter上的活动和任期,以及网络促进的特征(即Twitter的追随者数量和社区结构中的中心性),如何在跨越运动的时间窗口中影响转发影响。利用由机器学习和人类编码组成的混合方法,我们将Twitter用户的社会运动相关参与活动概念化,这些活动映射到社会运动动员的一般框架。分析揭示了埃及革命不同背景下的有趣模式。关于个人的社会行动,与"谁"和"在哪里"活动有关的社会运动以及任期都有助于个人的社会影响。在促进结构方面,追随者网络(观察到的网络结构)和中心性(未观察到的网络结构)都对持续影响有显著贡献。
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Influence in Social Media: An Investigation of Tweets Spanning the 2011 Egyptian Revolution
Through the lens of social movement theory, this paper investigates the drivers of individual users’ social influence on Twitter during the Egyptian Revolution of 2011. Following this lens, we suggest an extended model of sustained social influence (that considers retweets as the measure of user influence) as a function of the duality of individual Twitter users’ social actions and the underlying facilitating Twitter network structure. Based on an analysis of organic large-scale Twitter data on this social movement, we examine how characteristics of individuals’ social actions, namely activity and tenure on Twitter, and characteristics facilitated by the network (i.e., the number of followers as well as centrality in the community structure of Twitter), impact retweet influence in time windows spanning the movement. Utilizing a mixed methods approach consisting of machine learning and human coding we conceptualize social movement-related engagement activities of Twitter users, which map to generic frames of social movement mobilization. The analysis reveals interesting patterns across different contexts of the Egyptian Revolution. Regarding individual social action, social movement related to “who” and “where” activities, as well as tenure, were found to contribute to individual social influence. In terms of the facilitating structure, the follower network (an observed network structure) and centrality (an unobserved network structure) were both found to contribute significantly to sustained influence.
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