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首页> 外文期刊>Information Communication & Society >What is gained and what is left to be done when content analysis is added to network analysis in the study of a social movement: Twitter use during Gezi Park
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What is gained and what is left to be done when content analysis is added to network analysis in the study of a social movement: Twitter use during Gezi Park

机译：在社交运动的研究中，将内容分析添加到网络分析中后，会得到什么，剩下要做的事情：Gezi公园期间Twitter的使用

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As social movements relying on the weak ties found in social networks have spread around the world, researchers have taken several approaches to understanding how networks function in such instances as the Arab Spring. While social scientists have primarily relied on survey or content analysis methodology, network scientists have used social network analysis. This research combines content analysis with the automated techniques of network analysis to determine the roles played by those using Twitter to communicate during the Turkish Gezi Park uprising. Based on a network analysis of nearly 2.4 million tweets and a content analysis of a subset of 5126 of those tweets, we found that information sharing was by far the most common use of the tweets and retweets, while tweets that indicated leadership of the movement constituted a small percentage of the overall number of tweets. Using automated techniques, we experimented with coded variables from content analysis to compute the most discriminative tokens and to predict values for each variable using only textual information. We achieved 0.61 precision on identifying types of shared information. Our results on detecting the position of user in the protest and purpose of the tweets achieved 0.42 and 0.33 precision, respectively, illustrating the necessity of user cooperation and the shortcomings of automated techniques. Based on annotated values of user tweets, we computed similarities between users considering their information production and consumption. User similarities are used to compute clusters of individuals with similar behaviors, and we interpreted average activities for those groups.
机译：依靠社会网络中薄弱联系的社会运动在世界范围内蔓延，研究人员已经采取了几种方法来理解网络在“阿拉伯之春”等情况下的运作方式。社会科学家主要依靠调查或内容分析方法，而网络科学家则使用社会网络分析。这项研究将内容分析与自动化的网络分析技术结合在一起，以确定在土耳其盖兹公园起义期间使用Twitter进行交流的人员所扮演的角色。根据对近240万条推文的网络分析以及对其中5126条推文的一部分的内容分析，我们发现，信息共享是推文和转推的最常见用法，而表明运动领导地位的推文构成了占总推文总数的一小部分。使用自动化技术，我们对内容分析中的编码变量进行了实验，以计算最具区别性的标记，并仅使用文本信息来预测每个变量的值。我们在识别共享信息的类型上达到了0.61的精度。我们在抗议中的用户位置检测和推文目的检测结果分别达到0.42和0.33精度，这说明了用户合作的必要性和自动化技术的缺点。基于用户推文的注释值，考虑到用户的信息生产和消费情况，我们计算了用户之间的相似度。用户相似性用于计算具有相似行为的个人的集群，我们解释了这些群体的平均活动。

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