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Commenting on Political Topics Through Twitter: Is European Politics European?

机译：通过推特评论政治主题：是欧洲政治欧洲人吗？

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The aim of this study was to explore social media, and specifically Twitter’s potential to generate a European demos. Our use of data derived from social media complements the traditional use of mass media and survey data within existing studies. We selected two Twitter hashtags of European relevance, #schengen and #ttip, to test several theories on a European demos (non-demos, European democracy, or pan-European demos) and to determine which of these theories was most applicable in the case of Twitter topics of European relevance. To answer the research question, we performed sentiment analysis. Sentiment analysis performed on data gathered on social media platforms, such as Twitter, constitutes an alternative methodological approach to more formal surveys (e.g., Eurobarometer) and mass media content analysis. Three dimensions were coded: (1) sentiments toward the issue public, (2) sentiments toward the European Union (EU), and (3) the type of framing. Among all of the available algorithms for conducting sentiment analysis, integrated sentiment analysis (iSA), developed by the Blog of Voices at the University of Milan, was selected for the data analysis. This is a novel supervised algorithm that was specifically designed for analyses of social networks and the Web 2.0 sphere (Twitter, blogs, etc.), taking the abundance of noise within digital environments into consideration. An examination and discussion of the results shows that for these two hashtags, the results were more aligned with the demoicracy and “European lite identity” models than with the model of a pan-European demos.
机译：本研究的目的是探索社交媒体，特别是推特潜力产生欧洲演示。我们使用来自社交媒体的数据补充了现有研究中的传统使用大众媒体和调查数据。我们选择了欧洲相关性，#schengen和#ttip的两个Twitter主题标签，以测试欧洲演示（非演示，欧洲民主或泛欧洲演示）的几个理论，并确定这些理论在案件中最适用欧洲相关的推特主题。要回答研究问题，我们进行了情绪分析。对在社交媒体平台上收集的数据进行的情感分析构成了更正式调查（例如，Eurobarometer）和大众媒体内容分析的替代方法方法。编码了三个维度：（1）向发行的情绪公开，（2）对欧盟（欧盟）的情绪，以及（3）框架的类型。在用于进行情感分析的所有可用算法中，选择由米兰大学的声音博客开发的综合情感分析（ISA）进行数据分析。这是一种新颖的监督算法，专门用于分析社交网络和Web 2.0球体（Twitter，博客等），以考虑数字环境中的丰富噪声。对结果的检查和讨论表明，对于这两个哈希特，结果比泛欧演示的模型与模型和“欧洲精简识别”模型更加顺应。

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