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Polarisation in political Twitter conversations

机译:政治性Twitter对话中的两极分化

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Purpose - The purpose of this paper is to describe and analyse relationships and communication between Twitter actors in Swedish political conversations. More specifically, the paper aims to identify the most prominent actors, among these actors identify the sub-groups of actors with similar political affiliations, and describe and analyse the relationships and communication between these sub-groups. Design/methodology/approach - Data were collected during four weeks in September 2012, using Twitter API. The material included 77,436 tweets from 10,294 Twitter actors containing the hashtag #svpol. In total, 916 prominent actors were identified and categorised according to the main political blocks, using information from their profiles. Social network analysis was utilised to map the relationships and the communication between these actors. Findings - There was a marked dominance of the three main political blocks among the 916 most prominent actors: left block, centre-right block, and right-wing block. The results from the social network analysis suggest that while polarisation exists in both followership and re-tweet networks, actors follow and re-tweet actors from other groups. The mention network did not show any signs of polarisation. The blocks differed from each other with the right-wingers being tighter and far more active, but also more distant from the others in the followership network. Originality/value - While a few papers have studied political polarisation on Twitter, this is the first to study the phenomenon using followership data, mention data, and re-tweet data.
机译:目的-本文的目的是描述和分析瑞典政治对话中Twitter参与者之间的关系和交流。更具体地说,本文旨在确定最杰出的参与者,在这些参与者中确定具有相似政治隶属关系的参与者的子群体,并描述和分析这些子群体之间的关系和交流。设计/方法/方法-在2012年9月的四个星期内,使用Twitter API收集了数据。该材料包括来自10294个Twitter演员的77436条推文,其中包含#svpol标签。使用主要人物的资料,根据主要政治人物,总共确定了916名杰出演员并进行了分类。利用社交网络分析来映射这些参与者之间的关系和交流。调查结果-在916位最杰出的演员中,三个主要政治块有显着优势:左块,中右块和右翼块。社交网络分析的结果表明,虽然追随者网络和转发社区中都存在两极分化,但参与者跟随并转发了其他群体的参与者。提及网络未显示任何极化迹象。这些块彼此不同,右翼更加紧密,活跃得多,但与跟随者网络中的其他块也更加遥远。原创性/价值-尽管有几篇论文研究了Twitter上的政治两极分化,但这是第一篇使用追随者数据,提及数据和重新发布数据研究这种现象的方法。

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