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Analysing Twitter semantic networks: the case of 2018 Italian elections
Scientific Reports ( IF 4.6 ) Pub Date : 2021-06-24 , DOI: 10.1038/s41598-021-92337-2
Tommaso Radicioni 1, 2 , Fabio Saracco 2 , Elena Pavan 3 , Tiziano Squartini 2
Affiliation  

Social media play a key role in shaping citizens’ political opinion. According to the Eurobarometer, the percentage of EU citizens employing online social networks on a daily basis has increased from 18% in 2010 to 48% in 2019. The entwinement between social media and the unfolding of political dynamics has motivated the interest of researchers for the analysis of users online behavior—with particular emphasis on group polarization during debates and echo-chambers formation. In this context, semantic aspects have remained largely under-explored. In this paper, we aim at filling this gap by adopting a two-steps approach. First, we identify the discursive communities animating the political debate in the run up of the 2018 Italian Elections as groups of users with a significantly-similar retweeting behavior. Second, we study the mechanisms that shape their internal discussions by monitoring, on a daily basis, the structural evolution of the semantic networks they induce. Above and beyond specifying the semantic peculiarities of the Italian electoral competition, our approach innovates studies of online political discussions in two main ways. On the one hand, it grounds semantic analysis within users’ behaviors by implementing a method, rooted in statistical theory, that guarantees that our inference of socio-semantic structures is not biased by any unsupported assumption about missing information; on the other, it is completely automated as it does not rest upon any manual labelling (either based on the users’ features or on their sharing patterns). These elements make our method applicable to any Twitter discussion regardless of the language or the topic addressed.



中文翻译:

分析 Twitter 语义网络:以 2018 年意大利选举为例

社交媒体在塑造公民政治观点方面发挥着关键作用。根据欧洲晴雨表,每天使用在线社交网络的欧盟公民比例从 2010 年的 18% 增加到 2019 年的 48%。社交媒体与政治动态的展开之间的交织激发了研究人员对分析用户在线行为——特别强调辩论和回声室形成过程中的群体极化。在这种情况下,语义方面在很大程度上仍未得到充分探索。在本文中,我们旨在通过采用两步法来填补这一空白。首先,我们确定话语社区将 2018 年意大利大选前夕的政治辩论作为转发行为非常相似的用户群体活跃起来。其次,我们通过每天监测它们诱导的语义网络的结构演变来研究塑造它们内部讨论的机制。除了指定意大利选举竞争的语义特征之外,我们的方法还以两种主要方式对在线政治讨论的研究进行了创新。一方面,它通过实施一种植根于统计理论的方法,在用户行为中建立语义分析的基础,该方法保证我们对社会语义结构的推断不会受到任何关于缺失信息的无支持假设的偏见;在另一,它是完全自动化的,因为它不依赖于任何手动标记(基于用户的特征或他们的共享模式)。这些元素使我们的方法适用于任何 Twitter 讨论,无论所讨论的语言或主题如何。

更新日期:2021-06-24
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