Three Essays on Political Polarisation: A Computational Analysis of the Italian Political Discourse on Twitter (2014-2019)

关于政治极化的三篇文章：推特上意大利政治话语的计算分析（2014-2019）

• 批准号: 2588065
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2588065
• 关键词: Three; Essays; Political; Polarisation; Computational

Recent studies show that populist polarised social media communication leads to the public's negative attitudes towards democratic institutions and politicians. However, political scientists have failed to produce a comprehensive understanding of why and how such communication strategies negatively affect online behaviour. This dissertation aims to contribute to this gap in the literature by investigating the populist use of affective polarised language on social media, and its effects on online political behaviour, measuring responsiveness to posts in Western Europe. In particular, this research provides a computational analysis of the major populist parties' social media communication in Italy, Germany, France and the UK. With a combination of cutting- edge supervised machine learning, deep learning, econometric and web-scraping methods, it studies populist communication across Twitter and Facebook between 2013 and 2020.This doctoral dissertation has significant theoretical, normative, and methodological implications. Theoretically, it contributes to ongoing debates regarding political economy, psychology and computational social sciences on social media. In particular, the theoretical contributions of each paper are combined into a comprehensive theory of affective polarisation, and tested through hypotheses, which aim to grasp the relationship between political economy, homophily and information processing and AP. Normatively, this dissertation contributes to online behaviour policies in the countries under analysis, and to global tech companies' research to better identify and address online polarisation issues. Finally, this dissertation utilises a combination of cutting- edge supervised machine learning, deep learning, econometrics and web-scraping methods. Doing so allows this research to contribute empirically to the broad effects of AP by applying computational methods to the context of political online behaviour on social media. In conclusion, this research project has the potential to provide a strong understanding of the use of polarised language in political discourse and its effects across-country and across- time. The results may shed light on how online polarisation affects responsiveness, and its broad consequences on online behaviour in Western Europe and beyond.

最近的研究表明，民粹主义两极分化的社交媒体传播导致公众对民主制度和政治家的负面态度。然而，政治科学家未能全面理解这种传播策略为什么以及如何对在线行为产生负面影响。本文旨在通过调查民粹主义者在社交媒体上使用情感极化语言及其对在线政治行为的影响，测量西欧对帖子的反应，来弥补文献中的这一空白。特别是，这项研究提供了一个计算分析的主要民粹主义政党的社会媒体传播在意大利，德国，法国和英国。本论文结合最前沿的监督机器学习、深度学习、计量经济学和网络抓取方法，研究了2013 - 2020年间Twitter和Facebook上的民粹主义传播。从理论上讲，它有助于社会媒体上关于政治经济学，心理学和计算社会科学的持续辩论。特别是，每篇论文的理论贡献结合成一个全面的情感极化理论，并通过假设进行测试，其目的是掌握政治经济学，同质性和信息处理和AP之间的关系。规范，本论文有助于在线行为政策在所分析的国家，并为全球科技公司的研究，以更好地识别和解决在线两极分化问题。最后，本文结合了最先进的监督机器学习、深度学习、计量经济学和网络抓取方法。这样做使这项研究能够通过将计算方法应用于社交媒体上的政治在线行为的背景下，从经验上对AP的广泛影响做出贡献。总之，这个研究项目有可能提供一个强大的理解在政治话语中使用两极分化的语言及其影响，跨国家和跨时间。研究结果可能揭示了在线极化如何影响反应，以及其对西欧及其他地区在线行为的广泛影响。