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

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
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --

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英文摘要
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.
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