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Negativity spreads faster: A large-scale multilingual twitter analysis on the role of sentiment in political communication

Antypas, Dimosthenis, Preece, Alun ORCID: and Camacho Collados, Jose ORCID: 2023. Negativity spreads faster: A large-scale multilingual twitter analysis on the role of sentiment in political communication. Online Social Media and Networks 33 , 100242. 10.1016/j.osnem.2023.100242

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Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians’ negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 2468-6964
Funders: UKRI
Date of First Compliant Deposit: 14 February 2023
Date of Acceptance: 26 January 2023
Last Modified: 21 Jul 2023 16:57

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