论文标题
消极情绪更快地传播：关于情感在政治传播中作用的大规模多语言Twitter分析
Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication
论文作者
论文摘要
在现代社会的政策制定方面，尤其是在西方世界，诸如Twitter之类的平台使用户能够跟随政客，从而使公民更多地参与政治讨论时，社交媒体变得极为影响。同样，政客们使用Twitter来表达他们的意见，就当前主题进行辩论，并促进其旨在影响选民行为的政治议程。在本文中，我们试图分析来自三个欧洲国家的政客的推文，并探讨其推文的病毒性。先前的研究表明，传达负面情绪的推文可能会更频繁地转发。通过利用最先进的预训练的语言模型，我们对希腊，西班牙和英国的数十万条推文进行了情感分析，包括权威的政府。我们通过系统地探索和分析有影响力和不流行的推文之间的差异来实现这一目标。我们的分析表明，政治家的负责推文更广泛地传播，尤其是在最近的时候，重点介绍了政党以及政客与普通人群之间的有趣差异。
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.
