Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 86 references that are in the map. First posted on arXiv 1 Feb 2022.
Builds on
15 of 86 references.
References · 15
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 414
- 2020BERTweet: A pre-trained language model for English Tweetscited by 346
- 2018Learning Word Vectors for 157 Languagescited by 279
- 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 209
- 2020SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweetscited by 62
- 2018Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Datacited by 19
- 2018Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languagescited by 7
- 2018Election campaigning on social media: Politicians, audiences and the mediation of political communication on Facebook and Twittercited by 4
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
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- 2025Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit commentscited by 0
- 2026Exploiting contextual information to improve stance detection in informal political discourse with LLMs4 Feb 2026
- 2025Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments1 Feb 2025
- 2024Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds5 Apr 2024