# Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication

arXiv: https://arxiv.org/abs/2202.00396 · HTML: https://mlsanity.com/paper/2202.00396/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 86 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020)
- [Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data](https://mlsanity.com/paper/1806.04511/) (2018)
- [Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages](https://mlsanity.com/paper/1805.09016/) (2018)
- [Election campaigning on social media: Politicians, audiences and the mediation of political communication on Facebook and Twitter](https://mlsanity.com/paper/1801.08825/) (2018)
- [An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections](https://mlsanity.com/paper/1707.09939/) (2017)
- [Political Popularity Analysis in Social Media](https://mlsanity.com/paper/1812.03258/) (2018)
- [That Message Went Viral?! Exploratory Analytics and Sentiment Analysis into the Propagation of Tweets](https://mlsanity.com/paper/2004.09718/) (2020)

## Built on by (most cited first, 3 of 3)

- [Exploiting contextual information to improve stance detection in informal political discourse with LLMs](https://mlsanity.com/paper/2602.04750/) (2026)
- [Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds](https://mlsanity.com/paper/2404.04031/) (2024)
- [Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments](https://mlsanity.com/paper/2502.00414/) (2025)

## 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.