# Political Communities on Twitter: Case Study of the 2022 French Presidential Election

arXiv: https://arxiv.org/abs/2204.07436 · HTML: https://mlsanity.com/paper/2204.07436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 20 of 20)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://mlsanity.com/paper/2103.01664/) (2021)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Deep Neural Networks for Bot Detection](https://mlsanity.com/paper/1802.04289/) (2018)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election](https://mlsanity.com/paper/1707.00086/) (2017)
- [The DARPA Twitter Bot Challenge](https://mlsanity.com/paper/1601.05140/) (2016)
- [LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot Classifiers](https://mlsanity.com/paper/1809.09684/) (2018)
- [Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms](https://mlsanity.com/paper/1902.00043/) (2019)
- [BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets](https://mlsanity.com/paper/2109.10234/) (2021)
- [Spotting political social bots in Twitter: A use case of the 2019 Spanish general election](https://mlsanity.com/paper/2004.00931/) (2020)
- [Analysing Twitter Semantic Networks: the case of 2018 Italian Elections](https://mlsanity.com/paper/2009.02960/) (2020)
- [The Limited Reach of Fake News on Twitter during 2019 European Elections](https://mlsanity.com/paper/1911.12039/) (2019)

## Built on by (most cited first, 1 of 1)

- [Realistic Synthetic Social Networks with Graph Neural Networks](https://mlsanity.com/paper/2212.07843/) (2022)

## Abstract (arXiv)

With the significant increase in users on social media platforms, a new means of political campaigning has appeared. Twitter and Facebook are now notable campaigning tools during elections. Indeed, the candidates and their parties now take to the internet to interact and spread their ideas. In this paper, we aim to identify political communities formed on Twitter during the 2022 French presidential election and analyze each respective community. We create a large-scale Twitter dataset containing 1.2 million users and 62.6 million tweets that mention keywords relevant to the election. We perform community detection on a retweet graph of users and propose an in-depth analysis of the stance of each community. Finally, we attempt to detect offensive tweets and automatic bots, comparing across communities in order to gain insight into each candidate's supporter demographics and online campaign strategy.