# Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks

arXiv: https://arxiv.org/abs/2207.08349 · HTML: https://mlsanity.com/paper/2207.08349/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies](https://mlsanity.com/paper/2105.05134/) (2021)
- [Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study](https://mlsanity.com/paper/2103.10979/) (2021)
- [Who Falls for Online Political Manipulation?](https://mlsanity.com/paper/1808.03281/) (2018)
- [Echo Chambers on Social Media: A comparative analysis](https://mlsanity.com/paper/2004.09603/) (2020)
- [Social Politics: Agenda Setting and Political Communication on Social Media](https://mlsanity.com/paper/1607.06819/) (2016)

## Built on by (most cited first, 13 of 13)

- [Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data](https://mlsanity.com/paper/2401.00893/) (2023)
- [DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks](https://mlsanity.com/paper/2301.08774/) (2023)
- [A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles](https://mlsanity.com/paper/2404.04261/) (2024)
- [Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach](https://mlsanity.com/paper/2307.04668/) (2023)
- [A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs](https://mlsanity.com/paper/2411.12196/) (2024)
- [Quantitative Analysis of Forecasting Models:In the Aspect of Online Political Bias](https://mlsanity.com/paper/2309.05589/) (2023)
- [Modeling Political Orientation of Social Media Posts: An Extended Analysis](https://mlsanity.com/paper/2311.12323/) (2023)
- [Community Needs and Assets: A Computational Analysis of Community Conversations](https://mlsanity.com/paper/2403.13272/) (2024)
- [Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks](https://mlsanity.com/paper/2404.10228/) (2024)
- [Limits to Predicting Online Speech Using Large Language Models](https://mlsanity.com/paper/2407.12850/) (2024)
- [Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations](https://mlsanity.com/paper/2409.02965/) (2024)
- [Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations](https://mlsanity.com/paper/2607.14491/) (2026)
- [Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media](https://mlsanity.com/paper/2608.17987/) (2026)

## Abstract (arXiv)

Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.