# Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

arXiv: https://arxiv.org/abs/2202.12382 · HTML: https://mlsanity.com/paper/2202.12382/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 68 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels](https://mlsanity.com/paper/1512.06974/) (2015)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Fame for sale: efficient detection of fake Twitter followers](https://mlsanity.com/paper/1509.04098/) (2015)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media](https://mlsanity.com/paper/1704.01347/) (2017)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [Coordinated Behavior on Social Media in 2019 UK General Election](https://mlsanity.com/paper/2008.08370/) (2020)
- [Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior](https://mlsanity.com/paper/1902.02765/) (2019)
- [Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum](https://mlsanity.com/paper/1808.08538/) (2018)
- [#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate](https://mlsanity.com/paper/1802.09808/) (2018)
- [Reducing Controversy by Connecting Opposing Views](https://mlsanity.com/paper/1611.00172/) (2016)
- [Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter](https://mlsanity.com/paper/1711.10251/) (2017)
- [Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing](https://mlsanity.com/paper/1907.07228/) (2019)
- [The Limited Reach of Fake News on Twitter during 2019 European Elections](https://mlsanity.com/paper/1911.12039/) (2019)
- [To Kavanaugh or Not to Kavanaugh: That is the Polarizing Question](https://mlsanity.com/paper/1810.06687/) (2018)
- [Predicting the Topical Stance of Media and Popular Twitter Users](https://mlsanity.com/paper/1907.01260/) (2019)

## Built on by (most cited first, 8 of 8)

- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets](https://mlsanity.com/paper/2204.10710/) (2022)
- [Recent Trends in Artificial Intelligence Technology: A Scoping Review](https://mlsanity.com/paper/2305.04532/) (2023)
- [Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach](https://mlsanity.com/paper/2304.11367/) (2023)
- [Party Prediction for Twitter](https://mlsanity.com/paper/2308.13699/) (2023)
- [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)
- [Recommender system in X inadvertently profiles ideological positions of users](https://mlsanity.com/paper/2602.02624/) (2026)

## Abstract (arXiv)

Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation. Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts. Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task. Then, users are projected in a low-dimensional ideology space where they are subsequently clustered. The political leaning of a user is automatically derived from the cluster to which the user is assigned. We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. Other than being interesting on their own, our results also pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.