# Unsupervised User Stance Detection on Twitter

arXiv: https://arxiv.org/abs/1904.02000 · HTML: https://mlsanity.com/paper/1904.02000/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 30 of 45)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media](https://mlsanity.com/paper/2204.04046/) (2022)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media](https://mlsanity.com/paper/2108.03861/) (2021)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders](https://mlsanity.com/paper/2110.00210/) (2021)
- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study](https://mlsanity.com/paper/2110.07693/) (2021)
- [A semantic embedding space based on large language models for modelling human beliefs](https://mlsanity.com/paper/2408.07237/) (2024)
- [Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning](https://mlsanity.com/paper/2202.12382/) (2022)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [GREENER: Graph Neural Networks for News Media Profiling](https://mlsanity.com/paper/2211.05533/) (2022)
- [Measuring the Validity of Clustering Validation Datasets](https://mlsanity.com/paper/2503.01097/) (2025)
- [PAR: Political Actor Representation Learning with Social Context and Expert Knowledge](https://mlsanity.com/paper/2210.08362/) (2022)
- [Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://mlsanity.com/paper/2302.03950/) (2023)
- [Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text](https://mlsanity.com/paper/2106.01033/) (2021)
- [DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks](https://mlsanity.com/paper/2301.08774/) (2023)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://mlsanity.com/paper/2310.14450/) (2023)
- [Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges](https://mlsanity.com/paper/2409.13594/) (2024)
- [Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles](https://mlsanity.com/paper/2504.03520/) (2025)
- [Semi-supervised Stance Detection of Tweets Via Distant Network Supervision](https://mlsanity.com/paper/2201.00614/) (2022)
- [Mining Reaction and Diffusion Dynamics in Social Activities](https://mlsanity.com/paper/2208.04846/) (2022)
- [Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020](https://mlsanity.com/paper/2303.12029/) (2023)
- [Advancing Annotation of Stance in Social Media Posts: A Comparative Analysis of Large Language Models and Crowd Sourcing](https://mlsanity.com/paper/2406.07483/) (2024)
- [Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets](https://mlsanity.com/paper/2204.10710/) (2022)

## Abstract (arXiv)

We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics. In particular, we use dimensionality reduction to project users onto a low-dimensional space, followed by clustering, which allows us to find core users that are representative of the different stances. Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification. First, we do not require any prior labeling of users: instead, we create clusters, which are much easier to label manually afterwards, e.g., in a matter of seconds or minutes instead of hours. Second, there is no need for domain- or topic-level knowledge either to specify the relevant stances (labels) or to conduct the actual labeling. Third, our framework is robust in the face of data skewness, e.g., when some users or some stances have greater representation in the data. We experiment with different combinations of user similarity features, dataset sizes, dimensionality reduction methods, and clustering algorithms to ascertain the most effective and most computationally efficient combinations across three different datasets (in English and Turkish). We further verified our results on additional tweet sets covering six different controversial topics. Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2--3, with more than 98% purity. The resulting user clusters can be used to train downstream classifiers. Moreover, our framework is robust to variations in the hyper-parameter values and also with respect to random initialization.