# Stance Detection: A Practical Guide to Classifying Political Beliefs in Text

arXiv: https://arxiv.org/abs/2305.01723 · HTML: https://mlsanity.com/paper/2305.01723/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 68 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://mlsanity.com/paper/2201.05955/) (2022)
- [AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators](https://mlsanity.com/paper/2303.16854/) (2023)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Text Segmentation as a Supervised Learning Task](https://mlsanity.com/paper/1803.09337/) (2018)
- [Consistency of a Recurrent Language Model With Respect to Incomplete Decoding](https://mlsanity.com/paper/2002.02492/) (2020)
- [On the Importance of Effectively Adapting Pretrained Language Models for Active Learning](https://mlsanity.com/paper/2104.08320/) (2021)
- [Multi-class Text Classification using BERT-based Active Learning](https://mlsanity.com/paper/2104.14289/) (2021)
- [ConStance: Modeling Annotation Contexts to Improve Stance Classification](https://mlsanity.com/paper/1708.06309/) (2017)
- [What can we Learn from Predictive Modeling?](https://mlsanity.com/paper/1612.05844/) (2016)
- [Assessing Sentiment of the Expressed Stance on Social Media](https://mlsanity.com/paper/1908.03181/) (2019)
- [On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations](https://mlsanity.com/paper/2201.00318/) (2022)

## Built on by (most cited first, 20 of 20)

- [Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts](https://mlsanity.com/paper/2407.10747/) (2024)
- [Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement](https://mlsanity.com/paper/2310.18440/) (2023)
- [The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions](https://mlsanity.com/paper/2406.12480/) (2024)
- [Towards Coding Social Science Datasets with Language Models](https://mlsanity.com/paper/2306.02177/) (2023)
- [Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Text](https://mlsanity.com/paper/2409.02078/) (2024)
- [Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles](https://mlsanity.com/paper/2510.03898/) (2025)
- [SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions](https://mlsanity.com/paper/2603.14027/) (2026)
- [Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speech](https://mlsanity.com/paper/2504.19594/) (2025)
- [StanceMoE: Mixture-of-Experts Architecture for Stance Detection](https://mlsanity.com/paper/2604.00878/) (2026)
- [What is Sentiment Meant to Mean to Language Models?](https://mlsanity.com/paper/2405.02454/) (2024)
- [PReSS: An Automated Black-Box Framework for Evaluating Political Stance Stability in LLMs](https://mlsanity.com/paper/2504.17052/) (2025)
- [Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content](https://mlsanity.com/paper/2505.23812/) (2025)
- [Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction](https://mlsanity.com/paper/2603.23531/) (2026)
- [Political Leaning and Politicalness Classification of Texts](https://mlsanity.com/paper/2507.13913/) (2025)
- [Fifty Shades of Greenwashing: The Political Economy of Climate Change Advertising on Social Media](https://mlsanity.com/paper/2511.14930/) (2025)
- [A conceptual framework for ideology in online discourse beyond the left and right](https://mlsanity.com/paper/2603.18945/) (2026)
- [Multicalibration for Unbiased Model-Based Prevalence Estimation](https://mlsanity.com/paper/2604.21549/) (2026)
- [LLM-Assisted Stance Detection in Scientific Discourse: A Test Case in Bayesian Cognitive Science](https://mlsanity.com/paper/2606.15566/) (2026)
- [Enhancing LLMs in Predictive Political QA with Semi-Structured Data](https://mlsanity.com/paper/2608.21218/) (2026)
- [Tipping Points in LLM-Based Multi-Agent Systems: Stance on Climate Change Action](https://mlsanity.com/paper/2609.25432/) (2026)

## Abstract (arXiv)

Stance detection is identifying expressed beliefs in a document. While researchers widely use sentiment analysis for this, recent research demonstrates that sentiment and stance are distinct. This paper advances text analysis methods by precisely defining stance detection and presenting three distinct approaches: supervised classification, natural language inference, and in-context learning with generative language models. I discuss how document context and trade-offs between resources and workload should inform your methods. For all three approaches I provide guidance on application and validation techniques, as well as coding tutorials for implementation. Finally, I demonstrate how newer classification approaches can replicate supervised classifiers.