mlsanityData to 7 Oct 2026

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

arXiv 2305.01723 ↗First posted 2 May 2023cs.CLcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 26 of its 68 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

26 of 68 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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14 more
  1. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 299
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  5. 2023ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learningcited by 97
  6. 2020Stance Detection on Social Media: State of the Art and Trendscited by 93
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  11. 2017ConStance: Modeling Annotation Contexts to Improve Stance Classificationcited by 7
  12. 2016What can we Learn from Predictive Modeling?cited by 6
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 6; 2026: 8.

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  1. 2024Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Conceptscited by 28
  2. 2023Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreementcited by 17
  3. 2024The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussionscited by 16
  4. 2023Towards Coding Social Science Datasets with Language Modelscited by 10
  5. 2024Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Textcited by 7
  6. 2025Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articlescited by 5
  7. 2026SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasionscited by 5
  8. 2025Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speechcited by 2
  9. 2026StanceMoE: Mixture-of-Experts Architecture for Stance Detectioncited by 2
  10. 2024What is Sentiment Meant to Mean to Language Models?cited by 1
  11. 2025PReSS: An Automated Black-Box Framework for Evaluating Political Stance Stability in LLMscited by 1
  12. 2025Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Contentcited by 1
8 more
  1. 2026Large Language Models Unpack Complex Political Opinions through Target-Stance Extractioncited by 1
  2. 2025Political Leaning and Politicalness Classification of Textscited by 0
  3. 2025Fifty Shades of Greenwashing: The Political Economy of Climate Change Advertising on Social Mediacited by 0
  4. 2026A conceptual framework for ideology in online discourse beyond the left and rightcited by 0
  5. 2026Multicalibration for Unbiased Model-Based Prevalence Estimationcited by 0
  6. 2026LLM-Assisted Stance Detection in Scientific Discourse: A Test Case in Bayesian Cognitive Sciencecited by 0
  7. 2026Enhancing LLMs in Predictive Political QA with Semi-Structured Datacited by 0
  8. 2026Tipping Points in LLM-Based Multi-Agent Systems: Stance on Climate Change Actioncited by 0