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Stance Detection in Prediction Markets: Addressing Imbalanced Trader Commentary via Counterfactual Augmentation and Market Context

arXiv 2605.28745 ↗First posted 27 May 2026cs.CLcited by 0

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Abstract (arXiv)

Prediction markets such as Polymarket aggregate crowd beliefs into real-time probability estimates, and the comments traders post beneath each market contain rich directional stance signals that prices alone cannot capture. This work introduces the first stance detection study applied to prediction market commentary, a domain characterized by extreme brevity, trader- specific vernacular, and severe class imbalance (only 8.7% of comments oppose the market outcome). RoBERTa-base is fine-tuned across a 4 x 3 ablation: four input configurations ({2- class, 3-class} x {with/without market context}) and three augmentation conditions (baseline, 50% synthetic, 100% synthetic). Synthetic minority-class samples are generated via LLM-driven Pro -> Anti counterfactual flips using the Anthropic API. Results show that (1) market context is the single most impactful factor, raising 3-class Anti recall from 0.10 to 0.45; (2) counterfactual augmentation is conditionally effective, improving Anti F1 in weak configurations (0.10 -> 0.24) while degrading strong ones (2-class-ctx macro F1: 0.68 -> 0.50 at full dose); and (3) 50% augmentation is the optimal dose, with 100% consistently hurting performance. Attention-based interpretability analysis provides mechanistic support for all three findings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 27 May 2026.

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References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 994
  4. 2019Attention is not Explanationcited by 916
  5. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 428
  6. 2020Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representationscited by 97
  7. 2020Explaining The Efficacy of Counterfactually Augmented Datacited by 64
  8. 2021An Investigation of the (In)effectiveness of Counterfactually Augmented Datacited by 42
  9. 2024Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalancecited by 16
  10. 2022Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learningcited by 5
  11. 2024Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibrationcited by 5
  12. 2023Robust Stance Detection: Understanding Public Perceptions in Social Mediacited by 2
2 more
  1. 2024Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detectioncited by 2
  2. 2023CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identificationcited by 1

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