# STANCY: Stance Classification Based on Consistency Cues

arXiv: https://arxiv.org/abs/1910.06048 · HTML: https://mlsanity.com/paper/1910.06048/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [Few-Shot Stance Detection via Target-Aware Prompt Distillation](https://mlsanity.com/paper/2206.13214/) (2022)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning](https://mlsanity.com/paper/2212.10392/) (2022)
- [Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining](https://mlsanity.com/paper/2310.12172/) (2023)
- [WhatTheWikiFact: Fact-Checking Claims Against Wikipedia](https://mlsanity.com/paper/2105.00826/) (2021)
- [Tribrid: Stance Classification with Neural Inconsistency Detection](https://mlsanity.com/paper/2109.06508/) (2021)
- [Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection](https://mlsanity.com/paper/2405.10991/) (2024)
- [Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study](https://mlsanity.com/paper/2404.03987/) (2024)
- [CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning](https://mlsanity.com/paper/2404.17609/) (2024)
- [FarExStance: Explainable Stance Detection for Farsi](https://mlsanity.com/paper/2412.14008/) (2024)
- [Distant finetuning with discourse relations for stance classification](https://mlsanity.com/paper/2204.12693/) (2022)

## Abstract (arXiv)

Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.