# Adversarial Domain Adaptation for Stance Detection

arXiv: https://arxiv.org/abs/1902.02401 · HTML: https://mlsanity.com/paper/1902.02401/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19](https://mlsanity.com/paper/2208.09578/) (2022)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions](https://mlsanity.com/paper/2109.00475/) (2021)
- [Artefact Retrieval: Overview of NLP Models with Knowledge Base Access](https://mlsanity.com/paper/2201.09651/) (2022)
- [From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains](https://mlsanity.com/paper/2203.10659/) (2022)
- [Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management](https://mlsanity.com/paper/2104.01436/) (2021)
- [Rating Facts under Coarse-to-fine Regimes](https://mlsanity.com/paper/2107.06051/) (2021)
- [ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization](https://mlsanity.com/paper/2412.01476/) (2024)
- [Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction](https://mlsanity.com/paper/2601.02884/) (2026)

## Abstract (arXiv)

This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on machine learning are viable alternatives. In this paper, we focus on adversarial domain adaptation for stance detection where we assume there exists sufficient labeled data in the source domain and limited labeled data in the target domain. Extensive experiments on publicly available datasets show the effectiveness of our domain adaption model in transferring knowledge for accurate stance detection across domains.