# Adversarial Learning for Zero-Shot Stance Detection on Social Media

arXiv: https://arxiv.org/abs/2105.06603 · HTML: https://mlsanity.com/paper/2105.06603/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Learning to select data for transfer learning with Bayesian Optimization](https://mlsanity.com/paper/1707.05246/) (2017)
- [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://mlsanity.com/paper/2003.08385/) (2020)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://mlsanity.com/paper/1606.03784/) (2016)
- [Aspect-augmented Adversarial Networks for Domain Adaptation](https://mlsanity.com/paper/1701.00188/) (2017)
- [Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum](https://mlsanity.com/paper/1808.08538/) (2018)
- [Stance Detection in Turkish Tweets](https://mlsanity.com/paper/1706.06894/) (2017)

## Built on by (most cited first, 30 of 37)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation](https://mlsanity.com/paper/2306.04344/) (2023)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation](https://mlsanity.com/paper/2312.12480/) (2023)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Multi-modal Stance Detection: New Datasets and Model](https://mlsanity.com/paper/2402.14298/) (2024)
- [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://mlsanity.com/paper/2208.08797/) (2022)
- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation](https://mlsanity.com/paper/2405.16486/) (2024)
- [Can Large Language Models Address Open-Target Stance Detection?](https://mlsanity.com/paper/2409.00222/) (2024)
- [Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study](https://mlsanity.com/paper/2110.07693/) (2021)
- [Learning Stance Embeddings from Signed Social Graphs](https://mlsanity.com/paper/2201.11675/) (2022)
- [OpenStance: Real-world Zero-shot Stance Detection](https://mlsanity.com/paper/2210.14299/) (2022)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance](https://mlsanity.com/paper/2403.00236/) (2024)
- [Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning](https://mlsanity.com/paper/2212.10392/) (2022)
- [Zero-Shot Stance Detection using Contextual Data Generation with LLMs](https://mlsanity.com/paper/2405.11637/) (2024)
- [Stanceformer: Target-Aware Transformer for Stance Detection](https://mlsanity.com/paper/2410.07083/) (2024)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://mlsanity.com/paper/2310.14450/) (2023)
- [Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges](https://mlsanity.com/paper/2409.13594/) (2024)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [A Logically Consistent Chain-of-Thought Approach for Stance Detection](https://mlsanity.com/paper/2312.16054/) (2023)
- [EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection](https://mlsanity.com/paper/2403.15715/) (2024)
- [MSME: A Multi-Stage Multi-Expert Framework for Zero-Shot Stance Detection](https://mlsanity.com/paper/2512.04492/) (2025)
- [Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation](https://mlsanity.com/paper/2407.20910/) (2024)
- [Collaborative Stance Detection via Small-Large Language Model Consistency Verification](https://mlsanity.com/paper/2502.19954/) (2025)
- [Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies](https://mlsanity.com/paper/2209.04631/) (2022)
- [Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning](https://mlsanity.com/paper/2210.03380/) (2022)
- [Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study](https://mlsanity.com/paper/2404.03987/) (2024)

## Abstract (arXiv)

Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to new topics, highlighting future directions for zero-shot transfer.