# Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

arXiv: https://arxiv.org/abs/2208.08797 · HTML: https://mlsanity.com/paper/2208.08797/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 18 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [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)
- [Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling](https://mlsanity.com/paper/1906.07241/) (2019)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://mlsanity.com/paper/1606.03784/) (2016)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis](https://mlsanity.com/paper/2005.00791/) (2020)
- [Improving Relation Extraction with Knowledge-attention](https://mlsanity.com/paper/1910.02724/) (2019)

## Built on by (most cited first, 16 of 16)

- [Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias](https://mlsanity.com/paper/2304.00010/) (2023)
- [Zero-Shot Conversational Stance Detection: Dataset and Approaches](https://mlsanity.com/paper/2506.17693/) (2025)
- [Can Large Language Models Address Open-Target Stance Detection?](https://mlsanity.com/paper/2409.00222/) (2024)
- [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)
- [Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://mlsanity.com/paper/2302.03950/) (2023)
- [Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detection](https://mlsanity.com/paper/2308.16763/) (2023)
- [Collaborative Knowledge Infusion for Low-resource Stance Detection](https://mlsanity.com/paper/2403.19219/) (2024)
- [Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges](https://mlsanity.com/paper/2409.13594/) (2024)
- [EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection](https://mlsanity.com/paper/2403.15715/) (2024)
- [Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information](https://mlsanity.com/paper/2310.05073/) (2023)
- [XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners](https://mlsanity.com/paper/2310.05502/) (2023)
- [PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization](https://mlsanity.com/paper/2412.12588/) (2024)
- [Disentangled VAD Representations via a Variational Framework for Political Stance Detection](https://mlsanity.com/paper/2502.19276/) (2025)
- [Multilingual Target-Stance Extraction](https://mlsanity.com/paper/2510.22334/) (2025)
- [MMDS-Bench: Benchmarking Multimodal Large Language Models on Dynamic Stance in Social Media Interactions](https://mlsanity.com/paper/2608.30903/) (2026)
- [From Two Passes to One: Compact and Efficient Target-Stance Extraction](https://mlsanity.com/paper/2609.06108/) (2026)

## Abstract (arXiv)

The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset--VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.