# How would Stance Detection Techniques Evolve after the Launch of ChatGPT?

arXiv: https://arxiv.org/abs/2212.14548 · HTML: https://mlsanity.com/paper/2212.14548/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://mlsanity.com/paper/1909.03477/) (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)
- [Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks](https://mlsanity.com/paper/1804.06536/) (2018)
- [Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention](https://mlsanity.com/paper/1801.03032/) (2018)
- [Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum](https://mlsanity.com/paper/1808.08538/) (2018)

## Built on by (most cited first, 30 of 76)

- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://mlsanity.com/paper/2307.02477/) (2023)
- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks](https://mlsanity.com/paper/2304.10145/) (2023)
- [Can we trust the evaluation on ChatGPT?](https://mlsanity.com/paper/2303.12767/) (2023)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [An Empirical Study on Information Extraction using Large Language Models](https://mlsanity.com/paper/2305.14450/) (2023)
- [ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification](https://mlsanity.com/paper/2303.03953/) (2023)
- [The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks](https://mlsanity.com/paper/2304.13861/) (2023)
- [AI for social science and social science of AI: A Survey](https://mlsanity.com/paper/2401.11839/) (2024)
- [Primacy Effect of ChatGPT](https://mlsanity.com/paper/2310.13206/) (2023)
- [Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions](https://mlsanity.com/paper/2310.02431/) (2023)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [Unmasking Conversational Bias in AI Multiagent Systems](https://mlsanity.com/paper/2501.14844/) (2025)
- [Can LLMs facilitate interpretation of pre-trained language models?](https://mlsanity.com/paper/2305.13386/) (2023)
- [ChatGPT is a Remarkable Tool -- For Experts](https://mlsanity.com/paper/2306.03102/) (2023)
- [Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways](https://mlsanity.com/paper/2406.11980/) (2024)
- [Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence](https://mlsanity.com/paper/2309.14379/) (2023)
- [Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media](https://mlsanity.com/paper/2305.13047/) (2023)
- [Chain-of-Thought Embeddings for Stance Detection on Social Media](https://mlsanity.com/paper/2310.19750/) (2023)
- [Large Language Models for Travel Behavior Prediction](https://mlsanity.com/paper/2312.00819/) (2023)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [Cross-target Stance Detection by Exploiting Target Analytical Perspectives](https://mlsanity.com/paper/2401.01761/) (2024)

## Abstract (arXiv)

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection.