# The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

arXiv: https://arxiv.org/abs/2406.12480 · HTML: https://mlsanity.com/paper/2406.12480/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 20 of 20)

- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Active Learning by Acquiring Contrastive Examples](https://mlsanity.com/paper/2109.03764/) (2021)
- [Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://mlsanity.com/paper/2310.07849/) (2023)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://mlsanity.com/paper/2003.08385/) (2020)
- [Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science](https://mlsanity.com/paper/2305.15041/) (2023)
- [KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media](https://mlsanity.com/paper/2204.04046/) (2022)
- [Stance Detection: A Practical Guide to Classifying Political Beliefs in Text](https://mlsanity.com/paper/2305.01723/) (2023)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [On the Importance of Data Size in Probing Fine-tuned Models](https://mlsanity.com/paper/2203.09627/) (2022)
- [Stance Detection on Social Media with Fine-Tuned Large Language Models](https://mlsanity.com/paper/2404.12171/) (2024)
- [Zero-Shot Stance Detection using Contextual Data Generation with LLMs](https://mlsanity.com/paper/2405.11637/) (2024)

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

- [Unmasking Conversational Bias in AI Multiagent Systems](https://mlsanity.com/paper/2501.14844/) (2025)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Generative AI in Transportation Planning: A Survey](https://mlsanity.com/paper/2503.07158/) (2025)
- [Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges](https://mlsanity.com/paper/2409.13594/) (2024)
- [Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them](https://mlsanity.com/paper/2503.22006/) (2025)
- [Data Swarms: Optimizable Generation of Synthetic Evaluation Data](https://mlsanity.com/paper/2506.00741/) (2025)
- [Modeling Contextual Passage Utility for Multihop Question Answering](https://mlsanity.com/paper/2512.06464/) (2025)
- [Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation](https://mlsanity.com/paper/2409.07780/) (2024)
- [Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction](https://mlsanity.com/paper/2603.23531/) (2026)
- [From Measurement Instruments to Data: Leveraging Theory-Driven Synthetic Training Data for Classifying Social Constructs](https://mlsanity.com/paper/2410.12622/) (2024)
- [Solving Word-Sense Disambiguation and Word-Sense Induction with Dictionary Examples](https://mlsanity.com/paper/2503.04328/) (2025)
- [Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models](https://mlsanity.com/paper/2505.02858/) (2025)
- [ParlAI Vote: A Web Platform for Analyzing Gender and Political Bias in Large Language Models](https://mlsanity.com/paper/2509.16264/) (2025)
- [ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks](https://mlsanity.com/paper/2605.00513/) (2026)
- [Graph-Augmented LLMs for Swiss MP Ideology Prediction](https://mlsanity.com/paper/2605.04643/) (2026)
- [Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue](https://mlsanity.com/paper/2608.08210/) (2026)

## Abstract (arXiv)

Stance detection holds great potential to improve online political discussions through its deployment in discussion platforms for purposes such as content moderation, topic summarization or to facilitate more balanced discussions. Typically, transformer-based models are employed directly for stance detection, requiring vast amounts of data. However, the wide variety of debate topics in online political discussions makes data collection particularly challenging. LLMs have revived stance detection, but their online deployment in online political discussions faces challenges like inconsistent outputs, biases, and vulnerability to adversarial attacks. We show how LLM-generated synthetic data can improve stance detection for online political discussions by using reliable traditional stance detection models for online deployment, while leveraging the text generation capabilities of LLMs for synthetic data generation in a secure offline environment. To achieve this, (i) we generate synthetic data for specific debate questions by prompting a Mistral-7B model and show that fine-tuning with the generated synthetic data can substantially improve the performance of stance detection, while remaining interpretable and aligned with real world data. (ii) Using the synthetic data as a reference, we can improve performance even further by identifying the most informative samples in an unlabelled dataset, i.e., those samples which the stance detection model is most uncertain about and can benefit from the most. By fine-tuning with both synthetic data and the most informative samples, we surpass the performance of the baseline model that is fine-tuned on all true labels, while labelling considerably less data.