# Contextualized Prompting For Stance Detection On Social Media

arXiv: https://arxiv.org/abs/2606.06022 · HTML: https://mlsanity.com/paper/2606.06022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 69 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 25 of 25)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://mlsanity.com/paper/2306.15895/) (2023)
- [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://mlsanity.com/paper/2306.04140/) (2023)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://mlsanity.com/paper/2001.01565/) (2020)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [Empowering Large Language Models for Textual Data Augmentation](https://mlsanity.com/paper/2404.17642/) (2024)
- [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021)
- [Chain-of-Thought Embeddings for Stance Detection on Social Media](https://mlsanity.com/paper/2310.19750/) (2023)
- [Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning](https://mlsanity.com/paper/2403.14895/) (2024)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Is External Information Useful for Stance Detection with LLMs?](https://mlsanity.com/paper/2507.01543/) (2025)
- [Exploiting contextual information to improve stance detection in informal political discourse with LLMs](https://mlsanity.com/paper/2602.04750/) (2026)

## Abstract (arXiv)

Stance detection on social media is challenging due to short, noisy, and context-dependent language. While large language models (LLMs) show zero-shot generalization, they are typically prompted without contextual information, which limits their ability to interpret ambiguous posts. In this work, we systematically investigate the impact of incorporating real-world (e.g., user biographies), derived (e.g., political party), and LLM-generated (e.g., target descriptions) contextual features into zero-shot prompting for stance detection on Twitter. Our evaluation spans four benchmark datasets, including a new high-quality German Twitter stance dataset. Across multiple LLMs, we find that integrating contextual information improves performance, but only under specific conditions. LLM-generated target descriptions consistently enhance accuracy, while other user metadata has mixed or even detrimental effects. Notably, we show that the inclusion of other tweets by the same user, often beneficial in supervised learning, can impair performance due to input noise. Our qualitative analysis reveals that LLMs struggle to distinguish task-specific useful information from irrelevant context. Our findings highlight both the promise and challenges of prompting with context information in noisy real-world settings. We publish code and data at this hrefhttps://github.com/tilmanbeck/stance-context-twitterpage.