# Exploiting contextual information to improve stance detection in informal political discourse with LLMs

arXiv: https://arxiv.org/abs/2602.04750 · HTML: https://mlsanity.com/paper/2602.04750/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs](https://mlsanity.com/paper/2502.09597/) (2025)
- [Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs](https://mlsanity.com/paper/2406.09136/) (2024)
- [Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers](https://mlsanity.com/paper/2311.09180/) (2023)
- [Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts](https://mlsanity.com/paper/2406.10471/) (2024)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [User Embedding Model for Personalized Language Prompting](https://mlsanity.com/paper/2401.04858/) (2024)
- [Personalized Dialogue Generation with Persona-Adaptive Attention](https://mlsanity.com/paper/2210.15088/) (2022)
- [Guided Profile Generation Improves Personalization with LLMs](https://mlsanity.com/paper/2409.13093/) (2024)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication](https://mlsanity.com/paper/2202.00396/) (2022)
- [From Millions of Tweets to Actionable Insights: Leveraging LLMs for User Profiling](https://mlsanity.com/paper/2505.06184/) (2025)
- [Robust Stance Detection: Understanding Public Perceptions in Social Media](https://mlsanity.com/paper/2309.15176/) (2023)

## Built on by (most cited first, 2 of 2)

- [BITS Pilani at SemEval-2026 Task 9: Structured Supervised Fine-Tuning with DPO Refinement for Polarization Detection](https://mlsanity.com/paper/2604.11121/) (2026)
- [Contextualized Prompting For Stance Detection On Social Media](https://mlsanity.com/paper/2606.06022/) (2026)

## Abstract (arXiv)

This study investigates the use of Large Language Models (LLMs) for political stance detection in informal online discourse, where language is often sarcastic, ambiguous, and context-dependent. We explore whether providing contextual information, specifically user profile summaries derived from historical posts, can improve classification accuracy. Using a real-world political forum dataset, we generate structured profiles that summarize users' ideological leaning, recurring topics, and linguistic patterns. We evaluate seven state-of-the-art LLMs across baseline and context-enriched setups through a comprehensive cross-model evaluation. Our findings show that contextual prompts significantly boost accuracy, with improvements ranging from +17.5% to +38.5%, achieving up to 74% accuracy that surpasses previous approaches. We also analyze how profile size and post selection strategies affect performance, showing that strategically chosen political content yields better results than larger, randomly selected contexts. These findings underscore the value of incorporating user-level context to enhance LLM performance in nuanced political classification tasks.