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LLMs improve social media stance detection with contextual prompts

By PulseAugur Editorial · [2 sources] ·

Researchers have explored the effectiveness of contextualized prompting for stance detection on social media using large language models. Their study found that incorporating contextual features, such as user biographies or LLM-generated target descriptions, can improve accuracy in zero-shot settings. However, the impact varies, with some contextual information, like other tweets from the same user, sometimes hindering performance due to noise. The research highlights the challenges LLMs face in discerning relevant context from irrelevant data for stance detection. AI

IMPACT Enhances LLM capabilities for nuanced social media analysis, potentially improving content moderation and sentiment tracking.

RANK_REASON This is a research paper detailing a new method for improving LLM performance on a specific task.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs improve social media stance detection with contextual prompts

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Tilman Beck, Shakib Yazdani, Simon Kruschinski, Marcus Maurer, Iryna Gurevych ·

    Contextualized Prompting For Stance Detection On Social Media

    arXiv:2606.06022v1 Announce Type: new Abstract: 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…

  2. arXiv cs.CL TIER_1 English(EN) · Iryna Gurevych ·

    Contextualized Prompting For Stance Detection On Social Media

    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 pos…

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