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arXiv · 2409.13756

Language Models Learn Metadata: Political Stance Detection Case Study

Stanley Cao·Felix Drinkall
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Abstract

Stance detection is a crucial NLP task with numerous applications in social science, from analyzing online discussions to assessing political campaigns. This paper investigates the optimal way to incorporate metadata into a political stance detection task. We demonstrate that previous methods combining metadata with language-based data for political stance detection have not fully utilized the metadata information; our simple baseline, using only party membership information, surpasses the current state-of-the-art. We then show that prepending metadata (e.g., party and policy) to political speeches performs best, outperforming all baselines, indicating that complex metadata inclusion systems may not learn the task optimally.

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Stanley Cao, Felix Drinkall. 2024-09-15. Language Models Learn Metadata: Political Stance Detection Case Study. https://arxiv.org/abs/2409.13756

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