Language Models Learn Metadata: Political Stance Detection Case Study
<|reference_start|>Language Models Learn Metadata: Political Stance Detection Case Study: 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.<|reference_end|>
@article{cao2024language,
    title={Language Models Learn Metadata: Political Stance Detection Case Study},
    author={Stanley Cao, Felix Drinkall},
    journal={arXiv preprint arXiv:2409.13756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13756},
    primaryClass={cs.CL}
}