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Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media

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arxiv 2304.03087 v2 pith:5N2PK27M submitted 2023-04-06 cs.CL

Bowen Zhang , Xianghua Fu , Daijun Ding , Hu Huang , Genan Dai , Nan Yin , Yangyang Li , Liwen Jing This is my paper
classification cs.CL
keywords detectionstancechain-of-thoughtchallengeschatgptmediamodelspre-trained
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Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, early deep neural networks, and pre-trained fine-tuning models. However, with the evolution of very large pre-trained language models (VLPLMs) like ChatGPT (GPT-3.5), traditional methods face deployment challenges. The parameter-free Chain-of-Thought (CoT) approach, not requiring backpropagation training, has emerged as a promising alternative. This paper examines CoT's effectiveness in stance detection tasks, demonstrating its superior accuracy and discussing associated challenges.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 17 citations worldwide. Full citation record

  1. Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A hierarchical, cluster-aware imputation method (DTRGC) that reweights feature propagation by cluster membership and imputes missing node attributes in stages improves deep graph clustering on attribute-missing graphs.

  2. MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection

    cs.CL 2025-06 conditional novelty 6.0 of 10

    The paper presents a large new English conversational stance detection dataset and a model that fuses LLM-generated relation and act knowledge, reporting state-of-the-art F1.

  3. Quantifying Political Partisanship for Cross-Platform Analyses

    cs.SI 2026-07 reject novelty 5.0 of 10

    Partisanship of individual posts can be scored on a common embedding axis anchored by AllSides news-bias labels, yielding cross-platform scores that transfer from Bluesky/Truth Social to X.

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