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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 · ORCID
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 7 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. Zero-Shot Conversational Stance Detection: Dataset and Approaches

    cs.CL 2025-06 conditional novelty 6.0 of 10

    The authors release ZS-CSD, the first zero-shot conversational stance detection dataset with 280 Weibo targets, and their SITPCL model achieves a macro-F1 of 43.81%, establishing an initial benchmark.

  4. TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection

    cs.SI 2025-06 conditional novelty 6.0 of 10

    TwiUSD is a new manually annotated user-level stance benchmark with follower links, and MRFG, which filters followee tweets with an LLM and routes features by graph usefulness, reports top in-target performance.

  5. Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speech

    cs.SI 2025-04 conditional novelty 6.0 of 10

    A large-scale map of the Italian Telegram sphere shows ideological homophily, normalized toxicity across highly toxic communities, and consistent hate targets including Italians, Black people, Jews, and gay people.

  6. 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.

  7. A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs

    cs.CY 2024-11 reject novelty 5.0 of 10

    Group polarization is measured through a Community Sentiment Network built by a team of LLM agents, with a Community Opposition Index score; only the stance detection step is empirically tested.

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