TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909700949278720 |
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| author | Niu, Fuqiang Chen, Zini Xie, Zhiyu Huang, Hu Dai, Genan Zhang, Bowen |
| author_facet | Niu, Fuqiang Chen, Zini Xie, Zhiyu Huang, Hu Dai, Genan Zhang, Bowen |
| contents | User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduce TwiUSD, the first large-scale, manually annotated UserSD benchmark with explicit followee relationships, containing 16,211 users and 47,757 tweets. TwiUSD enables rigorous evaluation of stance models by integrating tweet content and social links, with superior scale and annotation quality. Building on this resource, we propose MRFG: a structure-aware framework that uses LLM-based relevance filtering and feature routing to address noise and context heterogeneity. MRFG employs multi-scale filtering and adaptively routes features through graph neural networks or multi-layer perceptrons based on topological informativeness. Experiments show MRFG consistently outperforms strong baselines (including PLMs, graph-based models, and LLM prompting) in both in-target and cross-target evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection Niu, Fuqiang Chen, Zini Xie, Zhiyu Huang, Hu Dai, Genan Zhang, Bowen Social and Information Networks User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduce TwiUSD, the first large-scale, manually annotated UserSD benchmark with explicit followee relationships, containing 16,211 users and 47,757 tweets. TwiUSD enables rigorous evaluation of stance models by integrating tweet content and social links, with superior scale and annotation quality. Building on this resource, we propose MRFG: a structure-aware framework that uses LLM-based relevance filtering and feature routing to address noise and context heterogeneity. MRFG employs multi-scale filtering and adaptively routes features through graph neural networks or multi-layer perceptrons based on topological informativeness. Experiments show MRFG consistently outperforms strong baselines (including PLMs, graph-based models, and LLM prompting) in both in-target and cross-target evaluation. |
| title | TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2506.13343 |