HotComment: A Benchmark for Evaluating Popularity of Online Comments
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914514348277760 |
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| author | Wu, Yafeng Zhang, Yunyao Ye, Liliang Zeng, Guiyi Yu, Junqing Xu, Chen Song, Zikai |
| author_facet | Wu, Yafeng Zhang, Yunyao Ye, Liliang Zeng, Guiyi Yu, Junqing Xu, Chen Song, Zikai |
| contents | Online comments play a crucial role in shaping public sentiment and opinion dynamics on social media. However, evaluating their popularity remains challenging, not only because it depends on linguistic quality, originality, and emotional resonance, but also because stylistic preferences vary widely across platforms and user groups, causing the same comment to resonate differently in different communities. In this work, we present HotComment, a multimodal benchmark integrating video and text modalities that comprehensively quantifies popularity from three enhanced aspects: (1) Content Quality, which evaluates semantic similarity with ground-truth human comments and extends quality assessment through four interpretable dimensions; (2) Popularity Prediction, based on trends from models trained on real-world interaction data; and (3) User Behavior Simulation, which models the distribution of platform users and approximates \textbf{engagement scores} through an agent-based framework. Furthermore, we propose StyleCmt, inspired by social ripple effects, where multiple stylistic dimensions align to amplify socially resonant expressions and suppress incongruent ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25614 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HotComment: A Benchmark for Evaluating Popularity of Online Comments Wu, Yafeng Zhang, Yunyao Ye, Liliang Zeng, Guiyi Yu, Junqing Xu, Chen Song, Zikai Artificial Intelligence Online comments play a crucial role in shaping public sentiment and opinion dynamics on social media. However, evaluating their popularity remains challenging, not only because it depends on linguistic quality, originality, and emotional resonance, but also because stylistic preferences vary widely across platforms and user groups, causing the same comment to resonate differently in different communities. In this work, we present HotComment, a multimodal benchmark integrating video and text modalities that comprehensively quantifies popularity from three enhanced aspects: (1) Content Quality, which evaluates semantic similarity with ground-truth human comments and extends quality assessment through four interpretable dimensions; (2) Popularity Prediction, based on trends from models trained on real-world interaction data; and (3) User Behavior Simulation, which models the distribution of platform users and approximates \textbf{engagement scores} through an agent-based framework. Furthermore, we propose StyleCmt, inspired by social ripple effects, where multiple stylistic dimensions align to amplify socially resonant expressions and suppress incongruent ones. |
| title | HotComment: A Benchmark for Evaluating Popularity of Online Comments |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.25614 |