构建多模态评论热度评估基准，量化内容质量与用户互动影响。
HotComment: A Benchmark for Evaluating Popularity of Online Comments
- 融合视频与文本的多模态评测框架，从内容质量、热度预测、用户行为三方面评估。
- 通过真实交互数据训练模型，实现对评论热度的精准预测。
- 提出风格适配机制，模拟社交传播效应，提升评价的社区适应性。
在线评论在社交媒体中对公众情绪与舆论动态具有关键作用，但其热度评估仍具挑战性，不仅受语言质量、原创性和情感共鸣影响，还因平台与用户群体间风格偏好差异，导致同一评论在不同社群中反响不一。本文提出 HotComment，一个融合视频与文本模态的多模态基准，从三个增强维度全面量化评论热度：(1) 内容质量，通过与真实人工评论的语义相似性评估，并扩展为四个可解释维度；(2) 热度预测，基于真实世界互动数据训练模型；(3) 用户行为模拟，通过基于代理的框架建模平台用户分布并估算**参与度得分**。此外，我们提出 StyleCmt，受社交涟漪效应启发，通过多风格维度协同放大社会共鸣表达，抑制不一致表达。
原文摘要 · Abstract (English)
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.
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