HotComment: 一个评估在线评论流行度的基准
HotComment: A Benchmark for Evaluating Popularity of Online Comments
AI总结：
本文提出HotComment基准，通过内容质量、流行度预测和用户行为模拟三个维度评估在线评论的流行度，同时引入StyleCmt提升社交共振效果。
摘要：
在线评论在塑造社交媒体公众意见动态中起关键作用，但评估其流行度具有挑战性，因其依赖语言质量、原创性和情感共鸣，且不同平台和用户群体的风格偏好差异大，使相同评论在不同社区产生不同反响。本文提出HotComment，一个整合视频和文本模态的多模态基准，从三个方面全面量化流行度：(1) 内容质量，通过与人类评论的语义相似性评估并扩展质量评估；(2) 流行度预测，基于真实互动数据训练模型的趋势；(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近似参与度分数。此外，我们提出StyleCmt，受社交涟漪效应启发，通过多个风格维度放大社交共鸣表达并抑制不一致表达。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
