打造懂平台文化的短视频评论生成器，让AI评论更像真人。
LOLGORITHM: Funny Comment Generation Agent For Short Videos
- 分模块设计，融合内容理解与流行梗增强
- 在双平台测试中获超80%人类偏好认可
- 支持六种风格控制，适合内容运营与创作
短视频平台已成为多媒体信息传播的核心，评论在提升互动、传播和算法反馈中起关键作用。然而，现有方法（包括视频摘要和直播弹幕生成）难以生成符合平台文化与语言规范的真实评论。本文提出LOLGORITHM，一种新颖的模块化多智能体框架，用于风格化短视频评论生成。该框架支持六种可控评论风格，包含三个核心模块：视频内容摘要、视频分类、以及结合语义检索与热门梗增强的评论生成。我们构建了一个跨平台的双语数据集，涵盖YouTube和Douyin上的3,267个视频与16,335条评论，覆盖五个高互动类别。自动评分与大规模人工偏好分析表明，LOLGORITHM在两平台均显著优于基线方法，人类偏好选择率分别达到80.46%（YouTube）和84.29%（Douyin），基于107名受试者。消融实验证明性能提升源于框架架构，而非基础大模型选择，验证了方法的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, propagation, and algorithmic feedback. However, existing approaches -- including video summarization and live-streaming danmaku generation -- fail to produce authentic comments that conform to platform-specific cultural and linguistic norms. In this paper, we present LOLGORITHM, a novel modular multi-agent framework for stylized short-form video comment generation. LOLGORITHM supports six controllable comment styles and comprises three core modules: video content summarization, video classification, and comment generation with semantic retrieval and hot meme augmentation. We further construct a bilingual dataset of 3,267 videos and 16,335 comments spanning five high-engagement categories across YouTube and Douyin. Evaluation combining automatic scoring and large-scale human preference analysis demonstrates that LOLGORITHM consistently outperforms baseline methods, achieving human preference selection rates of 80.46\% on YouTube and 84.29\% on Douyin across 107 respondents. Ablation studies confirm that these gains are attributable to the framework architecture rather than the choice of backbone LLM, underscoring the robustness and generalizability of our approach.
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