让视频评论又搞笑又有风格，还能自动适配不同平台文化。
Laugh, Relate, Engage: Stylized Comment Generation for Short Videos
- 用多智能体系统分析视频内容与情绪，精准生成六种幽默风格评论。
- 在抖音和YouTube上用户偏好率超90%和87.55%，显著优于基线模型。
- 支持中文和英文双语，适合短视频平台内容创作者与运营者使用。
短视频平台已成为现代互联网的核心媒介，高效信息传递与强互动性正在重塑用户参与和文化传播。评论在促进社区参与和内容再创作中扮演关键角色。然而，生成符合平台规范、兼具风格多样性与上下文感知的评论仍是挑战。本文提出LOLGORITHM，一个模块化多智能体系统（MAS），用于可控的短视频评论生成。系统整合视频分割、上下文与情感分析、风格化提示构建，支持六种评论风格：谐音梗、押韵、梗图应用、讽刺、单纯搞笑、内容提取。基于多模态大语言模型（MLLM），直接处理视频输入，通过显式提示标记与少样本示例实现细粒度风格控制。为支持开发与评估，构建了涵盖五类热门视频类型（喜剧小品、生活笑话、搞笑动物片段、幽默解说、脱口秀）的中英双语数据集，数据来自抖音与YouTube官方API。评估结合自动指标（原创性、相关性、风格一致性）与大规模人工偏好研究（40个视频，105名参与者）。结果表明，LOLGORITHM显著优于基线模型，在抖音和YouTube上的用户偏好率分别达90%以上与87.55%。该工作提供了一个可扩展且文化自适应的风格化评论生成框架，为提升用户参与度与创造性互动开辟新路径。
原文摘要 · Abstract (English)
Short-video platforms have become a central medium in the modern Internet landscape, where efficient information delivery and strong interactivity are reshaping user engagement and cultural dissemination. Among the various forms of user interaction, comments play a vital role in fostering community participation and enabling content re-creation. However, generating comments that are both compliant with platform guidelines and capable of exhibiting stylistic diversity and contextual awareness remains a significant challenge. We introduce LOLGORITHM, a modular multi-agent system (MAS) designed for controllable short-video comment generation. The system integrates video segmentation, contextual and affective analysis, and style-aware prompt construction. It supports six distinct comment styles: puns (homophones), rhyming, meme application, sarcasm (irony), plain humor, and content extraction. Powered by a multimodal large language model (MLLM), LOLGORITHM directly processes video inputs and achieves fine-grained style control through explicit prompt markers and few-shot examples. To support development and evaluation, we construct a bilingual dataset using official APIs from Douyin (Chinese) and YouTube (English), covering five popular video genres: comedy skits, daily life jokes, funny animal clips, humorous commentary, and talk shows. Evaluation combines automated metrics originality, relevance, and style conformity with a large-scale human preference study involving 40 videos and 105 participants. Results show that LOLGORITHM significantly outperforms baseline models, achieving preference rates of over 90% on Douyin and 87.55% on YouTube. This work presents a scalable and culturally adaptive framework for stylized comment generation on short-video platforms, offering a promising path to enhance user engagement and creative interaction.
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