笑、共鸣、互动：面向短视频的风格化评论生成
Laugh, Relate, Engage: Stylized Comment Generation for Short Videos
AI总结：
本文提出多智能体系统 LOLGORITHM，结合视频分割、情感分析和风格提示，利用多模态大模型实现六种风格的短视频评论生成，并在中英双语数据集上显著超越基线模型。
摘要：
短视频平台已成为现代互联网生态中的核心媒介，高效的信息传递与强互动性正在重塑用户参与和文化传播。在各种形式的用户互动中，评论在促进社区参与和实现内容再创作方面发挥着至关重要的作用。然而，生成既符合平台规范、又能展现风格多样性和语境感知能力的评论，仍然是一项重大挑战。我们提出了 LOLGORITHM，一个面向可控短视频评论生成的模块化多智能体系统（MAS）。该系统集成了视频分割、语境与情感分析以及风格感知的提示构建。它支持六种不同的评论风格：双关语（谐音）、押韵、梗应用、讽刺（反讽）、纯幽默和内容提取。在多媒体大语言模型（MLLM）的驱动下，LOLGORITHM 直接处理视频输入，并通过显式提示标记和少样本示例实现细粒度的风格控制。为了支持开发与评估，我们利用抖音（中文）和 YouTube（英文）的官方 API 构建了一个双语数据集，涵盖五种热门视频类型：喜剧小品、日常生活笑话、搞笑动物片段、幽默解说和脱口秀。评估将自动化指标（原创性、相关性和风格一致性）与一项涉及 40 个视频和 105 名参与者的大规模人类偏好研究相结合。结果表明，LOLGORITHM 显著优于基线模型，在抖音上取得了超过 90% 的偏好率，在 YouTube 上取得了 87.55% 的偏好率。本工作为短视频平台上的风格化评论生成提供了一个可扩展且具有文化适应性的框架，为增强用户参与和创造性互动提供了一条有前景的路径。
英文摘要：
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.
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。
