LOLGORITHM: Funny Comment Generation Agent For Short Videos
Xuan Ouyang, Bouzhou Wang, Senan Wang, Siyuan Xiahou, Jinrong Zhou, Yuekang Li

TL;DR
LOLGORITHM is a modular multi-agent framework that generates culturally and linguistically appropriate comments for short videos, enhancing engagement on platforms like YouTube and Douyin.
Contribution
It introduces a novel multi-agent system supporting six comment styles, with a bilingual dataset and evaluation demonstrating superior human preference over baselines.
Findings
Outperforms baseline methods in human preference tests with over 80% approval.
Supports six controllable comment styles for diverse engagement.
Constructed a bilingual dataset of over 16,000 comments across five categories.
Abstract
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…
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