Laugh, Relate, Engage: Stylized Comment Generation for Short Videos

TL;DR

This paper introduces LOLGORITHM, a multi-agent system that generates stylized, context-aware comments for short videos, enhancing user engagement through controllable, culturally adaptive comment creation.

Contribution

The paper presents a novel modular system leveraging multimodal large language models for controllable, style-specific comment generation on short videos, supported by a bilingual dataset and comprehensive evaluation.

Findings

LOLGORITHM outperforms baselines with over 90% preference rate on Douyin.

The system supports six distinct comment styles with fine-grained control.

Evaluation shows high relevance, originality, and style conformity of generated comments.

Abstract

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.