LOLGORITHM: Funny Comment Generation Agent For Short Videos
In recent years, short-form video platforms have emerged as vital channels for multimedia information dissemination.
The comments section in these platforms plays a crucial role in driving user engagement, enhancing content propagation,
and providing valuable algorithmic feedback. However, existing methods, including video summarization and live-streaming
danmaku generation, often fall short in producing authentic comments that align with platform-specific cultural and
linguistic norms.
To address this gap, researchers have developed LOLGORITHM, a novel modular multi-agent framework designed to generate
stylized comments for short-form videos. This innovative tool supports six distinct comment styles and is structured
around three core modules: video content summarization, video classification, and a sophisticated comment generation
system that incorporates semantic retrieval and hot meme augmentation.
Key Features of LOLGORITHM
- Multi-Agent Framework: LOLGORITHM’s modular architecture allows for flexibility and adaptability in comment generation.
- Controllable Comment Styles: Users can select from six different comment styles, tailoring the output to fit specific cultural contexts.
- Video Content Summarization: The framework effectively summarizes video content, enabling the generation of relevant comments.
- Video Classification: By classifying videos into distinct categories, LOLGORITHM ensures that comments are contextually appropriate.
- Semantic Retrieval: This feature enhances comment relevance by retrieving contextually relevant phrases and expressions.
- Hot Meme Augmentation: The integration of trending memes ensures that comments resonate with current cultural trends, making them more engaging.
Dataset and Evaluation
To validate the effectiveness of LOLGORITHM, the research team constructed a bilingual dataset consisting of 3,267 videos
and 16,335 comments across five high-engagement categories sourced from popular platforms such as YouTube and Douyin.
The evaluation process combined automatic scoring metrics with large-scale human preference analyses, demonstrating that
LOLGORITHM consistently outperforms existing baseline methods.
The results revealed that LOLGORITHM achieved an impressive human preference selection rate of 80.46% on YouTube and
84.29% on Douyin, as evaluated by 107 respondents. These findings underscore the framework’s ability to generate comments
that not only meet user expectations but also enhance overall engagement on the platforms.
Conclusion
The development of LOLGORITHM represents a significant advancement in the field of comment generation for short-form videos.
Its robust architecture, which emphasizes cultural relevance and user engagement, sets it apart from traditional methods.
As short-form video content continues to proliferate, tools like LOLGORITHM will be pivotal in shaping the future of online interactions.