Viral scoring systems use machine learning models to analyze data across multiple dimensions:
| rating dimension | Specific indicators | Optimization Recommendations |
|---|---|---|
| Content structure (30 points) | Opening 3 seconds of attraction, frequency of tempo changes | Use the AI-recommended 'Golden Beginnings' template |
| Social Element (25 points) | Number of interactive guide points, readability of subtitles | Insertion of questionable subtitles every 15 seconds |
| Platform Adaptation (20 points) | Tag relevance, title keyword density | Synchronization of recent platform hot tabs |
| Audience Portrait (25 points) | Target group match, completion rate prediction | Adjusting video duration to accommodate platform features |
Practicalities:
- A score of 60 or less suggests refactoring the content and optimizing the headline using the "Hot Word Replacement" tool
- 60-85 points video can try A/B test to compare the effect of different cover images
- Content with a score of 85 or higher is prioritized for release during peak traffic hours
Internal tests have shown that videos optimized by following the rating recommendations have an average 2.3x increase in playback, which is especially suitable for new accounts that need to increase their fan base quickly.
This answer comes from the articleShort AI: Automatically generating short video content suitable for social media distributionThe
May not be reproduced without permission:AI productivity tools " How does Short AI's viral rating system work? How can users utilize this feature to boost video distribution?
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