Hot Topic and Viral Content Mining

Problem

Content creators often face a concrete gap: they want to know whether a topic is worth pursuing, which platforms are likely to perform well, and why existing viral posts work. Raw search results are scattered across platforms, making it hard to compare freshness, engagement, and reusable angles.

How It Works

This skill turns hot-topic and viral-content mining into a practical workflow:
- Keyword expansion: extract entities, products, events, scenarios, and emotional terms from the input topic, then build multiple search queries.
- Multi-platform retrieval: prioritize Douyin, Xiaohongshu, WeChat Official Accounts, Bilibili, Zhihu, Weibo, Baidu, and other sources by P0, P1, and P2 levels.
- Data cleaning: validate publish time, engagement signals, platform attribution, and source links, with preference for verifiable samples from the last 30 days.
- Angle synthesis: summarize patterns in titles, content types, and propagation drivers, then recommend platform-specific positioning.

Boundaries

It is best used for topic scouting, competitive viral-post analysis, and content positioning. Some engagement metrics come from search snippets and may be incomplete. If timestamps are missing or data cannot be cross-checked, the conclusion should be down-weighted. For fast-moving events, the latest primary source should still be treated as authoritative.