twitter-algorithm-optimizer

Analyze and rewrite tweets to maximize reach using Twitter's open-source recommendation algorithms. Use this skill when you need to: Twitter's recommendation system uses multiple interconnected models: Real-graph : Predicts interaction likelihood between users SimClusters : Community detection with sparse embeddings TwHIN : Knowledge graph embeddings for users and posts Tweepcred : User reputation/authority scoring Twitter's Unified User Actions service tracks both explicit and implicit signals: Explicit Signals (high weight): Implicit Signals (also weighted): Negative Signals : Your tweet reaches users through this pipeline: Candidate Retrieval - Multiple sources find candidate tweets: Ranking - ML models rank candidates by predicted engagement: Filtering - Remove blocked content, apply preferences Delivery - Show ranked feed to user Strategy : Make content your followers WILL engage with Example Optimization : Strategy : Find and serve tight communities deeply interested in your topic Example Optimization : Strategy : Make your content clearly relevant to your established identity Example Optimization : Strategy : Build reputation through engagement consistency Example Optimization : Explicit Signal Triggers : For Likes : For Replies : For Retweets : For Bookmarks/Saves : Example Optimization : Avoid : Original : "I fixed a bug today" Algorithm Analysis : Optimized : "Spent...