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In a new blogpost, Twitter has revealed how its algorithm works to rank and filter tweets on users’ timelines. Explaining the process in a simple manner, Twitter says that it occurs in three main stages.

 

According to Twitter, its algorithmic system first gathers the “best” tweets from various recommendation sources. On the For You timeline, for instance, Twitter aims to display around 50% tweets from accounts that a user follows and 50% tweets from accounts that a user doesn’t follow.

 

In the ranking stage, the system uses a machine learning model to rank tweets based on engagement analytics including Likes, Retweets, and Replies.

 

In the final step, tweets are filtered out to be displayed on users’ timelines. The filtering happens in categories like tweets from people one has blocked, tweets one has already seen, or tweets that are not safe or appropriate. The aim of this process is also to ensure that a user isn’t seeing too many tweets from the same account.

 

In addition to providing this information, CEO Elon Musk announced that  will be open source starting today. The code, which Musk believes to be “overly complex,” is meant to provide users and researchers with transparency into the mechanisms that decide which tweets show up on people’s timelines.

 


Twitter has shared the code on GitHub, and while it doesn’t contain every little detail, the insights it provides are pretty significant as it contains core models and features that extract latent information from tweets, users, and engagement data on Twitter.

Twitter Makes its Recommendation Algorithm Code Open Source



In a new blogpost, Twitter has revealed how its algorithm works to rank and filter tweets on users’ timelines. Explaining the process in a simple manner, Twitter says that it occurs in three main stages.

 

According to Twitter, its algorithmic system first gathers the “best” tweets from various recommendation sources. On the For You timeline, for instance, Twitter aims to display around 50% tweets from accounts that a user follows and 50% tweets from accounts that a user doesn’t follow.

 

In the ranking stage, the system uses a machine learning model to rank tweets based on engagement analytics including Likes, Retweets, and Replies.

 

In the final step, tweets are filtered out to be displayed on users’ timelines. The filtering happens in categories like tweets from people one has blocked, tweets one has already seen, or tweets that are not safe or appropriate. The aim of this process is also to ensure that a user isn’t seeing too many tweets from the same account.

 

In addition to providing this information, CEO Elon Musk announced that  will be open source starting today. The code, which Musk believes to be “overly complex,” is meant to provide users and researchers with transparency into the mechanisms that decide which tweets show up on people’s timelines.

 


Twitter has shared the code on GitHub, and while it doesn’t contain every little detail, the insights it provides are pretty significant as it contains core models and features that extract latent information from tweets, users, and engagement data on Twitter.

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