Twitter to Open Source Tweet Recommendation Algorithm Code on March 31st

On March 31st, Twitter plans to release the source code for all the algorithms used in recommending tweets. This means that the company will make the code publicly available, allowing developers to access it and use it as they see fit. This move was communicated by Elon Musk on his Twitter handle today.

Twitter has been using these algorithms to suggest tweets to users for several years now, and the move to open source the code is intended to provide greater transparency into how the platform's recommendation system works. By making the code available to the public, Twitter hopes to encourage collaboration and improve the overall quality of the recommendation system.

Twitter will open source all code used to recommend tweets on March 31st

— Elon Musk (@elonmusk) March 17, 2023

The decision to open source the code comes amid increasing scrutiny of social media platforms and their algorithms. Critics have raised concerns about the potential for these algorithms to perpetuate bias and exacerbate existing inequalities. By releasing the code, Twitter hopes to address some of these concerns and promote greater accountability and trust in its recommendation system.

Overall, the move to open source the code for its tweet recommendation algorithms is a significant step for Twitter. It reflects the company's commitment to transparency and collaboration, and could help to promote greater understanding and trust in how the platform operates.

Our “algorithm” is overly complex & not fully understood internally. People will discover many silly things , but we’ll patch issues as soon as they’re found!

We’re developing a simplified approach to serve more compelling tweets, but it’s still a work in progress. That’ll also… https://t.co/uxxJe3RT36

— Elon Musk (@elonmusk) March 17, 2023

In a series of tweets, Elon Musk explained why this move was necessary and how Twitter can build the trust of people by providing code transparency and how this exercise will bring rapid improvement in recommendation quality.