USA: Twitter has posted some of its source code online in an effort to increase transparency in the service. As a result, tweet recommendation algorithms are the subject of much discussion due to claims of bias based on factors such as political affiliation and celebrity status. Platformer's Zoe Schiffer and tech blogger Jen Manchun Wong alerted us to the information.

Twitter receives approximately 500 million tweets per day. A mechanism is needed to give users a selection of the most popular Tweets that will appear on their For You timeline.

This is exactly what the recommendation algorithm does. It crunches the data so users see only the most relevant. For the same, it depends on three parameters.

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According to Twitter, it uses three factors to suggest tweets to users. First, it collects tweets from a variety of sources using a method called candidate sourcing.

The tweets are then ranked using a machine learning model. Finally, filters are used to prevent users from viewing content from blocked users, previously viewed Tweets, or even NSFW content.

Wong claims that in the US, in addition to labels such as "Democrat," "Republican," and "power user," Twitter's algorithm also indicates whether the tweet author is Elon Musk.

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While this is going on, famous figures such as Jack Dorsey, Barack Obama, Stephen Curry and Katy Perry are being used as "test accounts" to collect random tweets for analysis. This raises concerns about the potential for tracking political affiliations and promoting the accounts of some users.

Schiffer provided an alternative justification for the structure of the algorithm. She claims that the above tags are only used to track account metrics and do not affect users' visibility.

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This raises another question. Is there no possibility of discrimination if the tweets of Democrats, Republicans and power users are tracked separately?

Twitter claims that it is exploring options to improve user experience transparency. For creators, it will offer an advanced analytics platform with more reach and engagement data. Also, the safety rating given to Tweets and accounts will be more transparent.