Twitter expands recommendations push with new tests


Twitter is expanding how it recommends posts from accounts that users don’t follow, the social media company announced Tuesday.

As part of the expansion, it is also building tools for users to monitor that content and provide feedback.

“With millions of people signing up for Twitter every day, we want to make it easier for everyone to connect to accounts and topics that interest them,” Twitter said in a blog post.

The tests come as social media companies double down this year on what they call “disconnected content,” or posts from accounts that users don’t follow, after the short video app TikTok came to the fore and relied entirely on algorithm-driven suggestions.

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One of the new designs Twitter has been testing involves posting “related tweets” below conversations on a tweet detail page, said Angela Wise, a senior director of product management responsible for “discovery” on the service.

Twitter is also experimenting with an “X” tool that users can click to remove recommended tweets they don’t like from their timelines, the blog post said.

Competitor Meta Platforms aims to double the percentage of recommended content filling users’ feeds on Facebook and Instagram by the end of 2023, it announced in July.

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Twitter is making less of a shift than that, as it has embraced featured tweets in its home timeline as early as 2014, though at least some of its redesigns have also included nods to TikTok.

In a recent experiment in which a choice was made between algorithmic and chronological versions of its home timeline, it renamed the algorithmic version “For You”, the same as TikTok’s main page, for example.

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Twitter’s Wise said the company’s discovery efforts have largely focused on new users, who have yet to figure out which accounts to follow and generally send the company fewer signals about their interests than prolific old tweeters.

Some users have complained about “related tweets” exposing them to irrelevant partisan content and confusing which tweets were part of a conversation and which were suggested by the algorithm.


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