Twitter expands recommendation push with new tests

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SEPTEMBER 20 (Reuters) – Twitter (TWTR.N) is expanding its recommendation of posts from unfollowed accounts, the social media company announced on Tuesday.

As part of the extension, tools are also being developed to allow users to control and provide feedback on this content.

“With millions of people logging on to 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.

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The testing comes as social media companies this year double down on what they call “unaffiliated content,” or posts from accounts unfollowed by users, after short video app TikTok rose to prominence and relied solely on algorithm-driven suggestions.

Among the new designs Twitter has been testing is placing “related tweets” under conversations on a tweet detail page, said Angela Wise, senior director of product management, who is responsible for “discovery” for the service.

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

Competitor Meta Platforms (META.O) aims to double the percentage of recommended content that fills its users’ feeds on Facebook and Instagram by the end of 2023, it announced in July. Continue reading

Twitter is making less of a big shift than that, having added featured tweets to its home timeline as far back as 2014, though at least some of its redesigns also include nods to TikTok.

In a recent experiment that presented a choice between algorithmic and chronological versions of its home timeline, it renamed the algorithmic version “For You,” just like TikTok’s main page, for example.

Twitter’s Wise said the company’s discovery efforts are primarily aimed at new users who have yet to figure out which accounts to follow, and generally sends fewer signals to the company about their interests than prolific longtime tweeters.

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

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Reporting by Katie Paul; Editing by Jonathan Oatis

Our standards: The Thomson Reuters Trust Principles.

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