Twitter expands its recommendation feature
Twitter is continuously working for its users to make it easier for them to find the conversations and accounts that interest them the most. Recommendations are one of the manners in which twitter does that.
If you've seen a Tweet you enjoyed from somebody you didn't follow, you've likely seen a recommendation. Consider them customized ideas that are displayed to you in light of moves you make on Twitter.
"With millions of people signing up for Twitter every day, we want to make it easier for everyone to connect with 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 "unconnected content," or posts from accounts users do not follow, after short video app TikTok shot to prominence depending completely on algorithm-driven suggestions.
Among the new plans Twitter has been trying its placement of "related tweets" underneath conversations on a tweet detail page, said Angela Wise, a senior director of product management liable for "discovery" on the service.
Twitter is additionally exploring different avenues regarding an "X" tool that users might click to eliminate recommended tweets they do not find interesting from their timelines, the blog post said.
Competitor Meta Platforms is planning to double the level of recommended content that appears in its users feed on Facebook and Instagram toward the finish of 2023, it revealed in July.
In one ongoing examination introducing a decision among algorithmic and sequential variants of its home timetable, it renamed the algorithmic rendition "For You," equivalent to TikTok's fundamental page, for instance.
Twitter's Wise said the company’s revelation endeavors were to a great extent focused on new users who presently can't seem to sort out which accounts to follow and convey the company less signals about their interests than do prolific long-term tweeters.
Some users have grumbled about "related tweets" exposing them to irrelevant and unwanted hyperpartisan content and making disarray over which were part for a discussion and which were recommended by algorithm.