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For news, algorithmic social networks are a failed experiment

Dec 04, 2024 - fastcompany.com
The article discusses the changes in Twitter's algorithm since Elon Musk's takeover, and the rise of alternative social networks like Meta's Threads and Bluesky. It criticizes Musk's influence on Twitter's algorithm, suggesting it prioritizes his own tweets and suppresses news-related content. Threads, which has 275 million monthly users, is described as a more peaceful platform, but its algorithm's lack of emphasis on news and politics is seen as a downside. Meanwhile, Bluesky, a spin-off from Twitter, has grown to 24 million users, offering a more traditional, chronological feed and allowing users to discuss current events.

The author suggests that introducing algorithms into a social feed may cause more problems than it solves, particularly if timeliness is important. Bluesky's success is seen as evidence that a return to basics might be good for business. The article concludes by suggesting that while Musk's Twitter may be beyond repair, other social networks could benefit from adopting Bluesky's user-centric approach.

Key takeaways:

  • Twitter's algorithmic timeline, which was introduced in 2016, has been manipulated by Elon Musk to promote his own tweets and suppress others, leading to a decline in the quality of the user experience.
  • Threads, a new social network by Meta, has grown rapidly by allowing users to sign in with their Instagram accounts and focusing on a more peaceful and uncontroversial atmosphere, but it has been criticized for its lack of timely news content.
  • Bluesky, a social networking startup and spin-off from Twitter, has seen a surge in users who prefer to discuss current events on a platform not operated by Elon Musk, and its success suggests that a return to basics might be good for business.
  • Meta is reportedly planning to introduce its own version of Bluesky's Starter Packs, which allow users to curate and share lists of users, indicating a potential shift towards a more user-centric approach in social networking.
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