Today, the feed shows only tweets and interactions from the user’s For You section, a constant stream that feels like a one-sided narrative. The platform has echoed a trend seen elsewhere, copying a model that prioritizes a personalized rhythm of content. TikTok has already tested a chronological feed in some regions, and now Twitter appears to lean toward suggesting posts that align with a user’s interests. Yet a flood of content from a single, polarizing figure has crowded many timelines, cluttering feeds with material from one account regardless of whether it is followed.
With a vast following of 128.8 million, Elon Musk stands as one of the most influential voices online. The platform he acquired continues to run under his guidance, and his own posts draw intense engagement. In recent days, however, users noticed a shift: the recommendation wall seems saturated with Musk’s tweets, even for accounts that do not follow him. This saturation has sparked widespread discussion about how the algorithm surfaces content and whether it amplifies certain voices over others.
The company has not publicly disclosed the exact reasons for the shift, but observers point to the owner’s influence as a key factor. There is talk that Musk perceived less traction for his messages, especially when the conversation around him cooled. Reports describe a gathering of engineers at the company’s offices to review the issue. Sources indicate the engineers did not find a systemic bias in the algorithm against Musk; instead, they suggested a potential decline in user interest in his posts as a contributing factor. Musk’s response to the discussion included decisive personnel action aimed at rebalancing the team dynamics.
Beyond the internal debate, the owner directed staff to monitor how often tweets are recommended and to gauge the reach of those messages. Statements from leadership highlighted moments when a large portion of Musk’s tweets might not appear in standard feeds. Other steps were noted, such as adjustments to retweet amplification, the handling of hashtags, and changes to how certain content types like photos or videos are treated in the distribution flow. Whether these moves also involved artificially extending reach remains unclear.
Less visibility and shifting usage
Several months ago, Twitter introduced a metric showing how many views each tweet receives. The aim was to reveal the limited impact that posts from users with large followings can have on the broader audience. People familiar with the platform told outlets that this visibility feature could help curb excessive amplification by quietly trimming engagement loops. In parallel, the company trimmed some interface elements tied to sharing and reshares, a move seen as part of a broader effort to recalibrate what gets pushed onto users’ walls.
Engagement patterns may also reflect a broader trend in social media use. A study from the current year indicates a dip in overall activity on major networks in certain regions, with changes in how people discover, follow, and interact with content. Shifts in platform policies, frequent updates, and even occasional outages can contribute to a sense of instability in the user experience, prompting some to re-evaluate how, when, and what they consume online.
Recognizing the climate of user feedback, Musk has signaled further adjustments to the recommendation algorithm. The aim appears to be a more patient, deliberate approach to surfacing content, with a focus on balancing enthusiasm for high-profile accounts with the broader ecosystem of creators and topics. While the specifics remain fluid, the stated goal is to optimize the pace of recommendations and to encourage a more thoughtful interaction with the feed.