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X Tests Feature Highlighting Posts Liked by Users With Opposing Views

X Tests Feature Highlighting Posts Liked by Users With Opposing Views

X announced on Thursday that it is rolling out an experiment designed to identify and highlight posts that receive likes from people who typically hold opposing views. This new feature builds on X’s crowdsourced Community Notes program, which is currently used to flag content considered misleading or inaccurate.

Beginning Thursday, a select group of Community Notes contributors will see prompts encouraging them to rate and provide feedback on certain posts. These prompts are triggered based on the post’s early Like activity, specifically targeting posts that attract engagement from users with differing perspectives.

Contributors will be asked to share what aspects of the post they appreciate or disagree with, selecting from options such as “I learned something interesting” or “I don’t agree with it.” This input will help X build an open source algorithm that can detect posts resonating across diverse viewpoints.

Aiming to Bridge Divides and Foster Understanding

“People often feel the world is divided, yet Community Notes shows people can agree, even on contentious topics,” X stated in a blog post. “This experimental new feature seeks to uncover ideas, insights, and opinions that bridge perspectives. It can bring awareness to what resonates broadly. It could motivate people to share those ideas in the first place. Ultimately, it could help move the world forward in ways that the people want.”

X introduced Community Notes in 2022 after Elon Musk’s acquisition of the platform, moving away from traditional fact-checking methods. Since then, the feature has been adopted by other major platforms including Meta’s Facebook, Instagram, and Threads.

Author’s Opinion

This experiment by X signals a thoughtful approach to addressing the echo chamber effect on social media. By highlighting content that appeals across ideological lines, the platform may encourage more nuanced conversations and reduce polarization. However, the success of such initiatives depends on transparent algorithm design and genuine user engagement to prevent manipulation or superficial consensus.

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