X explores ‘Community Notes’ to identify popular posts

Subset of 'Community Notes' contributors will begin seeing new callout in product when a post is gaining attention
A 3D-printed miniature model of Elon Musk and the X logo are seen in this illustration taken January 23, 2025. — Reuters
A 3D-printed miniature model of Elon Musk and the X logo are seen in this illustration taken January 23, 2025. — Reuters

Elon Musk—owned X (formerly Twitter) is testing a new way to use “Community Notes,” its crowdsourcing fact-checking system, to highlight well-liked posts from users with different perspectives.

Taking to X, the "Community Notes" account announced the launch of a pilot test where select contributors would be able to rate posts by answering questions about why they either like or don’t like that particular post.

To note, the system is relatable to how “Community Notes” fact-checking works. Instead of simply letting users upvote or downvote posts for accuracy — something that could be easily gamed if like-minded contributors teamed up to promote their views — Community Notes uses something called a “bridging algorithm.”

Currently, X intends to view whether Community Notes could help identify the best posts. According to the company, a subset of Community Notes contributors will begin seeing a new callout in the product when a post is gaining attention in the form of Likes.

In a post on the "Community Notes" X account, the company explains the experiment could bring awareness to “what resonates broadly.”

The post stated: “People often feel the world is divided, yet Community Notes shows people can agree, even on contentious topics. This experimental new feature seeks to uncover ideas, insights, and opinions that bridge perspectives.”

“Following the path we used to develop Community Notes, we’re building in public with a small pilot so that this concept can be shaped by the people. We look forward to learning and iterating with you all as we do with Community Notes every day.”

Contributors will be able to rate and give feedback about the post, which will inform the algorithm if the post is being well-received by people with different perspectives.