TikTok has announced that Footnotes, its answer to X"s Community Notes, is officially getting started. Over the next few weeks, US users will begin seeing these notes on videos and will have the chance to write and rate them.
TikTok describes Footnotes as a way to bring "additional written context to video," a feature the company first announced back in April.
Footnotes bring additional written context to videos. A footnote might share a researcher"s view on a complex STEM-related topic, while another could highlight new statistics that give a fuller picture on an issue.
Here"s what the feature looks like:
The company says that nearly 80,000 users have qualified to become contributors since then. Getting into the program requires you to be in the US, be at least 18 years old, and have an account that is over six months old with no recent guideline violations.
The mechanism that decides which notes get published is a so-called "bridging system". Instead of relying on a simple majority vote, the algorithm specifically looks for consensus from contributors who have a history of "different opinions". A note only goes public when it is rated as helpful by people who do not typically agree. Contributors are also required to cite a source for their claims.
The "Footnotes" system is incredibly similar to Community Notes, X"s fact-checking system. That feature has its own history, starting as a pilot program named Birdwatch in January 2021 before Elon Musk renamed it and expanded its reach globally.
This crowdsourcing approach to content moderation is becoming something of an industry trend, with other companies like Meta launching a nearly identical system for Facebook, Instagram, and Threads.
Of course, letting the public write notes on videos presents its own set of challenges. TikTok acknowledges this and plans to use a combination of automated systems and human moderators to catch notes that violate its guidelines. Users can also report any footnote if it seems out of line. The company also states that a note will not affect a video"s performance in the algorithm.