How Community Notes reduce viral misinformation | Keith Coleman, Jay Baxter
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What is the main topic discussed in this episode?
You're listening to TED Talks Daily, where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hugh. X, formerly called Twitter, is now using Community Notes, a crowdsourced fact-checking system. The company's algorithm architect Jay Baxter and its VP of product, Keith Coleman, built it, starting with the question, what if the people got to decide what's true? If people don't trust tech companies to draw the line, could they draw it themselves?
You can download the real data, the community notes and ratings, run the code on the data to verify that there's no funny business that we're doing on our end, like there's no override button. So it's really by the people.
It's an idea that has earned genuine interest and trust across the political spectrum, even as it's become entangled in a larger, more contentious debate about the dismantling of professional fact-checkers. In this conversation, Jay and Keith sit down with TED guest curator and civic technologist Audrey Tang to discuss how Community Notes actually works.
If we can identify common ground at internet scale, it'll make it a lot easier to create a future that humanity likes.
They also talk about what they're working on next. And stick around.
What is Community Notes and how does it work?
After the talk, we caught up with the TED guest curators Audrey Tang and Divya Siddharth, who share a few more thoughts and takeaways on community notes for us to consider. That's all coming up right after a short break.
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And now, our conversation of the day.
You know, we built Community Notes because we wanted to build a better informed world. And as it scales to more parts of the internet, that means more people have access to accurate information.
Great. So let's look at Community Notes. That's a note. So what is it?
Yeah, so this is a real example of a community note we're looking at. So basically here the post on the left is about Iran and it's saying the USS Lincoln has been damaged and there's casualties. But actually the image is AI generated. So this thing on the right here that says readers added context, they thought people might want to know, that's a community note. And what it's doing there is it's actually giving a lot of specific details about what's wrong in the image. And it turns out that that level of detail that it goes into is a big reason why people on both sides of the political spectrum actually trust community notes more than a generic misinfo warning. The way these get here in the first place is they're actually written by a regular user, a community notes contributor.
And before they show on the platform to everyone and attach on the post, they are rated helpful by people from different perspectives. So they're not shown unless that happens. Another quick thing to call out is actually a lot of the best notes are not just fact checks.
Why do people across the political spectrum trust Community Notes?
They can add context to posts that are correct but otherwise misleading.
Okay, so it's a context engine for news, but is it also for official accounts or ads or any kind of post?
Yeah, so a really important principle of the program is that all posts are eligible. That means posts from heads of state, posts from our company can get noted. As Elon likes to point out, his posts get noted. It regularly identifies AI-generated imagery. It's been a ton of that recently with the Iran conflict. It's detected deep fake audio of world leaders. It covers lighter subjects like entertainment, fashion, et cetera. We've even had multiple notes on both recent White House administrations. And at least in one case, the White House actually took down the Post, issued an updated statement, and you can imagine, like, there was a person, a random person on the internet wrote that note.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:04–1:19
2
What is Community Notes and how does it work?
1:19–3:25
3
Why do people across the political spectrum trust Community Notes?
3:25–5:11
4
How does the surprising agreement algorithm function?
5:11–22:50
5
What challenges did Community Notes face in its early development?
22:50–29:33
Speakers
6 identifiedMore from TED Talks Daily
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