TED Talks Daily
How Community Notes reduce viral misinformation | Keith Coleman, Jay Baxter
10 Jun 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: 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.
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Chapter 2: 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.
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Chapter 3: 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. You know, this isn't like a famous person.
They went out there, saw the White House did something wrong, typed in this note, put it up there, and then suddenly leaders of the free world changed their public statement. That's like a superpower for people, so you can see why they're motivated to contribute.
Yeah, well ... So a teenager, I heard, caused that retraction, and it really is a superpower. But what is the mechanism? Can you take us back 10 years ago, before this superpower gets invented and distributed? What caused the invention?
Yeah, I mean, the origin for me goes back to 2016. I was just a Twitter user then. I was following the 2016 election. There were three televised debates that year, but every day there was a debate on Twitter.
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Chapter 4: How does the surprising agreement algorithm function?
So that's where I was following, that's where the world was following. I remember getting a lot of good information, but it was also hard to tell what was true. And I was thinking, just sitting on the outside thinking, like, how is the world going to solve this problem? Like, damn. Like, how are we going to do this in a way that works and that people feel is fair amidst polarization?
So then fast forward three years. I was working at Twitter at that point. And the industry had tried a lot of stuff by then. Facebook had built a huge fact-checking program Twitter was working with fact-checkers, and we also had internal teams that would try to review posts and decide whether they were or were not misleading. And there were a bunch of issues with it.
It was just very clear these solutions were not solving the problem. There were issues with speed. So typical fact checks, just to put it in perspective, were often coming back in two to four days, which is like infinity in internet time. Scale was an issue. Typically, people could review, I don't know, 10, an order of 10 posts or topics a day.
And even if you could solve those, trust was the fundamental issue. There were just a lot of people who did not want or trust tech companies to be deciding what was or was not accurate. And so I was managing a team at this time, handed that off, and just went to go prototype crazy new ideas, one of which became Community Notes.
Okay, so the crazy idea, just to play back, is to think from the bottom up, asking people to trust random strangers on the Internet. And amid a very high PPM, polarization per minute, environment, as you just alluded to, why would people trust random strangers?
Yeah, it's a really good question, and it's one we got all the time getting started, but the reality is people do trust Community Notes on both sides of the political spectrum, and I think there's a couple big reasons why. One is the process behind it. So it's totally open, transparent, verifiable.
You can actually, and this is pretty wild in the world of social media, you can actually download the real algorithm code that runs in production 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. There's no override button. So it's really by the people.
I think secondly, the notes are just really good. So they speak for themselves. They tend to be really accurate. And the main reason behind that is I think the principle behind the algorithm that doesn't ingest any sort of external authority, it actually decides what notes to show by looking at agreement from people who have disagreed in the past.
And sometimes we call that surprising agreement or bridging. And one thing that's really cool about this algorithm, if you compare it to something like a more naive upvote-downvote system, like a majority rules type of thing, Something like that would just end up showing really biased notes. And for here, our algorithm actually takes advantage of partisanship and polarization.
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Chapter 5: What challenges did Community Notes face in its early development?
Defending against all those manipulations and engagement through enragement and so on. But is there a future in which social media, instead of pitting humans against one another, puts people and connects them with each other, elevating the voice that bridge? And you're like, we have just a demo.
Yes, we are building this. This is an awesome future. So we have a pilot running. The idea is, so in community notes, we find the kind of like corrections or context that's helpful to people from different points of view. What if we could find the ideas or opinions that are liked by people from different points of view?
And when it happens in the pilot program, the post will just get a call out saying liked by people from different perspectives. And we see this, obviously, people were very happy to see Delta not allow Congress to skip the TSA line until TSA was funded. And we see this, yes. Yeah, you're among millions of people who also feel this way. And we see this agreement across a lot of topics.
Things that you think of as controversial, we see it across immigration, across the economy, taxes, international conflicts, et cetera. There really is a lot of agreement out there. Not on everything, but there's quite a bit of it. And the concept is, if we can identify that, we don't need to boost this to start. Just show people when there's agreement on something.
First of all, I think they'll find it interesting. It's a curiosity. Second, it might incentivize more of that. Maybe people will try to speak more in a way where they can find that agreement and get more momentum behind those ideas.
Yeah, that's a really good point. I think just in the same way that community notes spread less, even though there's no... Community notes caused posts to spread less, even though there's no downranking in the algorithm, I think you'll probably see something in Algus here where there's just a positive second-order effect from making that common ground, common knowledge.
So it's a common knowledge engine that turns polarization into what we can all live with. This is truly visionary. And think about it, because this thing is open source, it is open data, so it means that not just X, but rather blue sky, true social, everybody, can just plug in that stream, and so that AI can learn from that, and then connect the communities back together.
So what if we applied this engine beyond social media? Can you paint a picture of how that would look like?
Yeah, so where my head always goes is, imagine just for one session of Congress, everyone just focused on delivering where there was agreement, whether it's immigration, taxes, whatever. I think people would be stoked. There's a lot of agreement on these topics. If all we did was pursue the areas of agreement, I think people would be pretty happy with the direction the world was going.
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