How Community Notes reduce viral misinformation | Jay Baxter and Keith Coleman

0:00Jay Baxter: Outdated technology isn't going to improve a disconnected healthcare system. Next! So what will? At Optum, we wake up every day and work for better. We call it Healthy Optimism. It's how we use insights and technology to help providers focus less on paperwork and more on patients. Will we fix it all overnight? No. But we'll wake up trying, again and again, until healthcare works as one for everyone. That's Healthy Optimism. Learn more at Optum.com.

0:29Audrey Tang: Optum.

0:32Audrey Tang: This episode is sponsored by Charles Schwab. Wouldn't it be great if life's little upgrades didn't feel like a splurge? Like getting the aisle seat with extra legroom for a one-hour flight, ordering extra guac with zero hesitation, or paying for assembly on that swing set because you'd rather spend your Saturday doing literally anything else. It's called living filthy comfortable, and whatever filthy comfortable looks like to you, Schwab can help you find it with investing, trading, education, and human help all in one place. Learn more at schwab.com. Investing involves risk, including loss of principle.

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2:04Jay Baxter: On April 23rd, 2013, a hacked Associated Press Twitter account sent out a single fake tweet. Two explosions at the White House. President Obama injured. None of it was true. But it took less than three minutes for the market to lose roughly$136 billion in value, simply gone, before the correction ever caught up to the lie. That's the thing about disinformation. It doesn't check your party registration before it does damage. And it can wreak lasting havoc in mere moments, before you even realize what's happened. This is TED Tech. a podcast from TED. I'm your host, Sherelle Dorsey. More than a decade after that AP tweet, the tools for spreading disinformation have only gotten faster, cheaper, and harder to spot on site for the average user.

3:03Jay Baxter: Doctored headlines on current events, AI-generated images of damaged warships, deep fake audio of world leaders, all of it moving at the speed of a repost. So who's actually building the guardrails online to keep inaccurate and deceptive information from spreading across the web. Today's featured conversation gets at some answers. On the TED stage, guest curator Audrey Tang sat down with Keith Coleman, VP of Product at X, and Jay Baxter, the machine learning lead behind Community Notes. Even if you haven't used Community Notes yourself, you might have seen them floating around. They're the crowdsourced fact-checking system that's become one of the most closely watched experiments in fighting disinformation at scale.

3:53And one that, by Keith and Jay's account, people on both the left and the right actually trust.

4:00Jay Baxter: But before we dive in, a quick break to hear from our sponsors.

4:08Jay Baxter: Hi there, it's Adam Grant from TED's Rethinking Podcast. And this episode is brought to you by ServiceNow. I get to spend my days studying how people think and what it actually takes to change our minds. It's work I find deeply meaningful. But even in meaningful work, there's still busy work. The admin, the repetitive processes, the invisible load that pulls attention away from what really matters. That's where ServiceNow's AI specialists come in. They don't just tell you what you should do about your busy work. They actually do it. Start to finish. Cases closed. Requests handled. No extra work for you.

4:41Jay Baxter: To learn how to put AI to work for people, visit servicenow.com.

5:10Keith Coleman: hidden fees. Be smart. Get wise. Download the Wise app today. T's and C's apply.

5:21Jay Baxter: And now, Keith Coleman and Jay Baxter take the TED stage with Audrey Tank. 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.

5:38Audrey Tang: Great. So let's look at the community note. That's a note. So what is it?

5:43Keith Coleman: 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 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.

6:16Keith Coleman: 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, they can add context to posts that are correct but otherwise misleading.

6:40Audrey Tang: Okay, so it's a context engine for news, but is it also for official accounts or ads or any kind of post?

6:47Jay Baxter: 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 deepfake audio of world leaders. It covers lighter subjects like entertainment, fashion, etc. 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.

7:32Jay Baxter: 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, you know, 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.

7:48Audrey Tang: 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?

8:08Jay Baxter: 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. So that's where I was following, that's where the world was following. I just, you know, 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 in a, you know, amidst polarization?

8:44Jay Baxter: 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.

9:20Jay Baxter: Scale was an issue. Typically, people could review, I don't know, 10, 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.

9:47Audrey Tang: 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?

10:06Keith Coleman: 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, like there's no override button.

10:37Keith Coleman: So it's really the, you know, 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, you know, something like that would just end up showing really biased notes.

11:13Keith Coleman: And for here, our algorithm actually takes advantage of partisanship and polarization. So for any community note on a polarizing topic, basically there's always going to be someone out there who's really predisposed to disagree with that note. So before they're going to rate it helpful, they're going to go fact-check it from every angle possible. They're going to really check the sources in a lot of detail. And as a result, the notes that are actually found helpful in this way tend to be really accurate, tend to use primary sources, and tend to be pretty neutral in their language.

11:45Audrey Tang: Okay. So people on both sides, after turning polarization into essentially fuel, right, geothermal energy, uplift something and both sides are happy. But the person getting noted may not be very happy. So say a head of state gets noted and the head of state happens to have the phone number of your CEO and just calls Elon and say, take it down by tomorrow morning. What would he say?

12:15Jay Baxter: Yeah, so those emails like that, calls or whatever, they do come in. Fortunately, the answer is really simple. We have no override button. So if you're not happy with a note, you need to take it up with the people. And this was kind of a crazy idea when we started. We went into a room full of trusted safety people, and we're like, hey, so the notes that show are going to be the ones that the people decide, and we can't take it down. There's no veto. There's no veto. And, you know, they're like, what? Are you serious? What if there's a bad note? But I think, you know, the point was, if it's the tech company's opinion, why is anyone going to trust it?

12:55Jay Baxter: It needs to be the people's opinion. And so we stuck to that principle. Everyone got behind it. And, yeah, we have no way of changing the status of a note. Okay. Which is wonderful.

13:04Audrey Tang: Okay, it is wonderful. Yeah. And so what happens to the post after it gets noted?

13:10Keith Coleman: You can just see this thing's going super viral at the start, all the way up until it gets noted. Basically, after that point, it totally flattens out, gets almost no more views. And the kind of crazy thing about this is it's actually not getting downranked by our ForYou algorithm, the post. This is actually just because of what we call organic user behavior, where basically people are realizing now that the post is incorrect because the note's on it, so they're just liking it less and reposting it less. So I think this is really cool. And one thing that I also love is because our data is totally open.

13:43Keith Coleman: Actually, a lot of researchers from around the world have looked into this and found the same thing. So people from Stanford, MIT, UW, and Paris, and Luxembourg, have all actually found a very similar thing, that reposts will drop by about 50 % or 2x after a note's applied. And this is really big in the scale of social media, like, you know, 1 % or 5 % when would be, you know, pretty big in the scale of typical A, B tests. So one thing that I think is really heartening about this is that we know from this and some other studies that actually, you know, people are not just entrenched in their beliefs.

14:20Keith Coleman: When a note is applied to a post, they'll actually agree with the core claims in the post less. And I think that's really cool. And, you know, I guess there's a little bit of a mixed blessing here, though, because actually post authors will also be more likely to delete their posts after they get noted. So in that way, the best notes actually get seen very infrequently. So I'm torn about that because just for me personally, I think not everyone agrees on this, but for me personally, I'd rather see a post and a note than neither at all, just because that's probably not the only time in the world where you're ever going to see that particular wrong claim.

14:53Keith Coleman: So maybe you'll see it off X somewhere in another post. And just for me, seeing a lot of notes has kind of increased the skepticism that I have when reading things.

15:02Audrey Tang: Okay, they serve as inoculation, essentially.

15:05Jay Baxter: Yeah, it's kind of a big deal if this happens organically. People often assume the world is very polarized, certainly it feels very polarized. But people here, they're just making a choice. When they see a post, they see a correction, they're like, yeah, things are wrong, I'm just not going to share it. And that's happening across the political spectrum. And we've seen that pattern again and again. When we first were designing the products, we did interviews with hundreds of people, left and right. And it was really obvious that most people just want to know what's going on in the world. They know they're consuming incorrect stuff.

15:38Jay Baxter: They just want to sift through it. And this is just showing that in action. When given information, they're going to try to make a good decision. And so I think people often assume, like, man, it must be tough to work on in this space of misleading information. It must get sad all the time, whatever. It's actually, like, I feel very optimistic working on it because we see there's quite a lot of agreement. People are actually quite reasonable. Wow.

16:00Audrey Tang: Okay. So the PPM is going lower.

16:03Jay Baxter: It seems like it's lower than it might feel.

16:06Audrey Tang: Amazing. So let me now push on a more cynical take. Anyone who spent five minutes on the Internet is probably thinking now there's going to be a way to game this, maybe many ways to game this. And just one example, I co-wrote a paper called Malicious AI Swarm. It talks about one person farming like 5 ,000 agents, the machine kind, and then they are some coded left, some coded right. They behave completely normally. They contribute even to committee notes. And just when the controversial issue or election happens, then they manufacture a surprising agreement and just note something that is actually true.

16:49Audrey Tang: How do you deal with that?

16:50Keith Coleman: Yeah, manipulation's a real thing. I mean, people are always trying to game social media algorithms, and community notes is no exception. So I think one thing to call out is that surprising agreement mechanism does provide a bit of a defense against a more naive attack than the one you described. There's a lot of people with the same view all piling on, trying to get an incorrect note showing. You know, that's not going to work. But for a more sophisticated attack like the one you described, we do have a lot of defenses in place. So just to name a few, We do things like requiring a verified phone number from a trusted carrier just to increase the probability that we're dealing with real humans.

17:24Keith Coleman: We look for raters who have rated things really similarly in the past, and actually we might treat them as the same person just to limit the influence of really similar behavior. Another thing is we can look at random samples of raters, and if they're rating things very differently than self-selected, possibly malicious raters, then that's a very important signal. and we have other things too, like there's rate of reputation to deal with low quality people, but I think another key thing to call out is even with all these defenses, you know, committed notes are incorrect sometimes. Now, because it is really rare, we actually get the self-correcting property where the incorrect notes attract a lot of attention and they'll draw a lot of raters to go quickly rate them not helpful and then they'll stop showing.

18:10Keith Coleman: And I think that self-correcting property is super important also just, you know, a breaking news situation, right? Something that was true a few hours ago may not be anymore. So it's great that notes are not set in stone.

18:21Audrey Tang: Okay, so notes being wrong is noteworthy, and people recursively improve. Indeed, I've seen it happening on X for quite a while, because I was a long-time contributor. It just feels like magic. It's like Wikipedia or Gorkypedia, when many people swarm into some controversy. It just gets really nice. But what about the other situation? In a niche topic, just developing fast, there's just not enough attention to bootstrap the initial surprising agreement. So I've also seen like five hours, 10 hours go by without any consensus at all. And so how are you going to tackle this speed problem? Because as you pointed out, if it comes the next day, it's already gone.

19:07Keith Coleman: Yeah. Well, first, just to level set on speed, I think Keith already mentioned, The previous state-of-the-art fact-checking would often take on the order of days, and community notes is usually more on the order of hours. So it is already quite a bit faster. Notes can appear as often as about 20 minutes on a brand-new post, but they can actually appear instantly if there's already another note out there that's matching on a URL or image or video. I think on top of that, one thing that people really, really like is And if someone actually sees a post and engages with it before a community note is appearing, we'll actually send them a push notification later.

19:43Keith Coleman: So they get the correction as soon as the note comes out. Now, even with all that, I think it's super important for us to keep making community notes faster. People want instant context, and rightfully so. So to that end, what we've done last year is we've actually opened up an open API for AI contributors. And this is a little bit of a crazy thing. and the totally open spirit of community notes, just like a regular person can be writing notes, we let regular people write their own AI note writers and submit notes to our system. And what we've seen so far is it's actually working really well. The notes are really fast, and they're quite good.

20:21Keith Coleman: But definitely, because it's AI, they're wrong some of the time. So the way we treat this now that's been working well is we still have that human layer, where humans rate the notes in the same way as any other human authored note. And what we're working towards now is a way for AI and humans to collaborate more effectively to co-write better notes faster.

20:42Audrey Tang: So humans are not just downvoting or upvoting, but working with AI models.

20:46Jay Baxter: Yeah, the idea is they can, like, can we have humans and AI co-write these things together, co-create them together? And does that allow us to do this at a much faster speed and larger scale? If there's demand for a note, like people are requesting a note on a post, AI will take a first shot at it. Humans can write to you, but AI will take a shot. And a bunch of people, they rate it and they make these suggested improvements. They can also leave suggestions on style or tone. So they can say, like, hey, I think this source is biased, or I think you should use a primary source. It's going to be more trustworthy.

21:19Jay Baxter: And then the AI takes that, regenerates a note, and usually gets it right. And what's cool about this is, first, you get a better note on this post people care about. But two, all of those corrections, all those suggestions, are training data that you can feed back into the AI. So you can make it less likely to make that mistake again, you can make it better at researching in the first place, and also you can make it more neutral, less biased. So all these human suggestions, they make better notes and they make better AI.

21:49Audrey Tang: Okay. So just to play it back, this is not Grok helping humans to such a degree that it takes over all the judgment costs. It is basically human teaching AI, collaborative learning, so that the translation between communities, like climate justice on one side, biblical creation care on the other, the AI model learns how to translate. And then what? They become better at this kind of translation? Is this a new way to reward AI models? How does it work?

22:22Keith Coleman: Yeah, there's this thing that we sometimes call reinforcement warning from community feedback as opposed to just reinforcement learning from human feedback, which maybe would use potentially a smaller bias than a non-representative people. And basically, in the case of community notes, what it would look like is directly training the model to be writing notes that would be maximally likely to be found helpful by a simulated set of raters who have typically disagreed in the past.

22:46Audrey Tang: Okay. Well, that's really nice. Yeah, it's cool. So still, someday I just open X. and I just see peak slop. Like, the marginal cost for generating synthetic media, even synthetic intimacy, is now falling so fast. And so it's just sometimes I feel, personally, that whatever the corrective mechanism we invent is not going to be fast enough for this kind of peak slop situation. So why should anyone here believe that community nodes and collaborative nodes will evolve to meet the demand?

23:31Jay Baxter: Yeah, so definitely in the last six weeks or so with the Iran conflict, we've seen the biggest surge in synthetic media that I've seen, at least in the kind of misleading info space. And I will say, like, we're on the frontier here. So this is the highest scale, highest speed solution that exists. These are new problems. So we don't know what's going to work. We can't guarantee the problem won't be solved. But I think there's a bunch of reasons to be optimistic. For that problem, like synthetic media surge, we can both scale up the corrections and we can both change the fundamental incentives and dynamics of the system.

24:12Jay Baxter: So in terms of scaling corrections, we talked about AI. Just to put some numbers on that, in the last four months alone, we've doubled the number of notes that are showing on X. So that's not trivial for a scaled service, to 2X in four months. I think there's clearly headroom on that. Is it 10X, 100X? I don't know. But there's clearly headroom to grow. The other thing on the incentive side, one of the reasons people post these things is they can make money off of it through creator revenue sharing programs. And so we've recently put into place some changes to the policies there where if your post is noted, you can't make money off it.

24:52Jay Baxter: Also, if you post AI-generated footage of a war or conflict and you do not clearly call it out, you are suspended from the revenue-sharing program for three months. If you do it again, you're suspended forever. And so that's kind of a big deal. Those will shape the underlying motivations people have.

25:10Keith Coleman: That's huge.

25:18Jay Baxter: And this episode is brought to you by ServiceNow. I get to spend my days studying how people think and what it actually takes to change our minds. It's work I find deeply meaningful. But even in meaningful work, there's still busy work. The admin, the repetitive processes, the invisible load that pulls attention away from what really matters. That's where ServiceNow's AI specialists come in. They don't just tell you what you should do about your busy work. They actually do it. Start to finish. Cases closed. Requests handled. No extra work for you. To learn how to put AI to work for people, visit servicenow.com.

26:17Keith Coleman: Visit Schwab.com to learn more.

26:47Keith Coleman: Download the WISE app today. T's and C's apply.

26:55Audrey Tang: We've been talking about defense, right? 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.

27:19Jay Baxter: 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.

28:01Jay Baxter: And we see this agreement across a lot of topics. We see, like things that you think of as controversial. We see it across immigration, across the economy, taxes, international conflicts, etc. There really is a lot of agreements out there. Not on everything, but there's quite a bit of it. And the concept is, like, if we can identify that. Like, we don't need to boost this to start. Like, just, like, 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.

28:41Keith Coleman: Yeah, that's a really good point. I think just in the same way that community notes 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.

28:58Audrey Tang: 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 apply this engine beyond social media? Can you paint a picture of how that would look like?

29:34Jay Baxter: 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. And yeah. Yes. There's a lot of agreement on these topics. because if all we did was pursue the areas of agreement, I think people would be pretty happy with the direction the world was going. And so, you know, my hope is with programs like this, if we can identify common ground at internet scale, it'll make it a lot easier to create a future that humanity likes. And so hopefully we can help with that.

30:17Audrey Tang: And with that, Jay, Keith, thank you for being our best builders and showing us that a pro-social media future is not in some sci-fi. It's already here. Thank you.

30:29Jay Baxter: Thank you. Thank you, Audrey. That was Jay Baxter and Keith Coleman in conversation with Audrey Tang at TED 2026. Community notes are crowdsourced, radically transparent, and allergic to any override button. They're one bet on how we get out of this disinformation mess. But it's not the only bet being made. And some proposed solutions have hit road bumps. About a year before this conversation, Meta brought its own version of Community Notes to Facebook, Instagram, and Threads, retiring its old third-party fact-checking program in the process. It hasn't been a clean win. Meta's own oversight board has pushed back, warning that Community Notes alone aren't a real substitute for professional fact-checking, especially in places without the dense, highly engaged user base that makes the model work on X in the first place.

31:25Jay Baxter: Other efforts are trying to solve this further upstream before a fake image or video ever gets posted at all. A coalition that includes Adobe, Google, Microsoft, OpenAI, and the BBC has been building out a standard called C2PA, essentially a digital nutrition label for media cryptographically attached the moment a photo or video is created. So anyone downstream can verify where it actually came from. In that malicious AI swarm research Audrey mentioned mid-conversation, the thousands of coordinated fake accounts learning to game a system like this one, is now its own growing field. Researchers trying to outthink mass manipulation before it happens instead of cleaning up after.

32:09Jay Baxter: None of this is a finished system. These tools are being built in public and stress-tested in real time. And the reality is, the issue will not be solved by just one company, a singular algorithm, or rule. It's going to take all of it. Probably all of us, too, to keep things in check.

32:32Jay Baxter: TED Tech is a podcast from TED. This episode was produced by Rahima Nasa. Our editor is Alejandra Salazar. And the show is fact-checked by Julia Dickerson. Special thanks to Constanza, Gallardo, Daniela, Bellareso, Maria Ladias, Tanzika Sangmanivan, and Roxanne Hilash. If you're enjoying the show, make sure to subscribe and leave us a review so other people can find us too. I'm Sherelle Dorsey. Let's keep digging into the future. Join me next week for more.

33:12Jay Baxter: to you by ServiceNow. I get to spend my days studying how people think and what it actually takes to change our minds. It's work I find deeply meaningful. But even in meaningful work, there's still busy work. The admin, the repetitive processes, the invisible load that pulls attention away from what really matters. That's where ServiceNow's AI specialists come in. They don't just tell you what you should do about your busy work. They actually do it. Start to finish, Cases closed. Requests handled. No extra work for you. To learn how to put AI to work for people, visit servicenow.com.

34:07Keith Coleman: you want. Because at Schwab, you can invest your way. No matter your goals or appetite for investing, Schwab has everything you need, all in one place. Visit schwab.com to learn more.

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