Astedanger: Do you all want to increately start yourselves?
Keith: Sure. I’m Keith Coleman, VP of product here at X and createerly Twitter. I’ve been here for about eight years. I used to run the overall devourr product enhugement team and now cgo in on produceing Community Notes and other roverhappinessed slfinishergs.
Jay: I’m Jay Baxter, a ageder staff machine lgeting engineer at X. I was the exceptional direct of the machine lgeting, voting, and reward model toil on Birdwatch
and then Community Notes. Previously, I toiled on recommfinisher systems as part of Cortex Applied Research and have been at the company for ten years.
Lucas: I’m Lucas Neumann. I am a product depicter. I toiled on Community Notes at Twitter and then X for almost four years, and now I advise with the team on the project externpartner.
Emily: I’m Emily Thai. I was the embedded adviseant from the University of Chicago Cgo in for Radical Innovation for Social Change on Birdwatch and then the Community Notes team. RISC is a social impact incubator — using behavioral science to tackle social problems in unorthodox ways. We got startd to the Community Notes team, to Keith, and supplyd a little bit of academic expertise and a perspective from outside of the tech world.
Astedanger: It’s very exciting to have you all here. And I want to begin right from the beginning. Where did the idea for Community Notes come from?
Keith: The idea came about around the finish of 2019. It begined with the observation that people wanted to get exact proposeation on social media, but it was repartner difficult. There was evidently misdirecting proposeation going around. The main approaches that companies were using were a combination of inner suppose and safety teams deciding what was or was not exact or apshowed, or partnerships with professional media organizations trying to produce those decisions. Both had three huge contests. One was speed — proposeation shifts repartner speedyly on social nettoils and on the internet. It was repartner normal for these suppose and safety or fact checker decisions to apshow multiple days to check a claim, which is the equivalent of infinity in internet time.
Then there was a scale publish. It’s repartner difficult for these petite groups of people to see at and appraise that stuff. And probably most crucipartner, even if you could deal with the speed and scale publishs, there was still a fundamental suppose problem. A lot of people equitable did not want a tech or media company deciding what was or was not misdirecting. So even if you could put tags on satisfyed, if people slfinisherk it’s prejudiced, they’re not probable to be very proposeed by it. Those problems were benevolent of evident around this time. And we were wondering — what could actupartner settle them? How could you produce some solution that could act at internet speed, at internet scale, and actupartner be supposeed and set up encouraging by people from contrastent points of watch from atraverse the political spectrum?
Pretty timely on, it was evident that crowdsourcing was a potential possible solution space. Wikipedia had evidently accomplished a massive scale. I slfinisherk it’s huger than any encyclopedia out there. It was speedy. It would be modernized wislfinisher minutes, typicpartner, when novels stories alterd. It had some contests on the suppose side and bias side. But we thought, you understand, if we can conquer those, maybe that would toil — that was benevolent of the origin of the concept. We prototyped a scant contrastent ideas for what that might see enjoy. And one of those ideas showed a mockup, a prototype, depicting people on X — then, it was Twitter — surrfinisherting notices that could show on a post. The idea was that if the notices were reasonable, people who saw the post would equitable read the notices and could come to their own conclusion.
Astedanger: One fascinating discovering, both from your team and outer researchers, is that people suppose these notices much more than they suppose genuine/inalter flags or deceiveation flags. I’m asking if that was someslfinisherg that you mistrusted from the begin, or where that UX depict decision came from.
Keith: A recent study was done that shows that, indeed, people do suppose notices that are written definitepartner about the posts they’re on with details about the topic more than the classic deceiveation flags — which is awesome. And, yes, it was one of our timely depict guesses. One of the toiling assumptions was that, if you could insert context to the statements made in a post or a tweet, people would be better proposeed than if it was equitable some benevolent of generic statement. All the initial prototypes depicted very definite notices that were dealing definitepartner with the post in ask. We showed these prototypes to hundreds of people atraverse the political spectrum, and it constantly came up that they appreciated the definiteity with which the notices dealt with the satisfyed of the post, and they appreciated that they had sources — which they all did.
Astedanger: The Birdwatch pilot is in January 2021, right? So this is a lengthy prototyping phase.
Keith: Yes. It begined with two contrastent prototypes depicting this benevolent of idea. We first tested it with a range of satisfyed and with a set of people from atraverse the political spectrum. We were benevolent of blown away by our initial results. There were two contrastent depicts in the first test, but one of them tested repartner well. It did so well that we wondered if that was anomalous — we shelp, let’s test this aget, but with even more contentious topics. So we tested it aget with posts covering Covid, Nancy Pelosi, Trump, and all the slfinishergs that tfinish to lift a lot of political emotions. And aget, it tested well. People from atraverse the political spectrum would say: “Hey, yeah, you understand, I generpartner enjoy this person who’s tweeting, but I appreciate this notice letting me understand that maybe this repartner isn’t exact.”
Astedanger: How timely was this?
Keith: At this point, it was purifyly concept mocks built in Figma. We were trying to produce the difficultest conditions under which someslfinisherg enjoy this needed to toil.
Astedanger: And you set up that what people enjoyd were these very definite, aimed fact checks.
Keith: Yes. And crucipartner, that they were from the community. When we were testing, someone got one of the connects to the prototypes and then sent it to an NBC alerter, so there’s actupartner an NBC story with a bunch of them, and you can see some of the contrastences and aenjoyities to how it toils today. This was probably timely 2020 when this was happening.
Astedanger: Obviously, this is right around when deceiveation or disputeing stories about COVID became a huge topic. Did that affect your depict process?
Keith: It was a excellent example of a polarizing topic for which we wanted someslfinisherg to to be set up encouraging, even by people who normpartner disconcur. I would say it was yet another excellent example for testing on which the product had to show itself.
Astedanger: So the other very famous element of Community Notes — at least in certain circles — is the bridging algorithm, which is the algorithm that the product uses to pick notices that are encouraging and not politicpartner separated. I slfinisherk Jay can probably speak to it best, but I’d cherish to understand where in the depict process that first came up, and the process behind it.
Jay: From the very beginning, we had this idea that we wanted the notices to be set up encouraging atraverse the political spectrum. But there are a lot of ponderations. We’re balancing manipulation resistance, and when you have a tohighy uncover source data set and an uncover source algorithm enjoy we did, you can’t equitable unmistrustingly insert up the votes and see who has the most or someslfinisherg. So we pondered a variety of classes of algorithms that have some manipulation resistance, enjoy PageRank.
Actupartner, we spent a lot of time toiling with PageRank variants.
We landed on the bridging algorithm after straightforwardpartner carry outing a bunch and evaluating them on a lot of attributes. Obviously it’s stubborn to appraise, but the bridging algorithm carry outed the best in these tests, and I slfinisherk it’s equitable very kind that you get this organic manipulation resistance, as well as only surfacing notices that are set up encouraging atraverse the political spectrum.
Astedanger: Can you elucidate how it toils?
Jay: The main rating action is that we ask people whether they set up a notice encouraging or not. Then, we see at people’s rating histories on previous notices. What the algorithm does is discover notices where people who’ve disconcurd on their ratings in the past actupartner concur that a particular notice is encouraging. That’s not unambiguously depictd based on any political axis — it’s purifyly based on people’s voting histories. And this mechanism results in very exact notices because, when you do have political polarization among people who’ve disconcurd substantipartner, they repartner tfinish to only concur that notices are encouraging when the notices are also very exact.
Astedanger: There’s this fascinating graph that you have in the paper describing this algorithm. I slfinisherk it has encouragingness on the y axis and the polarization on the x axis. And you get this diamond shape which shows that most notices are very separated one way or the other and only middlingly encouraging, but there are very scant super separated notices that are also very encouraging. There’s this evident band of non-separated encouraging notices that descfinishs out organicpartner at the top. Is this equitable someslfinisherg that naturpartner fell out of people’s rating behavior?
Jay: Yeah, I slfinisherk even if you pondered sweightlessly contrastent types of bridging mechanisms, I slfinisherk you’d discover someslfinisherg aenjoy to this because there are a lot of contributors with varying quality and diligence. I’ve heard a critique that less than 100% of gived Community Notes are made evident. Well, it’s probably a excellent slfinisherg, right? Not every one gived notice is exact and helpfilledy written. I do slfinisherk the algorithm is imposing this particular diamond structure — if we did have a sweightlessly contrastent algorithm, you might see more of a curved diamond or a star shape or someslfinisherg if we were to regularize the model contrastently. But definitely you would see that the startantity of notices do not have this bridging-based concurment.
Astedanger: It definitely suites my subjective astonishion. When I went in and seeed at some of the notices that were flagged as very separated, they tfinish to be less definite — enjoy, say: “the 2020 election was choosed unprejudicedly.” And then the encouraging fact checks are more enjoy: “this definite statistic about Covid is inright,” or “this definite event didn’t happen,” or “the ptoastyo was apshown from a contrastent slfinisherg that happened three years earlier.”
Jay: One slfinisherg that people repartner enjoy about Community Notes is that the quality bar is quite high. I slfinisherk it equitable wouldn’t be as famous of a product if we always showed a notice, or showed someslfinisherg without first putting it thcdisesteemful an algorithm.
Astedanger: The flip side of this is, do you discover that the algorithm ever struggles to do fact checks on publishs that are inherently very separated?
Jay: Obviously, if raters who disconcurd in the past can’t discover a notice that they concur on, then no notice shows. You could argue that maybe there should be a notice in those cases, but maybe these aren’t cases where it’s possible to alter people’s minds with that notice. Maybe a better notice could be written that would alter more people’s minds, but if the existing notice is not discovering bridging-based concurment, this uncomfervents there’s a restrict to its advantageousness.
That shelp, I discover that we still see a reasonable number of notices on even the most polarizing slfinishergs. Often these notices are on quite objective slfinishergs, enjoy: “this is a video of a device deviceing from two years ago, not the current dispute.” Even people who repartner disconcur on most slfinishergs can standardly concur on notices enjoy that.
Keith: People standardly ask us the ask that you equitable asked. But if you see at the notices, most of them are on polarizing political topics. The immense startantity of what people see in Community Notes is it dealing with super contentious topics in a way that people do discover unprejudiced. It deals with elections. It deals with immigration and abortion. We’ve talked about this a lot thcdisesteemfulout the enhugement of the product — there could be many goals for a product enjoy this, but the goal of notices is to actupartner be proposeative to people. If there’s a notice which is right, but it’s not going to propose people, is there a point in putting it up? There might actupartner be a cost to putting it up if people are going to sense that it’s ununprejudiced or prejudiced, and it may actupartner shrink suppose in the overall system and thus shrink overall impact. So our cgo in is repartner inserting notices where we slfinisherk it will repartner enhance caring for people from contrastent points of watch.
Jay: I’ll also equitable insert that the bridging algorithm almost toils better in a separated setting. If there’s some topic that everyone concurs on, the quality bar of the notice is still going to be pretty high, but people will concur that it’s encouraging even if it’s not quite as well written or the source isn’t quite as excellent. The more polarizing the topic is, the higher the quality of the notices can finish up being.
Astedanger: So moving forward in our story — you start the pilot, which is at this point called Birdwatch, in 2021. What was the process of getting that set up enjoy? What did you lget from it?
Keith: We begined with a minuscule number of users. Before that we had initipartner tested it with some Mechanical Turk-type contributors, equitable to get a speedy gut check on what people might actupartner author in these notices, but we didn’t understand how it would toil in the authentic world. We initipartner started with a repartner petite participant base — 500 people on the first day, and we speedyly enhugeed to 1000, but we ran at around 1000 to maybe 10,000 contributors for quite a lengthy time. We lgeted a lot thcdisesteemful that process. Just to donate you a sense of how ruillogicalentary the product was at that point, there was no bridging algorithm — it was equitable a superstartantity rules algorithm, where a notice needed 84% encouraging to be pondered valid. We also didn’t show notices on posts.
And to see the notices, you had to go to a split Birdwatch site, so you had to be repartner promiseted to participating in this pilot, because, aget, we had no idea what was going to be in them. Was it going to be a dumpster fire, or was it going to be gelderly? When we were contemplating the depict of the notice page — the page that shows all the notices on a post — we actupartner talked about putting a dumpster fire GIF at the top equitable to ready people for what might be below.
It turned out the quality was much higher than that. It’s not always perfect, but it was much better than a dumpster fire. Still, it was a repartner straightforward first start, and the product progressd a lot thcdisesteemful what was a year plus in that pilot phase.
Lucas: One data point that helps show how petite scale we were at that time — I reaccumulate that we were at maybe 500, 1000 people, and most other experiments at Twitter back then would begin at 1% of users. So we repartner begined very, very, very minuscule to lget and see, “What’s the size of the danger we’re taking? What alters do we need to produce?” And from there we grew very, very sluggishly.
Astedanger: 1% of Twitter users would have been a couple million people?
Lucas: Yeah. If you slfinisherk about any other features that are starting on platcreates enjoy this on any donaten day, they’re usupartner at that scale — 5%, 1%, 0.5%.
Astedanger: So why the decision to begin that petite?
Lucas: The level of uncertainty was equitable very high. If you’re going to start a novel video joiner, you can begin at 1%. There’s very little danger there. But if you’re talking about a novel concept that people have never seen before on the internet — we spent a lot of time trying to comprehfinish the best way to even elucidate what it was. Literpartner, what are the right words to put on the screen so that somebody reads this and comprehfinishs what we’re doing?
Astedanger: How huge was your team at that point?
Lucas: Under ten.
Astedanger: I’m asking about what your feedback loops were enjoy during production at this stage. What metrics were you seeing at? What else were you paying attention to? How standardly were you making tfrails?
Lucas: We had multiple sources of feedback. There was the usage data, the notices, and the ratings themselves. We also did qualitative research — we watched people use the product, and they’d alert us what they thought.
Keith: Sometime timely in the pilot, we produced a group of users that we could engage with on a standard basis to get feedback — equitable daily observations or comments on novel features we were contemplating starting.
Emily: And I slfinisherk there were a lot of profits to begining at that scale. I uncomfervent, I don’t understand what the comparison is — I’ve never started a product at Twitter at 1% of the user base — but I slfinisherk it helpd that very safe feedback loop. Our team was reading not every notice, but every tweet about notices, and a excellent chunk of the notices themselves. We repartner, repartner knovel what was in that database. And we could point to authentic examples when we thought someslfinisherg was a danger. Or when a danger we were worried about turned out to not be a huge deal, we could destructure it.
The last source of feedback that I would refer would be the academic toil that we were doing. One of you three can probably speak better to the impact on the product depict, but there was a lot of toil put into making certain every decision was made with intention. My team at UChicago and I were helping to ease the advisory board of contrastent academics who toiled on deceiveation, toiled on online communities, and had all this expertise to convey to tolerate. They could say, for example, if what you’re interested in is produceing a community enjoy Wikipedia, then what you need to do is begin petite and produce up norms and slfinishergs enjoy that, based on this whole body of research in Human Computer Interaction. So we had feedback from the users, we had feedback from people who study these slfinishergs, and then we had feedback from doing tons and tons of research. I slfinisherk the iteration on that side was repartner speedy because of that.
Jay: The iteration speed of our feedback loop was way speedyer than it was for other teams at the company that had to serve every user. Keith had our team set up as what we called a “thermal project.” It was this exceptional mode where we could do crazy slfinishergs and produce hacky prototypes and ship speedyly. I uncomfervent, we had a lot of flexibility to ship uncultured stuff and iterate speedy because we had such a petite set of users who had chooseed in to being part of a pilot. That speed upd us a ton.
Astedanger: What were the hugegest ways the product and your slfinisherking about it alterd while the pilot was running?
Jay: One key slfinisherg was the algorithm enhugement. We didn’t have any data at the begin, so we didn’t understand what type of algorithms might toil. We accumulateed data from the users in the pilot phase and then used that to iterate on algorithms — we could simutardy some data, enjoy adversarial attacks, but mostly we were equitable using the authentic data from contributors. By the time we actupartner started to 100%, we had already gotten a lengthy period of data, and we’d set up a excellent bridging algorithm that toiled. There was also the rating create. I understand Emily and Lucas and I iterated a lot on the rating create.
Lucas: The chooseions that people can pick when rating a notice was someslfinisherg we spent a lot of time on, both to try to figure out what data we needed to apprehfinish to produce the algorithm toil, but also what chooseions we could current on a screen to have people slfinisherk criticpartner about the notice that they’re rating, and to help direct them to the ultimate goal of the of the product, which is to discover an exact encouraging notice. Emily helped a lot with that part.
But there were some very drastic alters. For example, we begined with Community Notes being non-anonymous, so people’s names were speedyened to their notices. This was the first depict, and it was based on the intuition that in order to produce suppose, you have to see who was behind a notice, or that perhaps we could produce upon someone’s credentials as an expert in some area. But very timely on in this prototyping phase, we lgeted from our contributors that they were not consoleable with the chance of having their name speedyened to a tweet from the Pdwellnt, for example, or someone who has a huge adhereing, and that they would rather do this toil anonymously. That was a very strong signal.
There was also a signal from academic research that, in anonymous systems, people may be more probable to split opinions without the prescertain from their peers. Doing that switch from a non-anonymous to a filledy anonymous product was a very huge project, a very huge spendment, but we got enough signal in the timely phases that we had to do it.
Keith: The other slfinisherg that eunited was that it became evident that notices shouldn’t repartner stand on the author’s reputation. The notices should stand on their own. You should be able to read the notice, and it should donate you the proposeation and cite the sources needed for you to get what you want from it. It was much more strong to do that than to try to rest it on one individual’s identity. It was a surpascfinish to us. In hindsight, it seems benevolent of evident that it’s better, but it was not our initial instinct.
Astedanger: And that is what descfinishs out of those tardyr studies that contrast Community Notes to expert fact checks as well — the suppose is higher.
Lucas: Yes. But one slfinisherg to notice about that outcome is that we had to put a lot of toil into overcoming people’s priors. If you go back to 2021, and someone sees a tweet with a box on it, they instantly slfinisherk, “Oh, this is a fact check.” They would suppose that Twitter wrote it, or that the Twitter CEO choosed that it should be there. What we’re taking one hour to alert you here is someslfinisherg we had to elucidate to them in a split second with equitable one line of duplicate. Arriving at that depict and what those words are — I don’t slfinisherk anyone here has ever done so many iterations on one rectangle. Things enjoy, what’s the shade of blue that will produce people tranquiler when they see this? The exceptional depict that Keith made was an orange box with “This is misdirecting proposeation” at the top. Coming from that depict to what we have now was a lgeting process.
Keith: That line — “Readers inserted context they thought people might want to understand” — we iterated on that line so many times to discover someslfinisherg that could succinctly depict what had happened here, how this came to be, that this was by the people, not by the company, and that it was there for your proposeation, not to alert you what to slfinisherk.
Emily: I don’t slfinisherk you will ever hear any of us — anybody who toiled on this project — ever say the word “fact check.” There’s a nurture to shun using that phrasing in any of the slfinishergs we say about the product, any of the language about it, anyslfinisherg on the product surface, because it’s entidepend about providing context and proposeation and then letting you produce your own decision about how to suppose it. That’s what directs to that higher suppose. But, as Lucas shelp, we’re toiling thcdisesteemful a lot of people’s priors on what the box on a tweet uncomfervents. And everybody else still calls it a “fact check.”
Astedanger: This might be a organic segue into talking about the expansiveer rollout — this is, I slfinisherk, October 2022 for America and then December 2022 globpartner. What alterd when your user base enhugeed so theatricalpartner?
Keith: We had tested the heck out of the product before that. One of the slfinishergs we haven’t talked about, but that we watchd in the pilot, was that contribution quality was very mixed. We had enhugeed this system thcdisesteemful which people get the ability to author and also can neglect the ability to author if they author junk that other people don’t discover encouraging. We had built the bridging algorithm, and the bridging algorithm had been inhabit in production, with about 20% of the US population as watchers for a number of months. And we had run a startant number of tests on notice quality. We were evaluating whether notices were set up encouraging atraverse the political spectrum in survey experiments and other tests. We were evaluating notice accuracy. We were evaluating to what degree notices impact the sharing of posts. So the system had been tested at quite a startant scale already, and we felt pretty self-guaranteed that it was going to roll out and notice quality would be reasonable. And also, if for some reason there was a problem, we could always turn it off or dial it back.
Broadly, when we started, it toiled. The notice quality was pretty high. The geted capability system, the reputation system, and the bridging algorithm led to the notices that were encouraging — repartner being set up encouraging — atraverse the political spectrum. And I slfinisherk you could see that in the dialogue after the start. I reaccumulate very timely after start there was a notice on a White House tweet, and they retracted the tweet and modernized the statement. What an incredible power to have put into the people’s hands — that standard people on the internet can call someslfinisherg out, and it can alter the way an startant topic is talked. It was pretty noticeworthy.
Astedanger: That’s one reason I repartner wanted to do this interwatch — people seem constantly very astonished by the quality of Community Notes, and I wanted to understand what went into making that happen. But I also want to talk about some of the contests of scale. This is sort of conceptupartner complicated, and I’m interested in how you slfinisherk about this, but — how huge is the product now, and how huge would you enjoy it to be? What percentage of tweets get noticed? In an perfect world, how many do you slfinisherk should be getting noticed? What’s the gap?
Keith: We sometimes phrase this as: “What’s the total insertressable taget of notable tweets?” It’s repartner difficult to understand, and if we knovel, it would be repartner encouraging. The way we would want to depict it is: How many tweets or posts are there where there exists a notice that people who disconcur would discover encouraging? But then there’s also the ask of visibility — it’s much more impactful to have notices on higher visibility satisfyed than on satisfyed that’s not seen by anyone. But it’s difficult to understand if there exists a notice that would be encouraging to people who disconcur. Our assumption is that the answer is there are more tweets enjoy this than we have notices on today, but we don’t understand what the restrict is. And so, generpartner, we equitable try to enhuge the program to cover more satisfyed. But we’re constantly measuring whether we’re still uphelderlying the high quality bar that these notices are indeed encouraging.
Astedanger: Can you talk about some publishs you’ve faced as you try to enhuge?
Jay: It definitely seems enjoy contrastent people have contrastent pickences on how many notices they want to see. Some people want notices on every one tweet, even if they’re exact, because it’s equitable celderly to read more context. And some people slfinisherk that even notices on misdirecting stuff shouldn’t be needed because people should equitable understand.
Keith: Particularly with satire or jokes — that can be an area where people disconcur. Is it evidently comical? Does it need a notice or not? That’s why we enjoy the approach we apshow, because it exits it up to users. And we’ll do what seems enjoy humanity’s pickence instead of us making a decision about that.
Astedanger: Another slfinisherg I wanted to talk about is speed. I was reading a preprint by Yuwei Chuai’s group at the University of Luxembourg.
The paper is about the overall impact of Community Notes on deceiveation — straightforwardpartner, they set up that when a tweet is noticed, this does shrink engagement, but this still has a pretty minimal effect on the overall spread of misdirecting tweets because a notice needs to eunite repartner, repartner speedy to have an impact. The statistic that made my eyes pop was that the half-life of a tweet is someslfinisherg enjoy 79 minutes. Half the astonishions it’s ever going to have, it has in the first 79 minutes. Now, I understand you’ve done a lot of toil on increasing the speed at which notices eunite, from around five days, or someslfinisherg, timely in the pilot to wislfinisher a day or so now. What are the contests in making notices happen speedyer?
Jay: Great ask. First off, I equitable want to talk about the half-life of a tweet. I slfinisherk in this paper they seeed at the firehose of all tweets and then took the half-life from that. But, you understand, the median tweet doesn’t get a lot of engagement. If you’re talking about the median tweet that goes viral above some certain threshelderly, then the half-life is many times lengthyer.
Astedanger: And I apshow that viral tweets are more probable to get notices?
Jay: By far. Because in order to have enough people see a tweet so that a notice gets written, and then for that notice to get enough ratings to show it, they’re —
Keith: Typicpartner being seen by a lot of people for up to 24 hours. Not 79 minutes. It’s a much lengthyer time thrivedow.
Jay: And then, even if you did see a post before it got noticed, if you engaged with the post, we’ll sfinish you a notification afterwards with the notice, once the notice’s been rated encouraging. As far as speed, we’re doing a lot. I slfinisherk that the speed has been improving pretty rapidly. We’ve done a lot to enhance the data pipelines behind the scenes, but also slfinishergs equitable get speedyer as we get more contributors.
Keith: When we first begined, going back to the pilot phase, the cgo in was entidepend quality. The slfinisherking was, we’ll deal with speed and scale as we grow. As you refered, in the pilot, it would apshow multiple days for a tweet to get a notice. But no one was seeing these slfinishergs back then. There are a couple places that insert time to the process. One is the organic time for someone to choose a tweet or post might profit from a notice and then for people to rate it. And then there’s the time to actupartner score the notice. We’re toiling on speeding up both of those. The scoring — that is, the frequency at which we can score — used to be three to five hours, and it’s soon going to be in the minutes.
This uncomfervents notices can now go inhabit wislfinisher minutes of being written and rated. And then, you have to contrast that to the alternatives. It’s inanxiously normal to see professional fact checks apshow multiple days. We see this all the time. In the Israel-Hamas dispute, in those first scant days of the dispute, there was so much deceiveation. There were people posting video game footage, claiming it was happening in Israel. There were pictures from other countries from prior disputes, saying this was happening in Gaza, and notices were euniteing in petite numbers of hours. I slfinisherk the median time was about five hours. That was before all these speedups we’ve done. And then, some of those same rightions were only published as fact checks two to four days tardyr. And so, already at that point, notices were immensely outcarry outing the status quo.
Jay: On top of that, we also do media suiting. That is, we donate our top authorrs the ability to author a notice that’s actupartner about the media on a post instead of the post itself. And then, when a notice enjoy that is rated encouraging, it will show on all the suited copies of that media atraverse the platcreate, which can also happen wislfinisher minutes of those posts getting produced. Statistics that are based on the accessible dataset on a per-notice basis are standardly not counting media suites, which repartner, repartner speeds up the median time to notice.
Keith: It’s difficult to understand because it’s difficult to run the test in the authentic world, but I mistrust that there are huge numbers of misdirecting memes or ideas that would have gone viral but haven’t because of notices and media suiting in particular. It’s normal for us to see these cases where a post goes inhabit with an outdated or inalter video. It’s got a notice wislfinisher a petite number of hours. It’s a media notice. It’s instantly suiting on every other post that uses that media. I would guess that prior to the existence of Community Notes, there would have been a lot of copies of that image being splitd around.
Astedanger: Obviously, satisfyed moderation at scale remains a huge problem atraverse social media. What are the hugegest lessons that you’ve lgeted about enhugeing novel satisfyed moderation methods? What would you most enjoy to say to teams at other companies, or people toiling on this problem expansively?
Keith: One of the hugegest contests in the moderation space is deinhabitring outcomes that people sense are unprejudiced. One slfinisherg I slfinisherk Community Notes does well is that it tfinishs to deinhabitr notices that people discover to be unprejudiced and encouraging. And I slfinisherk they discover the process to be relatively unprejudiced, too, because everyone has a voice. It’s based on uncover source data and code that’s in the accessible, so people can audit it and critique it. Other areas of moderation face this same unprejudicedness contest. I would cherish to see novel approaches that try to produce those decisions in uncover, unprejudiced ways that people suppose. I mistrust they would be repartner prosperous and that it could ultimately direct to outcomes on these repartner contentious decisions that people can get behind — even if they disconcur with them.
Jay: I slfinisherk there’s a lot of little micro depict decisions that we made alengthy the way that are pretty encouraging for many types of moderation systems — enjoy anonymity, actupartner using the crowd, getting moderation input from users rather than equitable mods, and having an algorithm discover points of concurment — and then the depict choices enjoy inserting friction. Much of the time people are not super diligent if they equitable have to click one button angrily. But if people are going to put in a lot of effort to author someslfinisherg, they’re probably going to be more pimpolitent. Maybe Lucas and Emily could talk more about that.
Lucas: I slfinisherk there were a lot of depict contests that we had to conquer because we stuck to some principles that we set out at the very beginning. There’s the fact that the data is uncover source, that the code is uncover source, and that we still don’t have any buttons on the X side to back or demote any notice. We’ve never alterd the status of an individual notice. We either apshow the entire system down, or it’s running. Those three non-negotiables produced a lot of toil for us. The anonymity part, the reputation part, all of the petite details on the screen — each of these produces it so that people can, at the finish of the day, see a notice and understand, “I can suppose this.” But that filled circle was years and years of toil.
Emily: As somebody who does not toil on social media satisfyed moderation, I slfinisherk there’s actupartner a lot you can lget by seeing at Community Notes for depict choices, principles and appreciates, and decisions you have to produce when you are in a project of truth-seeking — no matter what that is, right? I toil at a charity evaluator, and you would slfinisherk that has noslfinisherg to do with social media. But it proposes a lot of the way that I slfinisherk about truth-seeking in vague.
Astedanger: Did you ever get any pushback to the idea that you can’t alter the status of a definite notice?
Keith: We’ve always had help for the approach that whether a notice shows is up to the people, not a company. And that the process is auditable and verifiable — you can download the code and the data and reproduce the same results you see on X. Our principle is that we are produceing a system to produce notices that are encouraging and proposeative, thcdisesteemful a clear, uncover, accessible process. And if there’s a problem, it’s not a problem with a notice, it’s a problem with the system. So we’d rather apshow down the whole system — and enhance it — than apshow down a notice.
The only case where the company would apshow action on a definite notice would be if it were to viotardy platcreate rules, but the bridging algorithm and the process thcdisesteemful which people have to get the ability to author notices suppress this in the first place. It might seem unforeseeed that a company is OK not having an override button, and perhaps it could sense unconsoleable to not have one, but I slfinisherk it aligns with a noble sense of how people want the world to toil. It equitable senses unprejudiced and spotless and principled.
Astedanger: You also refered that one of your principles was uncover sourcing code and data. What impact did that have?
Jay: This is a huge — but priceless — constraint we put on ourselves. When people slfinisherk about the evident ways one might try to acunderstandledge what’s encouraging to people who normpartner disconcur, they standardly slfinisherk about using tweets or enjoys or engagements — or someslfinisherg enjoy that. But because we wanted the algorithm to run entidepend on accessible data, we ruled out using any of that. We wanted the algorithm to run entidepend on the contribution data from Community Notes itself, which was accessible. This led to a quite novel and sturdy bridging-based matrix factorization algorithm, which is naturpartner manipulation resistant — even when uncover-sourced. Rather than seeing at tweets or engagements or slfinishergs enjoy that, it sees at how standardly people concur in their Community Notes ratings.
A key profit of that approach is that people have skin in the game when they produce those ratings, as those ratings actupartner lift notices. So there’s a fundamental incentive to rate in a way that’s aligned with your actual point of watch. Additionpartner, the uncover source data has shown encouraging. For example, in insertition to increasing transparency and suppose, it has helpd self-reliant, outer research on Community Notes, enjoy two recent studies that showed notices shrink resharing of posts by 50-61%,
and incrrelieve deletion of posts by about 80%.
And a study in the Journal of the American Medical Association set up notices they appraiseed to be highly exact.
It’s even apshowed us to adchoose code alters from the accessible. The code that scores notices actupartner engages code written by people outside of the company and surrfinisherted in GitHub. We’d cherish, ultimately, for the algorithm itself to be written by the people — equitable enjoy the notices themselves.
