Speaker 1: Pushkin, I'm may have Higgins, and this is Solvable Interviews
Speaker 1: with the world's most innovative thinkers who are working to
Speaker 1: solve the world's biggest problems. In this episode, Anne Applebaum
Speaker 1: is in conversation with researcher and data analyst Renee Deresta
Speaker 1: about her solvable, which is the growing spread of dangerous
Speaker 1: misinformation online, especially on social media. For the most solvable,
Speaker 1: I think we need increasing awareness, increasing cooperations, helping algorithms
Speaker 1: make better decisions, recognizing that recommendation engines are not functioning
Speaker 1: as they should, and that we should be taking tangible
Speaker 1: steps to think about ways in which algorithm curation serves
Speaker 1: information to people. In late twenty sixteen, Oxford Dictionaries selected
Speaker 1: post truth as their word of the year, defining it
Speaker 1: as relating to or denoting circumstances in which objective facts
Speaker 1: are less influential in shaping public opinion than appeals to
Speaker 1: emotion and personal belief. It's like, I want to believe
Speaker 1: that nachos are the ideal balanced nutritional snack that appeals
Speaker 1: to my emotional and personal belief system, because God, I
Speaker 1: love nattos. So I'll go and I'll find some vague
Speaker 1: chitchat online that tells me, you know, something melted cheese
Speaker 1: is totally full of calcium that is good for your bones,
Speaker 1: and it's important for you, as an immigrant to the US,
Speaker 1: to assimilate by eating their national dish of natos. So
Speaker 1: I'll convince myself of that, and I'll maybe even eat
Speaker 1: myself into a delicious early grave. The age we live in,
Speaker 1: the digital age, affects every narrative we see and absorb,
Speaker 1: and that can be news based, or cultural or artistic.
Speaker 1: We have always had an instinct to find information that
Speaker 1: sinks with our perspective, and now a host of new
Speaker 1: platforms are only too happy to oblige that part of us.
Speaker 1: Pew reports that an analysis of almost four hundred million
Speaker 1: Facebook users interactions with over nine hundred news outlets found
Speaker 1: that people tend to seek information that aligns with their views.
Speaker 1: That makes many of us vulnerable to accepting and acting
Speaker 1: on misinformation. Social media firms are under pressure to halt
Speaker 1: the spread of fake contents on their platforms, and we
Speaker 1: know that the problem has both human and technical side,
Speaker 1: and so too does any potential solution. Reneed Arresta is
Speaker 1: the director of research at New Knowledge and a Mozilla
Speaker 1: Fellow in Media Misinformation and Trust. She investigates the spread
Speaker 1: of malign narratives across social networks and helps policymakers to
Speaker 1: understand and respond to the problem. Renee has advised Congress
Speaker 1: and the State Department, and she studies some fascinating areas
Speaker 1: of disinformation in contexts like pseudoscience, conspiracies, terrorism, and state
Speaker 1: sponsored information warfare, all that spooky stuff. I'm so glad
Speaker 1: she is scouting ahead and sending us back the best
Speaker 1: ways to deal with this. Let's take a listen and
Speaker 1: I'll speak to you after. So, Renee, you're one of
Speaker 1: the few people who identified the problem of online anti
Speaker 1: vax disinformation very early on. How did you first come
Speaker 1: into contact with the problem? How did you know it
Speaker 1: was a problem at all. I started working on a
Speaker 1: law in California called SP two seventy seven, and it
Speaker 1: was a law to eliminate vaccine opt outs. And I
Speaker 1: was a parent at a new baby, and I wanted,
Speaker 1: as a mom, just to volunteer to help get this
Speaker 1: law passed. So I am a data analyst, and I
Speaker 1: offered to do some analysis into things like the social
Speaker 1: media conversation around the law. And I was really surprised
Speaker 1: because the legislators, there are a number of legislators on
Speaker 1: both parties who were supporting the law. They were saying
Speaker 1: that their constituents were polling at around eighty five percent
Speaker 1: in favor, but the social media conversation was almost one
Speaker 1: hundred percent negative, and that was on Facebook and Twitter.
Speaker 1: So I started working with another data scientist named Glad
Speaker 1: Latan to look at the conversation on Twitter, to look
Speaker 1: at the different distinct groups, how they were evolving their messages,
Speaker 1: how they were connecting with other activists outside of California,
Speaker 1: how sometimes activists outside of California. It turned out we're
Speaker 1: pretending to be Californians, bunches of new accounts that had
Speaker 1: been created, and we were really looking at the idea
Speaker 1: of what had no name then but kind of came
Speaker 1: to be called manufactured consensus, the idea that the conversation
Speaker 1: online was really being driven by a relatively small number
Speaker 1: of people who were using things like tools to be
Speaker 1: always on, constantly being in the hashtag Facebook groups and
Speaker 1: ads to amplify their message, and then the way that
Speaker 1: the algorithm was amplifying the message. In addition to that,
Speaker 1: so ways in which I, as a parent who had
Speaker 1: just gotten involved in the conversation, had just demonstrated an
Speaker 1: interest in vaccine policy, was all of a sudden getting
Speaker 1: pushed tons of anti vaccine content on Facebook. It was
Speaker 1: recommending groups to me, it was recommending pages to me.
Speaker 1: And the realization that what was really not a very
Speaker 1: large number of people was actually having an extreme disproportionate
Speaker 1: amount of a share of voice in the conversation, and
Speaker 1: did you have to create tools in order to begin
Speaker 1: identifying who the people were who was being pushed. They
Speaker 1: were actually not very quiet about that. There was a
Speaker 1: page called tweet for Vaccine Freedom, and it was actually
Speaker 1: you know, when out of state activists were asking how
Speaker 1: can we help because the entire anti vaccine movement across
Speaker 1: the entire United States decided to fight this battle. They
Speaker 1: would say like, oh, you should just create an account
Speaker 1: and say or from California. So it was actually really transparent.
Speaker 1: It wasn't that hard to figure out that there were
Speaker 1: people pretending to be from California. There were also Twitter
Speaker 1: accounts that all of a sudden had a vested interest
Speaker 1: in California politics. But if you read their past material,
Speaker 1: which again is also public it was really right out
Speaker 1: there that that's not where they were actually from. Kind
Speaker 1: of a very interesting because it was extremely small, local
Speaker 1: and niche we you know, we thought in California. But
Speaker 1: as the law began to get more press coverage and
Speaker 1: things or would actually be like comments section battles, you know,
Speaker 1: the same kinds of stuff that we saw later with
Speaker 1: you know, entities that go and are like almost incentivized
Speaker 1: to leave comments on news articles to shape a perception
Speaker 1: about the topic. And actually we on the provac side thought,
Speaker 1: oh boy, I guess we're gonna you know, we need
Speaker 1: to do this too. Are we really engaging in Okay?
Speaker 1: They commented over here, so you know, we have to
Speaker 1: go comment over here. They have bots that are on
Speaker 1: twenty four to seven? Do we need bots that are
Speaker 1: on twenty four seven? Just became this this interesting firsthand
Speaker 1: experience of what it was going to be like to
Speaker 1: try to run any kind of influence or policy campaign
Speaker 1: in the future. I found it really troubling, especially when
Speaker 1: the algorithms just began recommending anti vaccine content to me constantly.
Speaker 1: And how did the Facebook and Twitter and other algorithms work.
Speaker 1: Were they affected by this campaign? The where the search
Speaker 1: engines affected by it. I don't think the search engines
Speaker 1: as much because it was you know, the Google is
Speaker 1: a little bit more sophisticated about this stuff than the
Speaker 1: social platforms. Social platforms the number one signal that they're
Speaker 1: using as popularity, and so you either if you have
Speaker 1: real popularity or if you can feign popularity. The number
Speaker 1: of likes and engagements and comments and things is what decides,
Speaker 1: you know, whether this is how Facebook was deciding what
Speaker 1: gets pushed into your feed. Instagram is like that too.
Speaker 1: Google has a framework now it has a proper name.
Speaker 1: It's called Your Money or Your Life, and it says
Speaker 1: that on topics related to health issues and financial issues
Speaker 1: they have to have a higher standard of care to
Speaker 1: make sure that it isn't just what's popular that's rising
Speaker 1: to the top. But even with that policy, one of
Speaker 1: the things that we consistently see is anti vaccine activists
Speaker 1: producing content at a higher rate and also candidly more
Speaker 1: engaging content, you know, a much more emotionally resonant versus
Speaker 1: more authoritative medical quote unquote establishment doctors, the CDC, the
Speaker 1: National Instituites for Health, their contents not as emotionally resonant.
Speaker 1: It doesn't get as much engagement, and so the search
Speaker 1: engines and the algorithm aren't amplifying the more factual, reality
Speaker 1: based content, and instead what we're getting is this conspiratorial stuff.
Speaker 1: Walk me through what it means to be emotionally resonant online.
Speaker 1: Is this something that's being done deliberately to the people
Speaker 1: who are creating it understand that that's what it is?
Speaker 1: Or is it that the human brain is just tuned
Speaker 1: to conspiracies and prefers them. Some of it is platform culturism,
Speaker 1: of it is the way that the algorithm understands engagement.
Speaker 1: So there's the human element which gets kind of the
Speaker 1: initial signal shows that there's a lot of people who
Speaker 1: are watching this, and the algorithm recognizes that a lot
Speaker 1: of people are watching it and then begins the amplification process.
Speaker 1: But the first step is actually the content, of course,
Speaker 1: and in that particular area, it's usually a first person,
Speaker 1: you know, looking directly at a camera, speaking about a
Speaker 1: personal experience they've had, recounting a narrative or an interesting story.
Speaker 1: So a lot of times with the anti vaccine movement,
Speaker 1: that's a person claiming that their child has autism and
Speaker 1: telling a story, you know, usually very sad story about
Speaker 1: their child's health, and so it is engaging. It is
Speaker 1: much more resonant versus seeing kind of infomercial about how
Speaker 1: vaccines don't cause autism because thousands and thousands and thousands
Speaker 1: of studies have said that they do not. I know
Speaker 1: that you were part of the Senate commission that looked
Speaker 1: through material that we knew that Facebook handed over to
Speaker 1: Congress which was originally created by the IRA, the Russian
Speaker 1: Internet Agency, in order to influence the US elections. When
Speaker 1: you looked over that material, did it seem to use
Speaker 1: those same tactics? Can you see a relationship between the
Speaker 1: way the Russian influence campaign worked and the anti vax campaigns?
Speaker 1: The Russian content was distinct, and that this was a
Speaker 1: foreign intelligent service of a foreign entity that was trying
Speaker 1: to pretend to be American. So it was far more
Speaker 1: duplicitous than anything that we've seen related to domestic activists
Speaker 1: pushing for a cause. Really, but what was happening there
Speaker 1: was again they were taking these extremely big topics things
Speaker 1: like who is America for? What does it mean to
Speaker 1: be an American? How do we feel about immigration? How
Speaker 1: do we feel about gay rights? How do we feel
Speaker 1: about police brutality? They were creating these pages, and each
Speaker 1: page was designed for a very particular type of person,
Speaker 1: So they were really creating these tribes, again relying on
Speaker 1: the sort of first person experience, first person concerns and fears,
Speaker 1: and putting out content that was again very much focused
Speaker 1: on achieving an emotional response. So for the black community,
Speaker 1: the content took the form of constant references to police
Speaker 1: violence mixed in with narratives of pride, and so it
Speaker 1: was really very much designed to evoke cultural pride and
Speaker 1: then also a sense of deep harm. And on the
Speaker 1: right leaning pages, it was really concerned about what America
Speaker 1: is and who it's for, and so a lot of
Speaker 1: photos of things like homeless veterans. This is a very
Speaker 1: real problem that we have in this country, and they
Speaker 1: were using the images of homeless veterans to say, why
Speaker 1: are we allowing in all of these outsiders when we
Speaker 1: can't take care of our own. This is how propaganda
Speaker 1: is most effective. It's when it has some degree of
Speaker 1: truth to it, and it spins it just enough that
Speaker 1: it doesn't necessarily trigger the part of the brain that says, hey,
Speaker 1: this is false. Instead it the person relies on the
Speaker 1: emotional reaction to it, and that's how they begin to
Speaker 1: develop a sustained relationship with the page and sustained engagement
Speaker 1: with that type of content. You know, as I'm listening
Speaker 1: to you, I'm wondering whether different kinds of propagandists they
Speaker 1: understand now that they need to tailor messages to particular audiences.
Speaker 1: Is it the case that some of the solutions to
Speaker 1: this they're also going to involve thinking differently about different
Speaker 1: audiences or offering different kinds of counter messaging or counter
Speaker 1: strategies to different audiences. Yeah. Absolutely, And this is something
Speaker 1: that you know. A third area I worked in was
Speaker 1: countering violent extremism. Briefly was Isis. The idea that we
Speaker 1: would kick Isis off the platforms was sort of a
Speaker 1: stretch at the time. There were a lot of people
Speaker 1: who were very concerned at the idea that we would
Speaker 1: delete terrorists accounts, and so a lot of the focus
Speaker 1: instead was on counter messaging. How do we reach these
Speaker 1: audiences that are receptive to ISIS propaganda and present counter
Speaker 1: narratives to them. Who is the authentic voice for the
Speaker 1: counter narrative. It's definitely not the United States State department,
Speaker 1: So who is it and what are the ways in
Speaker 1: which we can come together to think about ways to
Speaker 1: counter message to try to present people with it an alternate,
Speaker 1: also emotionally resonant narrative instead of just saying it's a
Speaker 1: bad idea to be a terrorist because you're going to
Speaker 1: go to jail or you're going to die. A lot
Speaker 1: of the tribal deep affinity ties is what is my
Speaker 1: place in society? This is something that comes up with
Speaker 1: conspiracy theorists also, they're looking for answers, they're looking for
Speaker 1: an explanation. What you get hooked into oftentimes is what
Speaker 1: is most visible to you, what's most prevalent in your
Speaker 1: space at that moment. Now that we're spending so much
Speaker 1: more of our time online, things like ad targeting and
Speaker 1: participation in Facebook groups where you're kind of declaring a
Speaker 1: particular alignment mean that bad actors who want to target
Speaker 1: you with certain types of propaganda can find you very easily.
Speaker 1: And can we reapply some of that thinking back, for example,
Speaker 1: to the anti vax problem. Can we think about counter
Speaker 1: messaging there? Can we think about how to reach people
Speaker 1: using counter emotional stories? Yes, absolutely, that is That's something
Speaker 1: that our groups like Voices for Vaccines are trying to
Speaker 1: work on that. The group that was formerly called every
Speaker 1: child by two, it now goes by vaccinate your family
Speaker 1: is trying to do that. We have to get out
Speaker 1: of statistics and get into storytelling. That's the one of
Speaker 1: the key takeaways of how the information ecosystem has evolved.
Speaker 1: If you look at even just from a design perspective,
Speaker 1: one of the things I always get at is the
Speaker 1: the subject of the narrative is interesting when you're thinking
Speaker 1: about how to counter message to a particular group of people.
Speaker 1: But when you think about this as a problem written large,
Speaker 1: a lot of it comes down to the algorithms and
Speaker 1: the design. And so memes in particular are getting more
Speaker 1: and more important in our lives. And that's because the
Speaker 1: design of the platform itself is privileging this large you know,
Speaker 1: this large square image or this piece of video, this
Speaker 1: short video clip. So what can you convey in the
Speaker 1: construct of that design. As people are scrolling by, they
Speaker 1: see your message in it immediately sticks. The fact that
Speaker 1: the algorithm will continue to serve up types of content
Speaker 1: that you've engaged within the past means that if you
Speaker 1: do engage with anti vaccine content, you're likely to see
Speaker 1: more of it. The challenge of algorithms that don't know
Speaker 1: what they're pushing because they have no actual awareness of
Speaker 1: what the underlying content is. So they treat something that's
Speaker 1: potentially radical, they treat something that's potentially blatantly false the
Speaker 1: exact same way that they would treat something that's accurate
Speaker 1: or uplifting. They don't actually know. They just know that
Speaker 1: this content drives engagement, and so they continue to show
Speaker 1: it to people. We see all this disinformation online. We
Speaker 1: you know, we hear about it. You know, we can
Speaker 1: sometimes see it in our Google searches. But doesn't really matter.
Speaker 1: I mean, for example, in the anti vax campaign, has
Speaker 1: this really affected anything, Does it make any difference? Or
Speaker 1: is this just stuff that exists somewhere in the ether
Speaker 1: and if we ignore it will go away. Let me
Speaker 1: give you two quick examples on that. First of all,
Speaker 1: with the anti vaccine movement, Yes, it absolutely has an impact.
Speaker 1: It really creates a lot of fear and hesitancy, and
Speaker 1: that translates very directly into vaccination rates declining in the
Speaker 1: communities that are that are seeing it. And so this
Speaker 1: is something that in California. The reason I started looking
Speaker 1: at it was because immunization rates in California communities had declined,
Speaker 1: and when I was trying to find a preschool for
Speaker 1: my son, I was actually looking at these rates, and
Speaker 1: there are certain schools in California with thirty percent immunization rates,
Speaker 1: which is terrifying. That's like South Sudan. The reason that
Speaker 1: we passed the lawn California was because we wound up
Speaker 1: with the Disneyland measles outbreak, where two hundred and something
Speaker 1: people got sick and I believe a quarter had to
Speaker 1: be hospitalized. So this was a very real outcome of
Speaker 1: that kind of misinformation becoming so pervasive to people, creating
Speaker 1: that very real fear and then leading to an outbreak
Speaker 1: in the case of Russia. Just because a lot of
Speaker 1: people think about this is just related to the election. No,
Speaker 1: what they were doing was they were also creating real
Speaker 1: world events. So they were sponsoring protests, and one of
Speaker 1: the things that they sponsored was an incident in Texas
Speaker 1: where they had two competing protests on the same day
Speaker 1: at the same time. So from Saint Petersburg, Troll created
Speaker 1: a Facebook event saying that people with Texas Pride had
Speaker 1: to come and protest outside of an Islamic center to
Speaker 1: defend their way of life. They also posted an event
Speaker 1: calling on members of the Islamic Center to come out
Speaker 1: and defend the Islamic faith. So they sponsored two protests
Speaker 1: on the same day at the same time, across the
Speaker 1: street from each other. And you can go on YouTube
Speaker 1: and you can see the video footage from that day
Speaker 1: of people showing up with kind of anti Islamic material
Speaker 1: on one side of the street and then people on
Speaker 1: the other side of the streets screaming back at them,
Speaker 1: and police getting involved in breaking up altercations. So this
Speaker 1: is an example of very real world tension erupting as
Speaker 1: a result of online disinformation. When you first started looking
Speaker 1: at this problem, did people believe it was a problem.
Speaker 1: Opinion polls all showed people were in favor of vaccinations.
Speaker 1: You saw something quite different online. How did you convince
Speaker 1: people that this was something they need to take seriously.
Speaker 1: In the California case in particular, I sent what I
Speaker 1: was seeing, you know, kind of quantifiable evidence to the
Speaker 1: legislators and said, I don't think that people are screaming
Speaker 1: at you online, they're threatening you online, You're seeing all
Speaker 1: of this anger and rage in the hashtags, I don't
Speaker 1: think that these are not your constituents, where it's pretty
Speaker 1: pretty clear that these are not all even Californians. So
Speaker 1: when you make your decision, I would lean into the
Speaker 1: polling numbers and the communications with your actual constituents. I
Speaker 1: don't think that we can treat the online conversation as
Speaker 1: representative of the reality of the population of California. So
Speaker 1: in that particular case, it was just really kind of
Speaker 1: appealing directly to the legislators with the evidence the challenges
Speaker 1: it really does bump up against things like freedom of
Speaker 1: expression right. So you have a right to have an
Speaker 1: anti vaccine opinion. Of course you have a right to
Speaker 1: put the content online. The challenge was at the time,
Speaker 1: the recommendation engine, the trending algorithm, the ways in which
Speaker 1: Twitter and Facebook were amplifying information was very different, far
Speaker 1: more primitive then than it is even now two and
Speaker 1: a half years later. After those of us who work
Speaker 1: on this challenge have kind of been constantly beating the
Speaker 1: drum with example after example of example of how this
Speaker 1: is manifesting in the real world. How do we preserve
Speaker 1: freedom of expression while at the same time recognizing that
Speaker 1: the platform is pushing this point of view at people
Speaker 1: people aren't even know me. In particular, I'm not going
Speaker 1: out there typing in anti vaccine search terms. The recommendation
Speaker 1: engine is just pushing it to me because it's seeing
Speaker 1: that I've expressed an interest in vaccines in general. As
Speaker 1: part of working on this law, I suppose there's also
Speaker 1: a question of Okay, you have a right to write something,
Speaker 1: but then do you have a right to artificially amplify
Speaker 1: it using bots and search engine optimization? So everyone has
Speaker 1: the right to freedom of expression online. The secondary piece
Speaker 1: of that, though, is do you have a right to
Speaker 1: free reach your right to algorithmic amplification. Nobody has that right.
Speaker 1: That is not part of the First Amendment, That is
Speaker 1: not part of our cultural experience of what it means
Speaker 1: to have a right to express, or you have never
Speaker 1: had the right to free mass dissemination as well. That's
Speaker 1: the piece where as people begin to talk about how
Speaker 1: the platform should think about these things. One of the
Speaker 1: ways that we can continue to maximize freedom of expression
Speaker 1: is to allow people to speak, but also for the
Speaker 1: algorithm to perhaps not begin to take that kind of
Speaker 1: sensationalist content and proactively broadcast it out to massive quantities
Speaker 1: of people because it checks the boxes of being sensational
Speaker 1: and emotional. And do you think that it's going to
Speaker 1: be enough to discuss this with the platforms, for people
Speaker 1: like you who have you, who are respected on these issues,
Speaker 1: to talk about it with people at Facebook and Google,
Speaker 1: or is this something that we're going to need to
Speaker 1: regulate or have Congress step in on. I don't think
Speaker 1: you can have Congress regulate what algorithms amplify. I think
Speaker 1: that that would probably be a little bit too close
Speaker 1: to Congress making decisions on speech. A lot of the
Speaker 1: dissemination that come about through inauthentic amplification through things like
Speaker 1: bots and stuff, can be addressed without even knowing what
Speaker 1: the narrative is actually about. So you're not looking for
Speaker 1: content related to a particular topic. You're looking for particular
Speaker 1: dissemination patterns. So you're looking at the authenticity of the accounts.
Speaker 1: Are these real accounts where they all created yesterday? Are
Speaker 1: they bots? Are they majority automated? Are they Twitter does
Speaker 1: have now a designation of something that considers a low
Speaker 1: quality account ways in which it surfaces top tweets, as
Speaker 1: opposed to just the straight up reverse chronological order where
Speaker 1: you see every single tweet about a particular hashtag, giving
Speaker 1: the user some control. So people who do want to
Speaker 1: go see that kind of fire hose of every single
Speaker 1: tweet coming through about a topic can go and do that.
Speaker 1: But the majority of people who just want to get
Speaker 1: the kind of quick takeaways are seeing more kind of
Speaker 1: higher caliber content. And that sounds like you do believe
Speaker 1: algorithms could eventually identify quality content that they could encompass
Speaker 1: a notion of better or more comprehensive or more fact based.
Speaker 1: Remember the olden days of the Internet where you had
Speaker 1: email spam, right, we did build classifiers, We did build
Speaker 1: tools to think about how to ensure that crap wasn't
Speaker 1: flooding people's inboxes, that there wasn't this mass cognitive load
Speaker 1: every time you opened your inbox of having to sift
Speaker 1: through all of the garbage to find the communications from
Speaker 1: people that you actually wanted or or find the things
Speaker 1: that were really intended for you. We need to put
Speaker 1: some things in place here to improve the system, to
Speaker 1: improve the user experience, to improve the outcomes. There were
Speaker 1: things like recognizing that certain domains were just not reputable
Speaker 1: domains that most people wanted in their inbox, and so
Speaker 1: some of this was user filtering, you know, feedback. You
Speaker 1: remember you used to kind of mark things as spam
Speaker 1: much more regularly then. It didn't mean that there were
Speaker 1: never false positives. There are still false positives today, But
Speaker 1: it was how can we create greatest value while at
Speaker 1: the same time recognizing that there are extremely coordinated, deliberate
Speaker 1: groups of people working to manipulate and evade that detection,
Speaker 1: in the case of spam, to wind up in your
Speaker 1: inbox and in the case of social algorithmic manipulation to
Speaker 1: wind up in your feed People who are concerned about
Speaker 1: this problem, people who worry about online disinformation, people who
Speaker 1: worry they're getting bad information. Is there anything they can
Speaker 1: do about it? Is there something that ordinary people can
Speaker 1: do to fight back? Stopping this spread a lot of
Speaker 1: the time is something where individuals really have a lot
Speaker 1: of power. It's been for a long time, you know,
Speaker 1: kind of a cultural norm where if you see someone
Speaker 1: sharing something a little bit nutty to just kind of
Speaker 1: ignore it, just let it go by. I don't think
Speaker 1: that that's necessarily really helped us. I've tried lately to try,
Speaker 1: like commenting gently or sending a private message saying hey,
Speaker 1: I don't think this is necessarily the most reputable source.
Speaker 1: Maybe you know, here's a fact check on that. There's
Speaker 1: a lot of evidence that says that interventions from people,
Speaker 1: you know, even in the kind of counter radicalization space,
Speaker 1: that really engagement with friends and family and people were
Speaker 1: there's a base of trust and an assumption of goodwill.
Speaker 1: People are receptive to rethinking maybe why they chose to
Speaker 1: share something. And then when you see something that makes
Speaker 1: you feel highly emotional and you go to click the
Speaker 1: share button or the retweet button just because you know,
Speaker 1: you feel outraged and you need to tell the world,
Speaker 1: that's where I think taking the extra second to stop
Speaker 1: and do the fact check, to stop and see is
Speaker 1: this a reputable domain or a reputable account, it really
Speaker 1: makes a difference. So friends, don't let friends share disinformation,
Speaker 1: and always check whose account you're retweeting or reposting before
Speaker 1: you do it. Yeah, I mean, I've made this mistake
Speaker 1: a couple of times. I remember I once retweeted something
Speaker 1: and a friend of mine ping me and said, hey,
Speaker 1: I think you should go read the rest of that
Speaker 1: accounts tweets, And I went and looked, and I ninety
Speaker 1: nine percent sure it was a bot, and I was like, oh,
Speaker 1: I fell for it, you know so. But that's the
Speaker 1: kind of thing where it's far better to tell somebody.
Speaker 1: I mean, you can just unretweet, you just click the
Speaker 1: button again. And it's more challenging if you are a
Speaker 1: person with a very, very large following, and it usually
Speaker 1: helps to send a follow up or something and say, hey,
Speaker 1: I inadvertently spread some misinformation. It's come to my attention
Speaker 1: that this is not real, or here's the actual story.
Speaker 1: It's so wild to hear about these disinformation campaigns online
Speaker 1: right now because here in the US there have been
Speaker 1: eight hundred and eighteen measles cases reported in this year's outbreak.
Speaker 1: It's already the largest since nineteen ninety four. People are
Speaker 1: in hospital here because of misinformation, and New York is
Speaker 1: seeing the fastest spread, particularly in Orthodox Jewish communities. The
Speaker 1: thing is that in that specific case, the misinformation about
Speaker 1: vaccines was not spread online, but through physical handbooks and
Speaker 1: phone conferences. The internet amplifies what we already do, so
Speaker 1: changing algorithms and policies and our own behavior online. It's
Speaker 1: all going to take a lot of changing, and I'm
Speaker 1: really grateful to people like Renee who work towards that
Speaker 1: every day. Solvable is a collaboration between Pushkin Industries and
Speaker 1: the Rockefella Foundation, with production by Chalk and Blade. Pushkin's
Speaker 1: executive producer is Mia LaBelle. Engineering by Jason Gambrell and
Speaker 1: the fine folks at GSI Studios. Original music composed by
Speaker 1: Pascal Wise. Special thanks to Maggie Taylor, Heather Fame, Julia Barton,
Speaker 1: Carlie Migliori, Sherif Vincent, Jacob Weisberg, and Malcolm Gladwell. You
Speaker 1: can learn more about solving today's biggest problems at Rockefella
Speaker 1: Foundation dot org, slash solvable. I'm Mave Higgins. Now go
Speaker 1: solve it.