1 00:00:15,076 --> 00:00:22,676 Speaker 1: Pushkin, I'm may have Higgins, and this is Solvable Interviews 2 00:00:22,716 --> 00:00:25,796 Speaker 1: with the world's most innovative thinkers who are working to 3 00:00:25,916 --> 00:00:31,516 Speaker 1: solve the world's biggest problems. In this episode, Anne Applebaum 4 00:00:31,596 --> 00:00:35,476 Speaker 1: is in conversation with researcher and data analyst Renee Deresta 5 00:00:35,636 --> 00:00:39,756 Speaker 1: about her solvable, which is the growing spread of dangerous 6 00:00:39,836 --> 00:00:45,796 Speaker 1: misinformation online, especially on social media. For the most solvable, 7 00:00:45,836 --> 00:00:50,036 Speaker 1: I think we need increasing awareness, increasing cooperations, helping algorithms 8 00:00:50,036 --> 00:00:55,956 Speaker 1: make better decisions, recognizing that recommendation engines are not functioning 9 00:00:55,996 --> 00:00:58,076 Speaker 1: as they should, and that we should be taking tangible 10 00:00:58,116 --> 00:01:02,116 Speaker 1: steps to think about ways in which algorithm curation serves 11 00:01:02,156 --> 00:01:07,676 Speaker 1: information to people. In late twenty sixteen, Oxford Dictionaries selected 12 00:01:07,796 --> 00:01:11,396 Speaker 1: post truth as their word of the year, defining it 13 00:01:11,436 --> 00:01:15,916 Speaker 1: as relating to or denoting circumstances in which objective facts 14 00:01:16,036 --> 00:01:21,036 Speaker 1: are less influential in shaping public opinion than appeals to 15 00:01:21,236 --> 00:01:25,116 Speaker 1: emotion and personal belief. It's like, I want to believe 16 00:01:25,156 --> 00:01:29,356 Speaker 1: that nachos are the ideal balanced nutritional snack that appeals 17 00:01:29,396 --> 00:01:32,276 Speaker 1: to my emotional and personal belief system, because God, I 18 00:01:32,356 --> 00:01:36,276 Speaker 1: love nattos. So I'll go and I'll find some vague 19 00:01:36,436 --> 00:01:40,156 Speaker 1: chitchat online that tells me, you know, something melted cheese 20 00:01:40,196 --> 00:01:42,836 Speaker 1: is totally full of calcium that is good for your bones, 21 00:01:43,236 --> 00:01:45,516 Speaker 1: and it's important for you, as an immigrant to the US, 22 00:01:45,556 --> 00:01:50,396 Speaker 1: to assimilate by eating their national dish of natos. So 23 00:01:50,796 --> 00:01:54,156 Speaker 1: I'll convince myself of that, and I'll maybe even eat 24 00:01:54,196 --> 00:01:59,156 Speaker 1: myself into a delicious early grave. The age we live in, 25 00:01:59,276 --> 00:02:03,036 Speaker 1: the digital age, affects every narrative we see and absorb, 26 00:02:03,236 --> 00:02:06,436 Speaker 1: and that can be news based, or cultural or artistic. 27 00:02:07,116 --> 00:02:09,916 Speaker 1: We have always had an instinct to find information that 28 00:02:09,996 --> 00:02:13,476 Speaker 1: sinks with our perspective, and now a host of new 29 00:02:13,556 --> 00:02:17,356 Speaker 1: platforms are only too happy to oblige that part of us. 30 00:02:18,156 --> 00:02:21,996 Speaker 1: Pew reports that an analysis of almost four hundred million 31 00:02:22,116 --> 00:02:26,916 Speaker 1: Facebook users interactions with over nine hundred news outlets found 32 00:02:26,916 --> 00:02:31,396 Speaker 1: that people tend to seek information that aligns with their views. 33 00:02:31,436 --> 00:02:34,556 Speaker 1: That makes many of us vulnerable to accepting and acting 34 00:02:34,636 --> 00:02:39,316 Speaker 1: on misinformation. Social media firms are under pressure to halt 35 00:02:39,396 --> 00:02:43,116 Speaker 1: the spread of fake contents on their platforms, and we 36 00:02:43,196 --> 00:02:46,836 Speaker 1: know that the problem has both human and technical side, 37 00:02:47,116 --> 00:02:51,876 Speaker 1: and so too does any potential solution. Reneed Arresta is 38 00:02:51,876 --> 00:02:55,196 Speaker 1: the director of research at New Knowledge and a Mozilla 39 00:02:55,236 --> 00:02:59,356 Speaker 1: Fellow in Media Misinformation and Trust. She investigates the spread 40 00:02:59,396 --> 00:03:03,796 Speaker 1: of malign narratives across social networks and helps policymakers to 41 00:03:03,916 --> 00:03:08,116 Speaker 1: understand and respond to the problem. Renee has advised Congress 42 00:03:08,116 --> 00:03:11,636 Speaker 1: and the State Department, and she studies some fascinating areas 43 00:03:11,676 --> 00:03:18,756 Speaker 1: of disinformation in contexts like pseudoscience, conspiracies, terrorism, and state 44 00:03:18,836 --> 00:03:23,556 Speaker 1: sponsored information warfare, all that spooky stuff. I'm so glad 45 00:03:23,596 --> 00:03:26,476 Speaker 1: she is scouting ahead and sending us back the best 46 00:03:26,476 --> 00:03:29,476 Speaker 1: ways to deal with this. Let's take a listen and 47 00:03:29,636 --> 00:03:33,076 Speaker 1: I'll speak to you after. So, Renee, you're one of 48 00:03:33,076 --> 00:03:38,836 Speaker 1: the few people who identified the problem of online anti 49 00:03:38,916 --> 00:03:42,476 Speaker 1: vax disinformation very early on. How did you first come 50 00:03:42,476 --> 00:03:43,996 Speaker 1: into contact with the problem? How did you know it 51 00:03:44,076 --> 00:03:46,396 Speaker 1: was a problem at all. I started working on a 52 00:03:46,476 --> 00:03:49,236 Speaker 1: law in California called SP two seventy seven, and it 53 00:03:49,276 --> 00:03:51,636 Speaker 1: was a law to eliminate vaccine opt outs. And I 54 00:03:51,676 --> 00:03:54,236 Speaker 1: was a parent at a new baby, and I wanted, 55 00:03:54,316 --> 00:03:56,596 Speaker 1: as a mom, just to volunteer to help get this 56 00:03:56,676 --> 00:03:59,476 Speaker 1: law passed. So I am a data analyst, and I 57 00:03:59,556 --> 00:04:03,476 Speaker 1: offered to do some analysis into things like the social 58 00:04:03,516 --> 00:04:06,436 Speaker 1: media conversation around the law. And I was really surprised 59 00:04:06,476 --> 00:04:09,236 Speaker 1: because the legislators, there are a number of legislators on 60 00:04:09,756 --> 00:04:11,876 Speaker 1: both parties who were supporting the law. They were saying 61 00:04:11,876 --> 00:04:14,356 Speaker 1: that their constituents were polling at around eighty five percent 62 00:04:14,396 --> 00:04:17,196 Speaker 1: in favor, but the social media conversation was almost one 63 00:04:17,236 --> 00:04:19,996 Speaker 1: hundred percent negative, and that was on Facebook and Twitter. 64 00:04:20,396 --> 00:04:23,316 Speaker 1: So I started working with another data scientist named Glad 65 00:04:23,356 --> 00:04:26,596 Speaker 1: Latan to look at the conversation on Twitter, to look 66 00:04:26,596 --> 00:04:29,756 Speaker 1: at the different distinct groups, how they were evolving their messages, 67 00:04:30,036 --> 00:04:33,316 Speaker 1: how they were connecting with other activists outside of California, 68 00:04:33,436 --> 00:04:36,516 Speaker 1: how sometimes activists outside of California. It turned out we're 69 00:04:36,716 --> 00:04:40,236 Speaker 1: pretending to be Californians, bunches of new accounts that had 70 00:04:40,236 --> 00:04:42,556 Speaker 1: been created, and we were really looking at the idea 71 00:04:42,596 --> 00:04:44,596 Speaker 1: of what had no name then but kind of came 72 00:04:44,636 --> 00:04:48,276 Speaker 1: to be called manufactured consensus, the idea that the conversation 73 00:04:48,356 --> 00:04:51,676 Speaker 1: online was really being driven by a relatively small number 74 00:04:51,676 --> 00:04:54,396 Speaker 1: of people who were using things like tools to be 75 00:04:54,476 --> 00:04:58,396 Speaker 1: always on, constantly being in the hashtag Facebook groups and 76 00:04:58,436 --> 00:05:01,436 Speaker 1: ads to amplify their message, and then the way that 77 00:05:01,476 --> 00:05:04,516 Speaker 1: the algorithm was amplifying the message. In addition to that, 78 00:05:04,636 --> 00:05:07,116 Speaker 1: so ways in which I, as a parent who had 79 00:05:07,156 --> 00:05:09,796 Speaker 1: just gotten involved in the conversation, had just demonstrated an 80 00:05:09,876 --> 00:05:12,596 Speaker 1: interest in vaccine policy, was all of a sudden getting 81 00:05:12,596 --> 00:05:15,916 Speaker 1: pushed tons of anti vaccine content on Facebook. It was 82 00:05:15,996 --> 00:05:18,556 Speaker 1: recommending groups to me, it was recommending pages to me. 83 00:05:19,036 --> 00:05:23,116 Speaker 1: And the realization that what was really not a very 84 00:05:23,236 --> 00:05:27,636 Speaker 1: large number of people was actually having an extreme disproportionate 85 00:05:27,676 --> 00:05:31,956 Speaker 1: amount of a share of voice in the conversation, and 86 00:05:32,036 --> 00:05:34,396 Speaker 1: did you have to create tools in order to begin 87 00:05:34,476 --> 00:05:37,676 Speaker 1: identifying who the people were who was being pushed. They 88 00:05:37,676 --> 00:05:39,996 Speaker 1: were actually not very quiet about that. There was a 89 00:05:40,036 --> 00:05:42,596 Speaker 1: page called tweet for Vaccine Freedom, and it was actually 90 00:05:42,916 --> 00:05:45,076 Speaker 1: you know, when out of state activists were asking how 91 00:05:45,116 --> 00:05:47,516 Speaker 1: can we help because the entire anti vaccine movement across 92 00:05:47,516 --> 00:05:50,676 Speaker 1: the entire United States decided to fight this battle. They 93 00:05:50,676 --> 00:05:53,036 Speaker 1: would say like, oh, you should just create an account 94 00:05:53,076 --> 00:05:55,636 Speaker 1: and say or from California. So it was actually really transparent. 95 00:05:55,676 --> 00:05:57,316 Speaker 1: It wasn't that hard to figure out that there were 96 00:05:57,316 --> 00:06:00,676 Speaker 1: people pretending to be from California. There were also Twitter 97 00:06:00,716 --> 00:06:02,996 Speaker 1: accounts that all of a sudden had a vested interest 98 00:06:03,036 --> 00:06:05,636 Speaker 1: in California politics. But if you read their past material, 99 00:06:05,676 --> 00:06:07,716 Speaker 1: which again is also public it was really right out 100 00:06:07,716 --> 00:06:09,516 Speaker 1: there that that's not where they were actually from. Kind 101 00:06:09,556 --> 00:06:12,996 Speaker 1: of a very interesting because it was extremely small, local 102 00:06:13,076 --> 00:06:15,636 Speaker 1: and niche we you know, we thought in California. But 103 00:06:15,756 --> 00:06:18,156 Speaker 1: as the law began to get more press coverage and 104 00:06:18,196 --> 00:06:21,356 Speaker 1: things or would actually be like comments section battles, you know, 105 00:06:21,396 --> 00:06:23,396 Speaker 1: the same kinds of stuff that we saw later with 106 00:06:23,556 --> 00:06:26,836 Speaker 1: you know, entities that go and are like almost incentivized 107 00:06:26,876 --> 00:06:29,916 Speaker 1: to leave comments on news articles to shape a perception 108 00:06:30,076 --> 00:06:33,116 Speaker 1: about the topic. And actually we on the provac side thought, 109 00:06:33,116 --> 00:06:34,916 Speaker 1: oh boy, I guess we're gonna you know, we need 110 00:06:34,956 --> 00:06:38,076 Speaker 1: to do this too. Are we really engaging in Okay? 111 00:06:38,076 --> 00:06:39,636 Speaker 1: They commented over here, so you know, we have to 112 00:06:39,636 --> 00:06:42,476 Speaker 1: go comment over here. They have bots that are on 113 00:06:42,516 --> 00:06:44,436 Speaker 1: twenty four to seven? Do we need bots that are 114 00:06:44,436 --> 00:06:47,196 Speaker 1: on twenty four seven? Just became this this interesting firsthand 115 00:06:47,196 --> 00:06:48,716 Speaker 1: experience of what it was going to be like to 116 00:06:48,836 --> 00:06:51,716 Speaker 1: try to run any kind of influence or policy campaign 117 00:06:51,756 --> 00:06:54,036 Speaker 1: in the future. I found it really troubling, especially when 118 00:06:54,036 --> 00:06:58,076 Speaker 1: the algorithms just began recommending anti vaccine content to me constantly. 119 00:06:58,396 --> 00:07:01,956 Speaker 1: And how did the Facebook and Twitter and other algorithms work. 120 00:07:02,036 --> 00:07:04,556 Speaker 1: Were they affected by this campaign? The where the search 121 00:07:04,596 --> 00:07:06,836 Speaker 1: engines affected by it. I don't think the search engines 122 00:07:06,876 --> 00:07:09,276 Speaker 1: as much because it was you know, the Google is 123 00:07:09,276 --> 00:07:11,596 Speaker 1: a little bit more sophisticated about this stuff than the 124 00:07:11,636 --> 00:07:14,716 Speaker 1: social platforms. Social platforms the number one signal that they're 125 00:07:14,796 --> 00:07:17,596 Speaker 1: using as popularity, and so you either if you have 126 00:07:17,676 --> 00:07:20,756 Speaker 1: real popularity or if you can feign popularity. The number 127 00:07:20,796 --> 00:07:23,836 Speaker 1: of likes and engagements and comments and things is what decides, 128 00:07:24,076 --> 00:07:26,436 Speaker 1: you know, whether this is how Facebook was deciding what 129 00:07:26,476 --> 00:07:30,036 Speaker 1: gets pushed into your feed. Instagram is like that too. 130 00:07:30,196 --> 00:07:33,476 Speaker 1: Google has a framework now it has a proper name. 131 00:07:33,476 --> 00:07:35,636 Speaker 1: It's called Your Money or Your Life, and it says 132 00:07:35,676 --> 00:07:39,716 Speaker 1: that on topics related to health issues and financial issues 133 00:07:39,756 --> 00:07:41,516 Speaker 1: they have to have a higher standard of care to 134 00:07:41,556 --> 00:07:44,396 Speaker 1: make sure that it isn't just what's popular that's rising 135 00:07:44,396 --> 00:07:47,236 Speaker 1: to the top. But even with that policy, one of 136 00:07:47,236 --> 00:07:50,796 Speaker 1: the things that we consistently see is anti vaccine activists 137 00:07:50,836 --> 00:07:54,316 Speaker 1: producing content at a higher rate and also candidly more 138 00:07:54,356 --> 00:07:57,556 Speaker 1: engaging content, you know, a much more emotionally resonant versus 139 00:07:57,956 --> 00:08:02,356 Speaker 1: more authoritative medical quote unquote establishment doctors, the CDC, the 140 00:08:02,436 --> 00:08:05,676 Speaker 1: National Instituites for Health, their contents not as emotionally resonant. 141 00:08:05,716 --> 00:08:08,116 Speaker 1: It doesn't get as much engagement, and so the search 142 00:08:08,156 --> 00:08:12,996 Speaker 1: engines and the algorithm aren't amplifying the more factual, reality 143 00:08:13,036 --> 00:08:16,076 Speaker 1: based content, and instead what we're getting is this conspiratorial stuff. 144 00:08:16,356 --> 00:08:19,596 Speaker 1: Walk me through what it means to be emotionally resonant online. 145 00:08:19,796 --> 00:08:21,796 Speaker 1: Is this something that's being done deliberately to the people 146 00:08:21,796 --> 00:08:23,996 Speaker 1: who are creating it understand that that's what it is? 147 00:08:24,516 --> 00:08:26,836 Speaker 1: Or is it that the human brain is just tuned 148 00:08:26,836 --> 00:08:30,756 Speaker 1: to conspiracies and prefers them. Some of it is platform culturism, 149 00:08:30,796 --> 00:08:34,276 Speaker 1: of it is the way that the algorithm understands engagement. 150 00:08:34,316 --> 00:08:36,596 Speaker 1: So there's the human element which gets kind of the 151 00:08:36,636 --> 00:08:39,276 Speaker 1: initial signal shows that there's a lot of people who 152 00:08:39,276 --> 00:08:41,476 Speaker 1: are watching this, and the algorithm recognizes that a lot 153 00:08:41,476 --> 00:08:44,556 Speaker 1: of people are watching it and then begins the amplification process. 154 00:08:44,796 --> 00:08:46,796 Speaker 1: But the first step is actually the content, of course, 155 00:08:47,076 --> 00:08:50,596 Speaker 1: and in that particular area, it's usually a first person, 156 00:08:50,916 --> 00:08:53,676 Speaker 1: you know, looking directly at a camera, speaking about a 157 00:08:53,676 --> 00:08:57,556 Speaker 1: personal experience they've had, recounting a narrative or an interesting story. 158 00:08:57,996 --> 00:08:59,956 Speaker 1: So a lot of times with the anti vaccine movement, 159 00:08:59,996 --> 00:09:02,836 Speaker 1: that's a person claiming that their child has autism and 160 00:09:03,036 --> 00:09:05,756 Speaker 1: telling a story, you know, usually very sad story about 161 00:09:05,796 --> 00:09:09,356 Speaker 1: their child's health, and so it is engaging. It is 162 00:09:09,436 --> 00:09:13,596 Speaker 1: much more resonant versus seeing kind of infomercial about how 163 00:09:13,676 --> 00:09:16,956 Speaker 1: vaccines don't cause autism because thousands and thousands and thousands 164 00:09:16,996 --> 00:09:18,796 Speaker 1: of studies have said that they do not. I know 165 00:09:18,876 --> 00:09:21,516 Speaker 1: that you were part of the Senate commission that looked 166 00:09:21,556 --> 00:09:24,796 Speaker 1: through material that we knew that Facebook handed over to 167 00:09:24,836 --> 00:09:28,076 Speaker 1: Congress which was originally created by the IRA, the Russian 168 00:09:28,436 --> 00:09:31,716 Speaker 1: Internet Agency, in order to influence the US elections. When 169 00:09:31,716 --> 00:09:34,076 Speaker 1: you looked over that material, did it seem to use 170 00:09:34,116 --> 00:09:36,596 Speaker 1: those same tactics? Can you see a relationship between the 171 00:09:36,636 --> 00:09:40,116 Speaker 1: way the Russian influence campaign worked and the anti vax campaigns? 172 00:09:40,276 --> 00:09:43,516 Speaker 1: The Russian content was distinct, and that this was a 173 00:09:43,556 --> 00:09:46,556 Speaker 1: foreign intelligent service of a foreign entity that was trying 174 00:09:46,556 --> 00:09:49,236 Speaker 1: to pretend to be American. So it was far more 175 00:09:49,316 --> 00:09:53,556 Speaker 1: duplicitous than anything that we've seen related to domestic activists 176 00:09:53,636 --> 00:09:56,036 Speaker 1: pushing for a cause. Really, but what was happening there 177 00:09:56,156 --> 00:10:00,076 Speaker 1: was again they were taking these extremely big topics things 178 00:10:00,116 --> 00:10:02,436 Speaker 1: like who is America for? What does it mean to 179 00:10:02,436 --> 00:10:04,676 Speaker 1: be an American? How do we feel about immigration? How 180 00:10:04,676 --> 00:10:06,436 Speaker 1: do we feel about gay rights? How do we feel 181 00:10:06,436 --> 00:10:10,676 Speaker 1: about police brutality? They were creating these pages, and each 182 00:10:10,716 --> 00:10:14,436 Speaker 1: page was designed for a very particular type of person, 183 00:10:14,716 --> 00:10:17,236 Speaker 1: So they were really creating these tribes, again relying on 184 00:10:17,276 --> 00:10:21,116 Speaker 1: the sort of first person experience, first person concerns and fears, 185 00:10:21,436 --> 00:10:24,116 Speaker 1: and putting out content that was again very much focused 186 00:10:24,156 --> 00:10:27,476 Speaker 1: on achieving an emotional response. So for the black community, 187 00:10:27,516 --> 00:10:31,276 Speaker 1: the content took the form of constant references to police 188 00:10:31,356 --> 00:10:34,796 Speaker 1: violence mixed in with narratives of pride, and so it 189 00:10:34,836 --> 00:10:38,276 Speaker 1: was really very much designed to evoke cultural pride and 190 00:10:38,316 --> 00:10:41,396 Speaker 1: then also a sense of deep harm. And on the 191 00:10:41,996 --> 00:10:44,956 Speaker 1: right leaning pages, it was really concerned about what America 192 00:10:45,076 --> 00:10:47,036 Speaker 1: is and who it's for, and so a lot of 193 00:10:47,236 --> 00:10:51,236 Speaker 1: photos of things like homeless veterans. This is a very 194 00:10:51,236 --> 00:10:52,876 Speaker 1: real problem that we have in this country, and they 195 00:10:52,876 --> 00:10:55,796 Speaker 1: were using the images of homeless veterans to say, why 196 00:10:55,836 --> 00:10:57,676 Speaker 1: are we allowing in all of these outsiders when we 197 00:10:57,716 --> 00:10:59,836 Speaker 1: can't take care of our own. This is how propaganda 198 00:10:59,916 --> 00:11:01,836 Speaker 1: is most effective. It's when it has some degree of 199 00:11:01,876 --> 00:11:05,036 Speaker 1: truth to it, and it spins it just enough that 200 00:11:05,156 --> 00:11:08,316 Speaker 1: it doesn't necessarily trigger the part of the brain that says, hey, 201 00:11:08,316 --> 00:11:11,676 Speaker 1: this is false. Instead it the person relies on the 202 00:11:11,716 --> 00:11:14,676 Speaker 1: emotional reaction to it, and that's how they begin to 203 00:11:15,476 --> 00:11:18,276 Speaker 1: develop a sustained relationship with the page and sustained engagement 204 00:11:18,276 --> 00:11:20,276 Speaker 1: with that type of content. You know, as I'm listening 205 00:11:20,316 --> 00:11:23,116 Speaker 1: to you, I'm wondering whether different kinds of propagandists they 206 00:11:23,196 --> 00:11:26,756 Speaker 1: understand now that they need to tailor messages to particular audiences. 207 00:11:26,956 --> 00:11:28,956 Speaker 1: Is it the case that some of the solutions to 208 00:11:29,036 --> 00:11:32,676 Speaker 1: this they're also going to involve thinking differently about different 209 00:11:32,676 --> 00:11:35,996 Speaker 1: audiences or offering different kinds of counter messaging or counter 210 00:11:36,036 --> 00:11:39,196 Speaker 1: strategies to different audiences. Yeah. Absolutely, And this is something 211 00:11:39,236 --> 00:11:41,356 Speaker 1: that you know. A third area I worked in was 212 00:11:41,476 --> 00:11:44,156 Speaker 1: countering violent extremism. Briefly was Isis. The idea that we 213 00:11:44,156 --> 00:11:46,396 Speaker 1: would kick Isis off the platforms was sort of a 214 00:11:46,476 --> 00:11:49,156 Speaker 1: stretch at the time. There were a lot of people 215 00:11:49,196 --> 00:11:51,036 Speaker 1: who were very concerned at the idea that we would 216 00:11:51,116 --> 00:11:53,756 Speaker 1: delete terrorists accounts, and so a lot of the focus 217 00:11:53,756 --> 00:11:56,116 Speaker 1: instead was on counter messaging. How do we reach these 218 00:11:56,116 --> 00:11:59,476 Speaker 1: audiences that are receptive to ISIS propaganda and present counter 219 00:11:59,596 --> 00:12:02,436 Speaker 1: narratives to them. Who is the authentic voice for the 220 00:12:02,436 --> 00:12:05,276 Speaker 1: counter narrative. It's definitely not the United States State department, 221 00:12:05,676 --> 00:12:07,436 Speaker 1: So who is it and what are the ways in 222 00:12:07,476 --> 00:12:10,236 Speaker 1: which we can come together to think about ways to 223 00:12:10,676 --> 00:12:14,196 Speaker 1: counter message to try to present people with it an alternate, 224 00:12:14,556 --> 00:12:17,876 Speaker 1: also emotionally resonant narrative instead of just saying it's a 225 00:12:17,916 --> 00:12:19,876 Speaker 1: bad idea to be a terrorist because you're going to 226 00:12:19,916 --> 00:12:21,636 Speaker 1: go to jail or you're going to die. A lot 227 00:12:21,676 --> 00:12:24,836 Speaker 1: of the tribal deep affinity ties is what is my 228 00:12:24,956 --> 00:12:27,156 Speaker 1: place in society? This is something that comes up with 229 00:12:27,196 --> 00:12:30,996 Speaker 1: conspiracy theorists also, they're looking for answers, they're looking for 230 00:12:31,036 --> 00:12:34,516 Speaker 1: an explanation. What you get hooked into oftentimes is what 231 00:12:34,676 --> 00:12:37,716 Speaker 1: is most visible to you, what's most prevalent in your 232 00:12:38,076 --> 00:12:41,036 Speaker 1: space at that moment. Now that we're spending so much 233 00:12:41,036 --> 00:12:45,236 Speaker 1: more of our time online, things like ad targeting and 234 00:12:45,876 --> 00:12:49,116 Speaker 1: participation in Facebook groups where you're kind of declaring a 235 00:12:49,156 --> 00:12:53,276 Speaker 1: particular alignment mean that bad actors who want to target 236 00:12:53,316 --> 00:12:55,836 Speaker 1: you with certain types of propaganda can find you very easily. 237 00:12:56,276 --> 00:12:59,876 Speaker 1: And can we reapply some of that thinking back, for example, 238 00:13:00,516 --> 00:13:03,476 Speaker 1: to the anti vax problem. Can we think about counter 239 00:13:03,516 --> 00:13:06,116 Speaker 1: messaging there? Can we think about how to reach people 240 00:13:06,796 --> 00:13:10,716 Speaker 1: using counter emotional stories? Yes, absolutely, that is That's something 241 00:13:10,756 --> 00:13:13,716 Speaker 1: that our groups like Voices for Vaccines are trying to 242 00:13:13,756 --> 00:13:15,916 Speaker 1: work on that. The group that was formerly called every 243 00:13:15,956 --> 00:13:17,956 Speaker 1: child by two, it now goes by vaccinate your family 244 00:13:18,036 --> 00:13:20,076 Speaker 1: is trying to do that. We have to get out 245 00:13:20,076 --> 00:13:23,516 Speaker 1: of statistics and get into storytelling. That's the one of 246 00:13:23,516 --> 00:13:27,196 Speaker 1: the key takeaways of how the information ecosystem has evolved. 247 00:13:27,676 --> 00:13:29,716 Speaker 1: If you look at even just from a design perspective, 248 00:13:29,756 --> 00:13:31,476 Speaker 1: one of the things I always get at is the 249 00:13:31,916 --> 00:13:35,116 Speaker 1: the subject of the narrative is interesting when you're thinking 250 00:13:35,156 --> 00:13:37,916 Speaker 1: about how to counter message to a particular group of people. 251 00:13:38,076 --> 00:13:40,636 Speaker 1: But when you think about this as a problem written large, 252 00:13:40,756 --> 00:13:42,956 Speaker 1: a lot of it comes down to the algorithms and 253 00:13:42,956 --> 00:13:46,596 Speaker 1: the design. And so memes in particular are getting more 254 00:13:46,636 --> 00:13:49,076 Speaker 1: and more important in our lives. And that's because the 255 00:13:49,116 --> 00:13:52,996 Speaker 1: design of the platform itself is privileging this large you know, 256 00:13:53,036 --> 00:13:55,676 Speaker 1: this large square image or this piece of video, this 257 00:13:55,956 --> 00:13:59,236 Speaker 1: short video clip. So what can you convey in the 258 00:14:00,076 --> 00:14:03,476 Speaker 1: construct of that design. As people are scrolling by, they 259 00:14:03,516 --> 00:14:06,276 Speaker 1: see your message in it immediately sticks. The fact that 260 00:14:06,356 --> 00:14:09,076 Speaker 1: the algorithm will continue to serve up types of content 261 00:14:09,236 --> 00:14:11,636 Speaker 1: that you've engaged within the past means that if you 262 00:14:11,676 --> 00:14:14,556 Speaker 1: do engage with anti vaccine content, you're likely to see 263 00:14:14,596 --> 00:14:19,036 Speaker 1: more of it. The challenge of algorithms that don't know 264 00:14:19,076 --> 00:14:21,436 Speaker 1: what they're pushing because they have no actual awareness of 265 00:14:21,436 --> 00:14:24,276 Speaker 1: what the underlying content is. So they treat something that's 266 00:14:24,276 --> 00:14:28,636 Speaker 1: potentially radical, they treat something that's potentially blatantly false the 267 00:14:28,676 --> 00:14:31,316 Speaker 1: exact same way that they would treat something that's accurate 268 00:14:31,436 --> 00:14:34,316 Speaker 1: or uplifting. They don't actually know. They just know that 269 00:14:34,356 --> 00:14:37,636 Speaker 1: this content drives engagement, and so they continue to show 270 00:14:37,676 --> 00:14:40,716 Speaker 1: it to people. We see all this disinformation online. We 271 00:14:41,156 --> 00:14:42,876 Speaker 1: you know, we hear about it. You know, we can 272 00:14:42,916 --> 00:14:45,956 Speaker 1: sometimes see it in our Google searches. But doesn't really matter. 273 00:14:46,076 --> 00:14:48,716 Speaker 1: I mean, for example, in the anti vax campaign, has 274 00:14:48,756 --> 00:14:50,836 Speaker 1: this really affected anything, Does it make any difference? Or 275 00:14:51,036 --> 00:14:52,996 Speaker 1: is this just stuff that exists somewhere in the ether 276 00:14:53,116 --> 00:14:55,396 Speaker 1: and if we ignore it will go away. Let me 277 00:14:55,956 --> 00:14:57,836 Speaker 1: give you two quick examples on that. First of all, 278 00:14:57,836 --> 00:15:00,556 Speaker 1: with the anti vaccine movement, Yes, it absolutely has an impact. 279 00:15:00,676 --> 00:15:03,196 Speaker 1: It really creates a lot of fear and hesitancy, and 280 00:15:03,276 --> 00:15:07,556 Speaker 1: that translates very directly into vaccination rates declining in the 281 00:15:07,596 --> 00:15:10,236 Speaker 1: communities that are that are seeing it. And so this 282 00:15:10,316 --> 00:15:13,276 Speaker 1: is something that in California. The reason I started looking 283 00:15:13,316 --> 00:15:17,276 Speaker 1: at it was because immunization rates in California communities had declined, 284 00:15:17,316 --> 00:15:19,116 Speaker 1: and when I was trying to find a preschool for 285 00:15:19,236 --> 00:15:22,076 Speaker 1: my son, I was actually looking at these rates, and 286 00:15:22,316 --> 00:15:25,676 Speaker 1: there are certain schools in California with thirty percent immunization rates, 287 00:15:26,036 --> 00:15:29,716 Speaker 1: which is terrifying. That's like South Sudan. The reason that 288 00:15:29,716 --> 00:15:31,756 Speaker 1: we passed the lawn California was because we wound up 289 00:15:31,756 --> 00:15:34,876 Speaker 1: with the Disneyland measles outbreak, where two hundred and something 290 00:15:34,876 --> 00:15:36,836 Speaker 1: people got sick and I believe a quarter had to 291 00:15:36,836 --> 00:15:40,756 Speaker 1: be hospitalized. So this was a very real outcome of 292 00:15:41,116 --> 00:15:45,316 Speaker 1: that kind of misinformation becoming so pervasive to people, creating 293 00:15:45,316 --> 00:15:48,516 Speaker 1: that very real fear and then leading to an outbreak 294 00:15:48,956 --> 00:15:50,796 Speaker 1: in the case of Russia. Just because a lot of 295 00:15:50,796 --> 00:15:53,396 Speaker 1: people think about this is just related to the election. No, 296 00:15:53,516 --> 00:15:55,476 Speaker 1: what they were doing was they were also creating real 297 00:15:55,516 --> 00:15:59,316 Speaker 1: world events. So they were sponsoring protests, and one of 298 00:15:59,356 --> 00:16:01,876 Speaker 1: the things that they sponsored was an incident in Texas 299 00:16:01,956 --> 00:16:04,636 Speaker 1: where they had two competing protests on the same day 300 00:16:04,676 --> 00:16:08,676 Speaker 1: at the same time. So from Saint Petersburg, Troll created 301 00:16:08,676 --> 00:16:11,916 Speaker 1: a Facebook event saying that people with Texas Pride had 302 00:16:11,916 --> 00:16:14,156 Speaker 1: to come and protest outside of an Islamic center to 303 00:16:14,196 --> 00:16:17,756 Speaker 1: defend their way of life. They also posted an event 304 00:16:18,116 --> 00:16:20,516 Speaker 1: calling on members of the Islamic Center to come out 305 00:16:20,516 --> 00:16:24,156 Speaker 1: and defend the Islamic faith. So they sponsored two protests 306 00:16:24,196 --> 00:16:26,196 Speaker 1: on the same day at the same time, across the 307 00:16:26,236 --> 00:16:28,316 Speaker 1: street from each other. And you can go on YouTube 308 00:16:28,356 --> 00:16:30,196 Speaker 1: and you can see the video footage from that day 309 00:16:30,396 --> 00:16:34,276 Speaker 1: of people showing up with kind of anti Islamic material 310 00:16:34,356 --> 00:16:35,836 Speaker 1: on one side of the street and then people on 311 00:16:35,876 --> 00:16:37,596 Speaker 1: the other side of the streets screaming back at them, 312 00:16:37,596 --> 00:16:41,396 Speaker 1: and police getting involved in breaking up altercations. So this 313 00:16:41,476 --> 00:16:45,956 Speaker 1: is an example of very real world tension erupting as 314 00:16:45,956 --> 00:16:49,196 Speaker 1: a result of online disinformation. When you first started looking 315 00:16:49,236 --> 00:16:52,796 Speaker 1: at this problem, did people believe it was a problem. 316 00:16:52,876 --> 00:16:55,756 Speaker 1: Opinion polls all showed people were in favor of vaccinations. 317 00:16:56,116 --> 00:16:58,876 Speaker 1: You saw something quite different online. How did you convince 318 00:16:58,916 --> 00:17:00,876 Speaker 1: people that this was something they need to take seriously. 319 00:17:01,156 --> 00:17:04,796 Speaker 1: In the California case in particular, I sent what I 320 00:17:04,876 --> 00:17:08,356 Speaker 1: was seeing, you know, kind of quantifiable evidence to the 321 00:17:08,476 --> 00:17:12,316 Speaker 1: legislators and said, I don't think that people are screaming 322 00:17:12,316 --> 00:17:15,356 Speaker 1: at you online, they're threatening you online, You're seeing all 323 00:17:15,396 --> 00:17:18,036 Speaker 1: of this anger and rage in the hashtags, I don't 324 00:17:18,156 --> 00:17:21,036 Speaker 1: think that these are not your constituents, where it's pretty 325 00:17:21,076 --> 00:17:25,196 Speaker 1: pretty clear that these are not all even Californians. So 326 00:17:25,436 --> 00:17:29,236 Speaker 1: when you make your decision, I would lean into the 327 00:17:29,316 --> 00:17:31,956 Speaker 1: polling numbers and the communications with your actual constituents. I 328 00:17:31,956 --> 00:17:34,236 Speaker 1: don't think that we can treat the online conversation as 329 00:17:34,316 --> 00:17:38,476 Speaker 1: representative of the reality of the population of California. So 330 00:17:38,556 --> 00:17:41,876 Speaker 1: in that particular case, it was just really kind of 331 00:17:41,916 --> 00:17:45,156 Speaker 1: appealing directly to the legislators with the evidence the challenges 332 00:17:45,276 --> 00:17:47,716 Speaker 1: it really does bump up against things like freedom of 333 00:17:47,756 --> 00:17:49,996 Speaker 1: expression right. So you have a right to have an 334 00:17:49,996 --> 00:17:52,636 Speaker 1: anti vaccine opinion. Of course you have a right to 335 00:17:52,636 --> 00:17:56,156 Speaker 1: put the content online. The challenge was at the time, 336 00:17:56,596 --> 00:18:00,796 Speaker 1: the recommendation engine, the trending algorithm, the ways in which 337 00:18:01,356 --> 00:18:06,996 Speaker 1: Twitter and Facebook were amplifying information was very different, far 338 00:18:07,036 --> 00:18:09,316 Speaker 1: more primitive then than it is even now two and 339 00:18:09,356 --> 00:18:11,596 Speaker 1: a half years later. After those of us who work 340 00:18:11,596 --> 00:18:13,676 Speaker 1: on this challenge have kind of been constantly beating the 341 00:18:13,756 --> 00:18:17,396 Speaker 1: drum with example after example of example of how this 342 00:18:17,516 --> 00:18:20,596 Speaker 1: is manifesting in the real world. How do we preserve 343 00:18:20,676 --> 00:18:23,316 Speaker 1: freedom of expression while at the same time recognizing that 344 00:18:23,916 --> 00:18:27,356 Speaker 1: the platform is pushing this point of view at people 345 00:18:27,556 --> 00:18:30,116 Speaker 1: people aren't even know me. In particular, I'm not going 346 00:18:30,116 --> 00:18:33,676 Speaker 1: out there typing in anti vaccine search terms. The recommendation 347 00:18:33,716 --> 00:18:36,076 Speaker 1: engine is just pushing it to me because it's seeing 348 00:18:36,076 --> 00:18:39,596 Speaker 1: that I've expressed an interest in vaccines in general. As 349 00:18:39,676 --> 00:18:42,036 Speaker 1: part of working on this law, I suppose there's also 350 00:18:42,076 --> 00:18:44,596 Speaker 1: a question of Okay, you have a right to write something, 351 00:18:44,636 --> 00:18:47,756 Speaker 1: but then do you have a right to artificially amplify 352 00:18:47,836 --> 00:18:52,156 Speaker 1: it using bots and search engine optimization? So everyone has 353 00:18:52,156 --> 00:18:56,596 Speaker 1: the right to freedom of expression online. The secondary piece 354 00:18:56,596 --> 00:18:58,156 Speaker 1: of that, though, is do you have a right to 355 00:18:58,236 --> 00:19:02,876 Speaker 1: free reach your right to algorithmic amplification. Nobody has that right. 356 00:19:02,996 --> 00:19:04,876 Speaker 1: That is not part of the First Amendment, That is 357 00:19:04,916 --> 00:19:07,956 Speaker 1: not part of our cultural experience of what it means 358 00:19:07,956 --> 00:19:10,116 Speaker 1: to have a right to express, or you have never 359 00:19:10,196 --> 00:19:14,396 Speaker 1: had the right to free mass dissemination as well. That's 360 00:19:14,436 --> 00:19:17,476 Speaker 1: the piece where as people begin to talk about how 361 00:19:17,476 --> 00:19:19,596 Speaker 1: the platform should think about these things. One of the 362 00:19:19,636 --> 00:19:22,796 Speaker 1: ways that we can continue to maximize freedom of expression 363 00:19:23,236 --> 00:19:25,556 Speaker 1: is to allow people to speak, but also for the 364 00:19:25,556 --> 00:19:29,876 Speaker 1: algorithm to perhaps not begin to take that kind of 365 00:19:30,436 --> 00:19:37,676 Speaker 1: sensationalist content and proactively broadcast it out to massive quantities 366 00:19:37,716 --> 00:19:41,596 Speaker 1: of people because it checks the boxes of being sensational 367 00:19:41,636 --> 00:19:44,436 Speaker 1: and emotional. And do you think that it's going to 368 00:19:44,476 --> 00:19:47,316 Speaker 1: be enough to discuss this with the platforms, for people 369 00:19:47,356 --> 00:19:50,236 Speaker 1: like you who have you, who are respected on these issues, 370 00:19:50,276 --> 00:19:52,796 Speaker 1: to talk about it with people at Facebook and Google, 371 00:19:53,236 --> 00:19:54,836 Speaker 1: or is this something that we're going to need to 372 00:19:54,876 --> 00:19:58,076 Speaker 1: regulate or have Congress step in on. I don't think 373 00:19:58,076 --> 00:20:02,076 Speaker 1: you can have Congress regulate what algorithms amplify. I think 374 00:20:02,076 --> 00:20:03,956 Speaker 1: that that would probably be a little bit too close 375 00:20:04,076 --> 00:20:08,116 Speaker 1: to Congress making decisions on speech. A lot of the 376 00:20:08,156 --> 00:20:12,716 Speaker 1: dissemination that come about through inauthentic amplification through things like 377 00:20:12,756 --> 00:20:15,236 Speaker 1: bots and stuff, can be addressed without even knowing what 378 00:20:15,276 --> 00:20:17,796 Speaker 1: the narrative is actually about. So you're not looking for 379 00:20:17,996 --> 00:20:21,156 Speaker 1: content related to a particular topic. You're looking for particular 380 00:20:21,236 --> 00:20:24,916 Speaker 1: dissemination patterns. So you're looking at the authenticity of the accounts. 381 00:20:24,916 --> 00:20:28,236 Speaker 1: Are these real accounts where they all created yesterday? Are 382 00:20:28,236 --> 00:20:31,556 Speaker 1: they bots? Are they majority automated? Are they Twitter does 383 00:20:31,636 --> 00:20:34,116 Speaker 1: have now a designation of something that considers a low 384 00:20:34,196 --> 00:20:38,476 Speaker 1: quality account ways in which it surfaces top tweets, as 385 00:20:38,476 --> 00:20:42,316 Speaker 1: opposed to just the straight up reverse chronological order where 386 00:20:42,356 --> 00:20:45,396 Speaker 1: you see every single tweet about a particular hashtag, giving 387 00:20:45,436 --> 00:20:47,916 Speaker 1: the user some control. So people who do want to 388 00:20:47,956 --> 00:20:50,756 Speaker 1: go see that kind of fire hose of every single 389 00:20:51,116 --> 00:20:53,476 Speaker 1: tweet coming through about a topic can go and do that. 390 00:20:53,636 --> 00:20:56,116 Speaker 1: But the majority of people who just want to get 391 00:20:56,156 --> 00:20:59,356 Speaker 1: the kind of quick takeaways are seeing more kind of 392 00:20:59,436 --> 00:21:02,716 Speaker 1: higher caliber content. And that sounds like you do believe 393 00:21:02,836 --> 00:21:08,796 Speaker 1: algorithms could eventually identify quality content that they could encompass 394 00:21:08,956 --> 00:21:13,556 Speaker 1: a notion of better or more comprehensive or more fact based. 395 00:21:14,156 --> 00:21:17,276 Speaker 1: Remember the olden days of the Internet where you had 396 00:21:17,276 --> 00:21:20,756 Speaker 1: email spam, right, we did build classifiers, We did build 397 00:21:20,956 --> 00:21:24,876 Speaker 1: tools to think about how to ensure that crap wasn't 398 00:21:24,876 --> 00:21:29,316 Speaker 1: flooding people's inboxes, that there wasn't this mass cognitive load 399 00:21:29,436 --> 00:21:31,876 Speaker 1: every time you opened your inbox of having to sift 400 00:21:31,916 --> 00:21:34,716 Speaker 1: through all of the garbage to find the communications from 401 00:21:34,756 --> 00:21:37,196 Speaker 1: people that you actually wanted or or find the things 402 00:21:37,196 --> 00:21:39,316 Speaker 1: that were really intended for you. We need to put 403 00:21:39,316 --> 00:21:42,516 Speaker 1: some things in place here to improve the system, to 404 00:21:42,556 --> 00:21:45,836 Speaker 1: improve the user experience, to improve the outcomes. There were 405 00:21:45,996 --> 00:21:50,676 Speaker 1: things like recognizing that certain domains were just not reputable 406 00:21:50,716 --> 00:21:52,916 Speaker 1: domains that most people wanted in their inbox, and so 407 00:21:52,956 --> 00:21:55,196 Speaker 1: some of this was user filtering, you know, feedback. You 408 00:21:55,196 --> 00:21:56,876 Speaker 1: remember you used to kind of mark things as spam 409 00:21:56,956 --> 00:21:59,116 Speaker 1: much more regularly then. It didn't mean that there were 410 00:21:59,156 --> 00:22:02,156 Speaker 1: never false positives. There are still false positives today, But 411 00:22:02,196 --> 00:22:06,596 Speaker 1: it was how can we create greatest value while at 412 00:22:06,596 --> 00:22:11,356 Speaker 1: the same time recognizing that there are extremely coordinated, deliberate 413 00:22:11,396 --> 00:22:14,556 Speaker 1: groups of people working to manipulate and evade that detection, 414 00:22:14,956 --> 00:22:16,716 Speaker 1: in the case of spam, to wind up in your 415 00:22:16,716 --> 00:22:19,316 Speaker 1: inbox and in the case of social algorithmic manipulation to 416 00:22:19,356 --> 00:22:21,996 Speaker 1: wind up in your feed People who are concerned about 417 00:22:21,996 --> 00:22:24,956 Speaker 1: this problem, people who worry about online disinformation, people who 418 00:22:24,956 --> 00:22:28,236 Speaker 1: worry they're getting bad information. Is there anything they can 419 00:22:28,276 --> 00:22:30,836 Speaker 1: do about it? Is there something that ordinary people can 420 00:22:30,876 --> 00:22:33,916 Speaker 1: do to fight back? Stopping this spread a lot of 421 00:22:33,916 --> 00:22:36,676 Speaker 1: the time is something where individuals really have a lot 422 00:22:36,676 --> 00:22:39,636 Speaker 1: of power. It's been for a long time, you know, 423 00:22:39,756 --> 00:22:41,716 Speaker 1: kind of a cultural norm where if you see someone 424 00:22:41,876 --> 00:22:44,076 Speaker 1: sharing something a little bit nutty to just kind of 425 00:22:44,116 --> 00:22:46,636 Speaker 1: ignore it, just let it go by. I don't think 426 00:22:46,676 --> 00:22:49,956 Speaker 1: that that's necessarily really helped us. I've tried lately to try, 427 00:22:49,996 --> 00:22:52,756 Speaker 1: like commenting gently or sending a private message saying hey, 428 00:22:52,796 --> 00:22:55,556 Speaker 1: I don't think this is necessarily the most reputable source. 429 00:22:55,716 --> 00:22:58,316 Speaker 1: Maybe you know, here's a fact check on that. There's 430 00:22:58,316 --> 00:23:01,916 Speaker 1: a lot of evidence that says that interventions from people, 431 00:23:02,396 --> 00:23:05,356 Speaker 1: you know, even in the kind of counter radicalization space, 432 00:23:05,436 --> 00:23:09,076 Speaker 1: that really engagement with friends and family and people were 433 00:23:09,156 --> 00:23:11,516 Speaker 1: there's a base of trust and an assumption of goodwill. 434 00:23:12,036 --> 00:23:15,796 Speaker 1: People are receptive to rethinking maybe why they chose to 435 00:23:15,836 --> 00:23:18,316 Speaker 1: share something. And then when you see something that makes 436 00:23:18,316 --> 00:23:20,276 Speaker 1: you feel highly emotional and you go to click the 437 00:23:20,276 --> 00:23:23,036 Speaker 1: share button or the retweet button just because you know, 438 00:23:23,196 --> 00:23:25,116 Speaker 1: you feel outraged and you need to tell the world, 439 00:23:25,436 --> 00:23:28,156 Speaker 1: that's where I think taking the extra second to stop 440 00:23:28,156 --> 00:23:30,276 Speaker 1: and do the fact check, to stop and see is 441 00:23:30,316 --> 00:23:33,436 Speaker 1: this a reputable domain or a reputable account, it really 442 00:23:33,436 --> 00:23:37,236 Speaker 1: makes a difference. So friends, don't let friends share disinformation, 443 00:23:37,516 --> 00:23:41,836 Speaker 1: and always check whose account you're retweeting or reposting before 444 00:23:41,836 --> 00:23:43,876 Speaker 1: you do it. Yeah, I mean, I've made this mistake 445 00:23:43,876 --> 00:23:46,676 Speaker 1: a couple of times. I remember I once retweeted something 446 00:23:46,716 --> 00:23:48,876 Speaker 1: and a friend of mine ping me and said, hey, 447 00:23:48,916 --> 00:23:50,756 Speaker 1: I think you should go read the rest of that 448 00:23:50,796 --> 00:23:54,236 Speaker 1: accounts tweets, And I went and looked, and I ninety 449 00:23:54,316 --> 00:23:57,276 Speaker 1: nine percent sure it was a bot, and I was like, oh, 450 00:23:57,316 --> 00:24:01,516 Speaker 1: I fell for it, you know so. But that's the 451 00:24:01,596 --> 00:24:04,036 Speaker 1: kind of thing where it's far better to tell somebody. 452 00:24:04,076 --> 00:24:05,876 Speaker 1: I mean, you can just unretweet, you just click the 453 00:24:05,916 --> 00:24:08,156 Speaker 1: button again. And it's more challenging if you are a 454 00:24:08,156 --> 00:24:10,476 Speaker 1: person with a very, very large following, and it usually 455 00:24:10,476 --> 00:24:12,516 Speaker 1: helps to send a follow up or something and say, hey, 456 00:24:12,556 --> 00:24:15,836 Speaker 1: I inadvertently spread some misinformation. It's come to my attention 457 00:24:15,876 --> 00:24:19,076 Speaker 1: that this is not real, or here's the actual story. 458 00:24:19,796 --> 00:24:23,756 Speaker 1: It's so wild to hear about these disinformation campaigns online 459 00:24:23,836 --> 00:24:26,276 Speaker 1: right now because here in the US there have been 460 00:24:26,796 --> 00:24:30,876 Speaker 1: eight hundred and eighteen measles cases reported in this year's outbreak. 461 00:24:31,276 --> 00:24:34,476 Speaker 1: It's already the largest since nineteen ninety four. People are 462 00:24:34,476 --> 00:24:38,756 Speaker 1: in hospital here because of misinformation, and New York is 463 00:24:38,756 --> 00:24:43,316 Speaker 1: seeing the fastest spread, particularly in Orthodox Jewish communities. The 464 00:24:43,356 --> 00:24:46,796 Speaker 1: thing is that in that specific case, the misinformation about 465 00:24:46,876 --> 00:24:50,716 Speaker 1: vaccines was not spread online, but through physical handbooks and 466 00:24:50,876 --> 00:24:55,876 Speaker 1: phone conferences. The internet amplifies what we already do, so 467 00:24:56,316 --> 00:25:00,676 Speaker 1: changing algorithms and policies and our own behavior online. It's 468 00:25:00,676 --> 00:25:03,276 Speaker 1: all going to take a lot of changing, and I'm 469 00:25:03,276 --> 00:25:06,836 Speaker 1: really grateful to people like Renee who work towards that 470 00:25:06,996 --> 00:25:14,716 Speaker 1: every day. Solvable is a collaboration between Pushkin Industries and 471 00:25:14,756 --> 00:25:19,316 Speaker 1: the Rockefella Foundation, with production by Chalk and Blade. Pushkin's 472 00:25:19,316 --> 00:25:23,756 Speaker 1: executive producer is Mia LaBelle. Engineering by Jason Gambrell and 473 00:25:23,796 --> 00:25:28,236 Speaker 1: the fine folks at GSI Studios. Original music composed by 474 00:25:28,276 --> 00:25:33,276 Speaker 1: Pascal Wise. Special thanks to Maggie Taylor, Heather Fame, Julia Barton, 475 00:25:33,516 --> 00:25:38,676 Speaker 1: Carlie Migliori, Sherif Vincent, Jacob Weisberg, and Malcolm Gladwell. You 476 00:25:38,716 --> 00:25:42,476 Speaker 1: can learn more about solving today's biggest problems at Rockefella 477 00:25:42,556 --> 00:25:47,396 Speaker 1: Foundation dot org, slash solvable. I'm Mave Higgins. Now go 478 00:25:47,716 --> 00:25:48,156 Speaker 1: solve it.