1 00:00:03,400 --> 00:00:06,440 Speaker 1: You're listening to Disinformed, a mini series from There Are 2 00:00:06,440 --> 00:00:13,280 Speaker 1: No Girls on the Internet. I'm Bridget Todd. So if 3 00:00:13,280 --> 00:00:15,480 Speaker 1: you listen to this week's episode of Disinformed, it featured 4 00:00:15,480 --> 00:00:17,919 Speaker 1: a conversation with Sharine Mitchell, who was pretty much the 5 00:00:18,000 --> 00:00:21,360 Speaker 1: o G of researching women of color and their experiences online, 6 00:00:21,680 --> 00:00:25,160 Speaker 1: including things like disinformation and online harassment. So in the episode, 7 00:00:25,280 --> 00:00:27,080 Speaker 1: we talked about how black women were some of the 8 00:00:27,120 --> 00:00:29,720 Speaker 1: earliest people to raise the alarm about the threat posed 9 00:00:29,720 --> 00:00:33,000 Speaker 1: by disinformation, and we name checked Shafika Hudson a few 10 00:00:33,000 --> 00:00:35,279 Speaker 1: times as someone who did this years before a lot 11 00:00:35,320 --> 00:00:38,920 Speaker 1: of people were really talking seriously about disinformation. Here's a 12 00:00:38,920 --> 00:00:42,720 Speaker 1: clip you and I know about women like Safika Huston 13 00:00:43,280 --> 00:00:49,080 Speaker 1: and Anissa Crockett who basically helped identify you know, you know, 14 00:00:49,360 --> 00:00:53,000 Speaker 1: uh fake accounts pretending to be black women. It's looking 15 00:00:53,040 --> 00:00:55,760 Speaker 1: at the way in which people of color, black people 16 00:00:55,800 --> 00:01:01,040 Speaker 1: specifically are trying to change their their environment. It trying 17 00:01:01,080 --> 00:01:04,440 Speaker 1: to stop the harassment, trying to stop being killed by 18 00:01:04,480 --> 00:01:07,920 Speaker 1: the state, trying to stop all these things are happening 19 00:01:07,920 --> 00:01:11,080 Speaker 1: to them in their communities, and they're seen as the 20 00:01:11,160 --> 00:01:16,240 Speaker 1: problem of which they should not be protected. Shafica story 21 00:01:16,280 --> 00:01:18,520 Speaker 1: is a really fascinating look at what happens when social 22 00:01:18,520 --> 00:01:21,240 Speaker 1: media platforms don't listen to their users, and it turns 23 00:01:21,280 --> 00:01:23,720 Speaker 1: out the stakes are actually really high, not just for 24 00:01:23,760 --> 00:01:27,039 Speaker 1: black women, for everybody. On this classic episode of There 25 00:01:27,040 --> 00:01:29,720 Speaker 1: Are No Girls on the Internet from July, I spoke 26 00:01:29,760 --> 00:01:32,839 Speaker 1: to Shafika about uncovering an army of bad actors trying 27 00:01:32,880 --> 00:01:35,520 Speaker 1: to use social media to so chaos and division. When 28 00:01:35,560 --> 00:01:38,520 Speaker 1: the powers that be did nothing, Shafika did what women 29 00:01:38,560 --> 00:01:41,200 Speaker 1: always do. She rolled up her sleeves and tried to 30 00:01:41,200 --> 00:01:44,440 Speaker 1: stop them herself. Let's listen in to how black women 31 00:01:44,560 --> 00:01:49,360 Speaker 1: tried to save Twitter. Okay, so I could tell you 32 00:01:49,400 --> 00:01:51,920 Speaker 1: this story a hundred times and a hundred different ways. 33 00:01:52,320 --> 00:01:55,840 Speaker 1: People just don't listen to women, especially Black women, and 34 00:01:55,920 --> 00:02:00,000 Speaker 1: it comes with big consequences. Six years ago, Black batminist 35 00:02:00,000 --> 00:02:03,720 Speaker 1: we're experiencing a coordinated pattern of disinformation on Twitter. They 36 00:02:03,720 --> 00:02:07,280 Speaker 1: spoke up, but no one listened. That failure to listen 37 00:02:07,320 --> 00:02:09,800 Speaker 1: to black women have a big impact. It allowed the 38 00:02:09,800 --> 00:02:13,400 Speaker 1: weaponization of online harassment tactics against other marginalized people on 39 00:02:13,440 --> 00:02:18,760 Speaker 1: social media and presents continued threats to our democracy and safety. Okay, 40 00:02:18,760 --> 00:02:20,320 Speaker 1: so let's just get this out of the way right now. 41 00:02:20,560 --> 00:02:23,480 Speaker 1: Twitter is a bucking cess school. If you spent any 42 00:02:23,520 --> 00:02:26,720 Speaker 1: time there, you probably already knew this bad faith, commentary, 43 00:02:26,840 --> 00:02:29,880 Speaker 1: reply guys, trolls, harassment, It can really just be an 44 00:02:29,960 --> 00:02:33,440 Speaker 1: unpleasant place. In May, Twitter announced they would start labeling 45 00:02:33,480 --> 00:02:36,799 Speaker 1: tweets that spread misleading information. But this comes years after 46 00:02:36,840 --> 00:02:39,359 Speaker 1: black feminists raised the alarm about it and were ignored. 47 00:02:40,000 --> 00:02:42,480 Speaker 1: These women weren't just being attacked. They were learning about 48 00:02:42,480 --> 00:02:45,680 Speaker 1: the tactics that bad actors used to infiltrate online communities. 49 00:02:46,080 --> 00:02:48,800 Speaker 1: They spoke up about what they were experiencing online. So 50 00:02:48,840 --> 00:02:51,400 Speaker 1: why did anyone listen? And what might have happened if 51 00:02:51,440 --> 00:03:00,000 Speaker 1: they had should Bega Hudson, freelancer, cat Lady sometimes activists. 52 00:03:01,919 --> 00:03:04,880 Speaker 1: Japika had been using Twitter regularly since almost its very beginning, 53 00:03:05,080 --> 00:03:07,000 Speaker 1: where he spent most of her time online connecting with 54 00:03:07,000 --> 00:03:10,200 Speaker 1: other black feminists. In twenty four teen, while job searching, 55 00:03:10,520 --> 00:03:13,280 Speaker 1: she noticed the hashtag that just did not make sense 56 00:03:14,000 --> 00:03:17,160 Speaker 1: end Father's Day. The people pushing the end fathers say 57 00:03:17,200 --> 00:03:20,519 Speaker 1: hashtag on Twitter appeared to be black feminists. They talked 58 00:03:20,520 --> 00:03:22,799 Speaker 1: about how we should abolished Father's Day because too many 59 00:03:22,800 --> 00:03:25,320 Speaker 1: black men date outside of their race, or because black 60 00:03:25,320 --> 00:03:28,040 Speaker 1: men don't support their children. Stuff that just seemed really 61 00:03:28,080 --> 00:03:31,520 Speaker 1: out there, unless they had like ten different ads open. 62 00:03:31,560 --> 00:03:35,720 Speaker 1: Because I was also like doing a job search and 63 00:03:35,880 --> 00:03:40,200 Speaker 1: just going about my life and it someone tweet caught 64 00:03:40,240 --> 00:03:45,200 Speaker 1: my attention because it was so completely off the wall, 65 00:03:45,280 --> 00:03:48,280 Speaker 1: and I don't know who retweeted it or like how 66 00:03:48,360 --> 00:03:53,400 Speaker 1: it even arrived in my timeline, but it wasn't anything 67 00:03:53,520 --> 00:03:58,080 Speaker 1: that any black feminist anywhere would say. It was like, 68 00:03:58,400 --> 00:04:05,760 Speaker 1: what was it? Oh? Yeah, in Father's day. I wish 69 00:04:05,760 --> 00:04:08,720 Speaker 1: these white women would stop stealing our men some some 70 00:04:08,720 --> 00:04:10,840 Speaker 1: something just completely off the wall. They had nothing to 71 00:04:10,880 --> 00:04:16,480 Speaker 1: do with anything, and the avatar was someone who I 72 00:04:16,480 --> 00:04:20,360 Speaker 1: didn't recognize. Now, the thing about the black feminist community 73 00:04:20,400 --> 00:04:23,159 Speaker 1: on Twitter, the thing about a lot of communities on Twitter, 74 00:04:23,600 --> 00:04:27,480 Speaker 1: is that you might not necessarily get along with everybody, 75 00:04:27,560 --> 00:04:31,080 Speaker 1: but you know who everyone is like, and if you 76 00:04:31,120 --> 00:04:33,880 Speaker 1: haven't met them or seeing them out, or you know, 77 00:04:34,000 --> 00:04:36,680 Speaker 1: done a tweet up, hung out at a party, something 78 00:04:37,120 --> 00:04:41,320 Speaker 1: someone you know has And and this particular instance, I'm 79 00:04:41,360 --> 00:04:45,200 Speaker 1: clicked on the person well the accounts profile. I said, okay, 80 00:04:45,200 --> 00:04:49,480 Speaker 1: who is this? I've never seen this person and it 81 00:04:49,480 --> 00:04:53,400 Speaker 1: looks like they just joined like two days ago, and 82 00:04:54,120 --> 00:04:57,599 Speaker 1: they're just tweeting about this with this hashtag, and they have, 83 00:04:58,360 --> 00:05:00,080 Speaker 1: you know, the photo of a black woman, but it 84 00:05:00,200 --> 00:05:03,880 Speaker 1: just nothing adds up. So that drove me to click 85 00:05:03,920 --> 00:05:06,120 Speaker 1: on the hashtag and father's jay and lo and behold. 86 00:05:07,320 --> 00:05:09,760 Speaker 1: When I did a Twitter search, there's a bunch of 87 00:05:09,800 --> 00:05:15,400 Speaker 1: accounts that are saying things that are completely left, like 88 00:05:15,560 --> 00:05:18,560 Speaker 1: not left, like you know, politically, just kind of left, 89 00:05:18,880 --> 00:05:23,440 Speaker 1: like left, where are you coming from? Left? And I 90 00:05:23,480 --> 00:05:27,280 Speaker 1: didn't recognize any of them, so I at that point 91 00:05:27,320 --> 00:05:30,920 Speaker 1: I just kind of asked the general question from my timeline, 92 00:05:31,000 --> 00:05:33,720 Speaker 1: just say, Okay, you guys, what's going on. I keep 93 00:05:33,760 --> 00:05:36,600 Speaker 1: seeing this hashtag and these accounts that I don't recognize, 94 00:05:36,600 --> 00:05:39,200 Speaker 1: with people who look like they just joined like five 95 00:05:39,240 --> 00:05:42,600 Speaker 1: seconds ago. And someone said, yeah, it looks like this 96 00:05:42,680 --> 00:05:49,800 Speaker 1: is like some kind of fortune thing that That's when 97 00:05:49,839 --> 00:05:53,320 Speaker 1: I really started digging it. I said, Okay, well, this 98 00:05:53,400 --> 00:05:56,719 Speaker 1: is really awful because they're pretending to be black women 99 00:05:56,760 --> 00:06:00,560 Speaker 1: who were saying these awful things, and I'm hard enough 100 00:06:00,720 --> 00:06:06,920 Speaker 1: to know that nothing here that they're saying is even 101 00:06:06,960 --> 00:06:11,440 Speaker 1: remotely what a real black feminist would say. I honestly 102 00:06:11,480 --> 00:06:15,599 Speaker 1: think the people who they fooled immediately we're already probably 103 00:06:15,839 --> 00:06:20,839 Speaker 1: biased against feminist or black women, or some combination of 104 00:06:20,880 --> 00:06:24,000 Speaker 1: the two. I didn't get the impression that they were 105 00:06:24,040 --> 00:06:26,080 Speaker 1: fulling most of the people I followed, is what is 106 00:06:26,120 --> 00:06:28,880 Speaker 1: what I mean? But um, they were getting some reaction. 107 00:06:29,720 --> 00:06:33,560 Speaker 1: That's when Shaffica went from curious to pissed. I got 108 00:06:33,760 --> 00:06:37,880 Speaker 1: so mad, like I remember just being so angry. I 109 00:06:37,880 --> 00:06:43,440 Speaker 1: could feel my cheeks and my ears heat up, like honestly, 110 00:06:43,640 --> 00:06:46,040 Speaker 1: like you know in the in the cartoons where the 111 00:06:46,480 --> 00:06:52,320 Speaker 1: character like start seeming from the coming for your ears 112 00:06:52,400 --> 00:06:59,000 Speaker 1: as here the tea kettle whistle. I was sarious. I 113 00:06:59,000 --> 00:07:00,920 Speaker 1: was like, you know, it's out like we don't get 114 00:07:01,080 --> 00:07:05,320 Speaker 1: enough garbage online just being black women, you know, but 115 00:07:05,440 --> 00:07:08,880 Speaker 1: with people just randomly showing up in our our mentions 116 00:07:09,200 --> 00:07:11,800 Speaker 1: to argue with something that we said, not because they 117 00:07:11,840 --> 00:07:14,200 Speaker 1: necessarily disagree, but because that's what people do when you're 118 00:07:14,240 --> 00:07:19,200 Speaker 1: a black woman online. Apparently, because we don't deal with 119 00:07:19,360 --> 00:07:22,000 Speaker 1: enough out here in real life and online, we don't 120 00:07:22,000 --> 00:07:27,000 Speaker 1: deal with enough. We've got this whole silly operation thing happening. 121 00:07:28,320 --> 00:07:32,240 Speaker 1: So I said, well, let me just go ahead and 122 00:07:32,280 --> 00:07:35,240 Speaker 1: take a look and see what's really going on and 123 00:07:35,280 --> 00:07:38,360 Speaker 1: see how bad this is. And as I began to dig, 124 00:07:38,440 --> 00:07:42,560 Speaker 1: I saw just how bad it was, and I realized 125 00:07:42,560 --> 00:07:45,320 Speaker 1: that I would not be able to point out all 126 00:07:45,400 --> 00:07:48,880 Speaker 1: of these accounts alone. You know how in movies, when 127 00:07:48,880 --> 00:07:51,320 Speaker 1: a character discovers this thing they've been investigating, it's much 128 00:07:51,400 --> 00:07:54,160 Speaker 1: much bigger than they realized. There is no Pepe silvia, 129 00:07:54,200 --> 00:07:56,360 Speaker 1: and this thing goes all the way to the top. Well, 130 00:07:56,360 --> 00:07:59,520 Speaker 1: that's how Shapika felt. She knew the Twitter accounts pushing 131 00:07:59,520 --> 00:08:02,280 Speaker 1: and Bother's Day weren't actually black women. They were just 132 00:08:02,320 --> 00:08:05,680 Speaker 1: impersonating black women, and pretty badly at that. But there 133 00:08:05,680 --> 00:08:07,040 Speaker 1: were too many of them for this to be a 134 00:08:07,040 --> 00:08:10,040 Speaker 1: one off thing. It had to be coordinated, and there 135 00:08:10,040 --> 00:08:12,440 Speaker 1: were also too many for her to tackle alone. She 136 00:08:12,480 --> 00:08:14,760 Speaker 1: wanted to give other black feminists a tool to sniff 137 00:08:14,800 --> 00:08:17,480 Speaker 1: out these impostors, so she fought back with a hashtag 138 00:08:17,480 --> 00:08:20,600 Speaker 1: of her own, your slip is Showing. I went ahead 139 00:08:20,600 --> 00:08:24,040 Speaker 1: with your slip is showing and going to run another 140 00:08:24,720 --> 00:08:26,680 Speaker 1: line like, I don't know your mess scare is running 141 00:08:26,720 --> 00:08:28,400 Speaker 1: something like that, but You're slipp is showing just seemed 142 00:08:28,400 --> 00:08:30,800 Speaker 1: to work. It really just seemed to work. Okay, So 143 00:08:30,840 --> 00:08:32,600 Speaker 1: if you're not a lady from the South. The phrase 144 00:08:32,640 --> 00:08:35,800 Speaker 1: your slip is showing might not mean anything to you. Literally, 145 00:08:35,840 --> 00:08:37,959 Speaker 1: it means when your slip is peeking out from underneath 146 00:08:38,000 --> 00:08:40,720 Speaker 1: your skirt address a big fashion no no, But where 147 00:08:40,760 --> 00:08:44,360 Speaker 1: I come from, that one phrase really highlights us averseness 148 00:08:44,400 --> 00:08:46,720 Speaker 1: of what I'll call auntie speak. Think of it a 149 00:08:46,760 --> 00:08:49,280 Speaker 1: bit like the phrase bless your heart. A lady at 150 00:08:49,320 --> 00:08:51,040 Speaker 1: church might tell you that your slip is peeking out 151 00:08:51,040 --> 00:08:52,800 Speaker 1: from the bottom of your skirt because they care about 152 00:08:52,800 --> 00:08:55,720 Speaker 1: you look at your best. Or they could tell you 153 00:08:55,720 --> 00:08:57,400 Speaker 1: your slip is showing because they don't like you, and 154 00:08:57,400 --> 00:09:00,240 Speaker 1: they're pointing out publicly that you aren't looking at good 155 00:09:00,240 --> 00:09:03,040 Speaker 1: and put together as you think you are. You know, 156 00:09:03,120 --> 00:09:05,079 Speaker 1: just the sort of thing that one of your aunties 157 00:09:05,120 --> 00:09:07,440 Speaker 1: might say to you in church, and oh, honey, you 158 00:09:07,480 --> 00:09:12,000 Speaker 1: need to you need to fix your slip is showing. 159 00:09:12,240 --> 00:09:21,679 Speaker 1: Except mean because right there's a there's a difference between 160 00:09:21,679 --> 00:09:23,400 Speaker 1: a your slip is showing from one of your aunties 161 00:09:23,400 --> 00:09:26,440 Speaker 1: and your slip is showing from somebody who doesn't like you. 162 00:09:28,920 --> 00:09:31,480 Speaker 1: And that was what I was going with it, like, yeah, 163 00:09:31,480 --> 00:09:34,240 Speaker 1: you're slipped is showing. I'm telling you because I was 164 00:09:34,360 --> 00:09:38,400 Speaker 1: raised right, Not because I particularly care about you being embarrassed. 165 00:09:42,320 --> 00:09:45,040 Speaker 1: I love that so much. I love how you kind 166 00:09:45,040 --> 00:09:51,200 Speaker 1: of use this southern auntie expression that we all sort 167 00:09:51,200 --> 00:09:53,280 Speaker 1: of know what it means. What's also funny is that 168 00:09:53,280 --> 00:09:55,880 Speaker 1: I would imagine the people who are impersonating black women 169 00:09:56,240 --> 00:09:59,440 Speaker 1: probably that probably that that nuance probably goes right over 170 00:09:59,480 --> 00:10:02,520 Speaker 1: their head. Yes, that was that also, um one of 171 00:10:02,559 --> 00:10:05,600 Speaker 1: the things that I also delighted in, because of course 172 00:10:05,600 --> 00:10:08,040 Speaker 1: they wouldn't get it, because you'd have to. I mean, 173 00:10:08,160 --> 00:10:11,960 Speaker 1: you have to be somewhat embedded within certain communities to 174 00:10:12,040 --> 00:10:17,920 Speaker 1: pick up on the nuance, and they really weren't. It's 175 00:10:17,960 --> 00:10:20,640 Speaker 1: fitting that we're talking about getting the nuance. That's certain 176 00:10:20,800 --> 00:10:23,760 Speaker 1: something you can't really teach. This would ultimately be the 177 00:10:23,800 --> 00:10:27,280 Speaker 1: undoing of people in persiating black women online, their inability 178 00:10:27,320 --> 00:10:30,440 Speaker 1: to authentically sound like black women. They try to use 179 00:10:30,480 --> 00:10:33,160 Speaker 1: a v or African American in venacular English, but get 180 00:10:33,160 --> 00:10:35,800 Speaker 1: the expressions all wrong in ways that might as well 181 00:10:35,800 --> 00:10:38,400 Speaker 1: be screaming I am a white person pretending to be 182 00:10:38,440 --> 00:10:41,040 Speaker 1: a Black woman. This is where I should probably say that. 183 00:10:41,080 --> 00:10:44,480 Speaker 1: Around the same time, I noticed someone on Twitter using 184 00:10:44,520 --> 00:10:47,880 Speaker 1: my photo and tweeting confusing things about black people. I 185 00:10:47,960 --> 00:10:50,120 Speaker 1: never knew who was behind it or why it was happening, 186 00:10:50,240 --> 00:10:52,160 Speaker 1: but if I had to say, I would say it 187 00:10:52,240 --> 00:10:54,839 Speaker 1: wasn't an actual black woman because the things they were saying, 188 00:10:54,880 --> 00:10:58,319 Speaker 1: we're just so out there, things like I'm gonna be 189 00:10:58,400 --> 00:11:00,640 Speaker 1: voting for Trump because Hillary Clinton is a whack y'all, 190 00:11:01,040 --> 00:11:05,040 Speaker 1: things that just didn't sound right because they're not speakers 191 00:11:05,240 --> 00:11:08,960 Speaker 1: of a a d um they're approximating. The thing that 192 00:11:09,200 --> 00:11:17,640 Speaker 1: like really really really seemed to like immediately point them 193 00:11:17,640 --> 00:11:23,680 Speaker 1: out was this consistent and ability to understand and properly 194 00:11:23,840 --> 00:11:27,640 Speaker 1: use the habitual be. They didn't get it. They did 195 00:11:27,640 --> 00:11:31,960 Speaker 1: not like they would use the habitual be just kind 196 00:11:32,000 --> 00:11:37,439 Speaker 1: of like for the future tens, you know what I mean. 197 00:11:37,480 --> 00:11:39,520 Speaker 1: Like it was terrible, and a lot of the time 198 00:11:39,559 --> 00:11:45,000 Speaker 1: it was just like really obviously racist word salad. Obviously 199 00:11:45,080 --> 00:11:49,480 Speaker 1: racist word salad. I love it my new band name. Ultimately, 200 00:11:49,720 --> 00:11:51,480 Speaker 1: it seemed like the point of End Father's Day was 201 00:11:51,520 --> 00:11:54,120 Speaker 1: to see what kind of discord bad actors organizing on 202 00:11:54,200 --> 00:11:57,800 Speaker 1: message boards like for Shan could sell within feminist online communities, 203 00:11:58,120 --> 00:12:00,640 Speaker 1: and to make actual feminists in our issue you look 204 00:12:00,679 --> 00:12:04,280 Speaker 1: like petty, stupid man haters whose issues were so outlandish, 205 00:12:04,400 --> 00:12:07,480 Speaker 1: they could never be taken seriously. It turns out this 206 00:12:07,520 --> 00:12:12,040 Speaker 1: is actually a pretty common disinformation tactic. Hijacking public conversations 207 00:12:12,040 --> 00:12:15,680 Speaker 1: about sensitive topics or wedge issues through media manipulation is 208 00:12:15,679 --> 00:12:17,800 Speaker 1: a way of making people afraid of having an opinion 209 00:12:17,800 --> 00:12:22,040 Speaker 1: in public and ultimately trying to silence them. I'm Joan 210 00:12:22,160 --> 00:12:25,480 Speaker 1: Donovan and I'm the research director at the Shorenstein Center 211 00:12:25,800 --> 00:12:29,960 Speaker 1: on Media, Politics and Public Policy. Dr Donovan says, the 212 00:12:30,000 --> 00:12:32,400 Speaker 1: same way that brands and politicians realized the power of 213 00:12:32,440 --> 00:12:34,600 Speaker 1: social media, the kind of people who want to harass 214 00:12:34,600 --> 00:12:37,720 Speaker 1: others did do it can have a big impact, especially 215 00:12:37,720 --> 00:12:40,400 Speaker 1: as we're using social media to talk about thorny issues 216 00:12:40,440 --> 00:12:44,200 Speaker 1: like race, gender, and sexuality, issues that require nuance to 217 00:12:44,200 --> 00:12:47,120 Speaker 1: discuss thoughtfully. It makes it tough for anyone to have 218 00:12:47,120 --> 00:12:51,360 Speaker 1: a good faith dialogue online. Over time, just like the 219 00:12:51,400 --> 00:12:55,640 Speaker 1: politicians learned to be used social media, we had white 220 00:12:55,640 --> 00:13:00,200 Speaker 1: supremacists figure out that you don't actually need to show 221 00:13:00,280 --> 00:13:04,560 Speaker 1: up in public to have an impact on people's lives, 222 00:13:05,040 --> 00:13:10,479 Speaker 1: and so we saw networked and coordinated harassment campaigns. Uh. 223 00:13:10,600 --> 00:13:16,080 Speaker 1: It just continue even to this day, continue to be 224 00:13:16,480 --> 00:13:21,240 Speaker 1: useful ways to shut down journalists, to impersonate different groups 225 00:13:21,320 --> 00:13:27,640 Speaker 1: and to really cause uh a fracture in public conversation 226 00:13:27,679 --> 00:13:32,560 Speaker 1: about really important issues that require some level of nuance, 227 00:13:32,679 --> 00:13:36,240 Speaker 1: some level of understanding and and a lot of compassion 228 00:13:36,360 --> 00:13:40,120 Speaker 1: to talk about, you know, especially racism in this moment, 229 00:13:40,200 --> 00:13:42,880 Speaker 1: and people are reticent to talk about it because they're 230 00:13:42,920 --> 00:13:49,000 Speaker 1: afraid of saying something wrong, especially in the environments online 231 00:13:49,080 --> 00:13:52,800 Speaker 1: where if you do make a misstep you could get dragged, 232 00:13:52,840 --> 00:13:56,600 Speaker 1: you could get canceled. But also some of that might 233 00:13:56,679 --> 00:13:59,680 Speaker 1: be artificial. It might be the case that people do 234 00:14:00,000 --> 00:14:01,880 Speaker 1: of the eyes with you, people do want to help 235 00:14:01,920 --> 00:14:05,920 Speaker 1: you grow and learn, but certain medium manipulators see that 236 00:14:05,960 --> 00:14:11,319 Speaker 1: as an opportunity to swarm in and really uh drive 237 00:14:11,400 --> 00:14:14,280 Speaker 1: the which as deep as it can go. A few 238 00:14:14,360 --> 00:14:17,280 Speaker 1: right wing news outlets picked up the hashtag and Father's 239 00:14:17,360 --> 00:14:20,400 Speaker 1: Day and amplified it as a legitimate feminist take. This 240 00:14:20,520 --> 00:14:22,960 Speaker 1: is how Fox News covered it, Like some of these tweets, 241 00:14:23,000 --> 00:14:25,600 Speaker 1: heres from Tasha. She wrote, and everyone knows we only 242 00:14:25,640 --> 00:14:28,520 Speaker 1: need mothers. Why do we even need Father's Day? Fathers 243 00:14:28,520 --> 00:14:31,960 Speaker 1: are useless? Hashtag and come on, oh, come on, just 244 00:14:32,080 --> 00:14:35,280 Speaker 1: you wanted this nasty feminist rhetoric that they're not just 245 00:14:35,320 --> 00:14:37,800 Speaker 1: like interested in ending Father's Day of interesting ending men. 246 00:14:38,000 --> 00:14:41,640 Speaker 1: That's really what they want. But Shafika says, only kind 247 00:14:41,640 --> 00:14:44,160 Speaker 1: of people who were already predisposed to be skeptical of 248 00:14:44,200 --> 00:14:48,440 Speaker 1: women and feminists, especially black women, fell for it. Well, 249 00:14:48,520 --> 00:14:53,400 Speaker 1: it was actually at first, I remember, I was incredulous, Like, honestly, 250 00:14:53,440 --> 00:14:56,120 Speaker 1: I was looking at people like, oh, and Father's Day 251 00:14:56,320 --> 00:15:00,760 Speaker 1: feminists take a terrible turn and radically done. I was like, 252 00:15:00,800 --> 00:15:04,320 Speaker 1: You've got to be kidding. But then I realized that no, 253 00:15:04,480 --> 00:15:08,000 Speaker 1: they were completely serious. And then it dawned on me 254 00:15:08,120 --> 00:15:14,640 Speaker 1: that these were people who could not possibly understand feminism, 255 00:15:14,680 --> 00:15:21,240 Speaker 1: possibly women in general, Black people her too much of 256 00:15:21,320 --> 00:15:26,760 Speaker 1: anything outside of their little Fox News bubble like that. 257 00:15:26,760 --> 00:15:29,240 Speaker 1: That was the impression that I got, Like, basically, if 258 00:15:29,280 --> 00:15:33,440 Speaker 1: you fell for this, it's because you already had a 259 00:15:33,480 --> 00:15:36,880 Speaker 1: certain set of bigotries in place too fall for it. 260 00:15:37,400 --> 00:15:39,880 Speaker 1: What was happening with black women online is much less 261 00:15:39,880 --> 00:15:42,920 Speaker 1: widely known than gamer game. Were angry men coordinated to 262 00:15:42,920 --> 00:15:45,880 Speaker 1: harass progressive voices online who were mostly women in the 263 00:15:45,880 --> 00:15:49,280 Speaker 1: months following and Father's Day. Shafika thinks it was ignored 264 00:15:49,280 --> 00:15:52,240 Speaker 1: because the women who were targeted were black. Not only 265 00:15:52,320 --> 00:15:54,120 Speaker 1: was she helping to create a tool to stamp out 266 00:15:54,120 --> 00:15:57,680 Speaker 1: this kind of disinformation online. She also wanted to document 267 00:15:57,720 --> 00:15:59,880 Speaker 1: that it was happening so it wouldn't go forgotten or 268 00:16:00,080 --> 00:16:06,080 Speaker 1: raised just because it was happening to black women. As um, 269 00:16:06,120 --> 00:16:09,360 Speaker 1: you're probably aware of a lot of us are big 270 00:16:09,440 --> 00:16:14,040 Speaker 1: on what we call receipts, So there are plenty of receipts. 271 00:16:14,040 --> 00:16:16,920 Speaker 1: We've got the screen cabs. You can't even you know, 272 00:16:17,000 --> 00:16:21,800 Speaker 1: delete the tweets because we got it, we got the information. 273 00:16:23,520 --> 00:16:26,120 Speaker 1: But yeah, I mean, that's that's been a big part 274 00:16:26,200 --> 00:16:30,400 Speaker 1: of it for me, and it's frustrating for a lot 275 00:16:30,440 --> 00:16:37,480 Speaker 1: of us to see essentially a history erased. It's particularly 276 00:16:37,480 --> 00:16:41,080 Speaker 1: distressing for me because you know, I'm not I wouldn't 277 00:16:41,080 --> 00:16:43,520 Speaker 1: consider myself a scholar at this particular point. And my 278 00:16:43,640 --> 00:16:50,600 Speaker 1: friend so True who also was absolutely integral with formulating 279 00:16:51,280 --> 00:16:53,480 Speaker 1: you're slippy showing and how it kind of played out 280 00:16:53,480 --> 00:16:58,040 Speaker 1: and became a useful tool. But back when I was 281 00:16:58,120 --> 00:17:00,840 Speaker 1: a scholar, I understood that one of the things that 282 00:17:00,880 --> 00:17:06,119 Speaker 1: people do when they're trying to erase the impact of 283 00:17:06,160 --> 00:17:09,720 Speaker 1: a movement is they kind of still start the leading history. 284 00:17:11,359 --> 00:17:17,399 Speaker 1: It's a huge feature of a racier when people talk 285 00:17:17,520 --> 00:17:20,320 Speaker 1: about your slip is showing. If they talk about it 286 00:17:21,840 --> 00:17:25,159 Speaker 1: or if they mentioned it at all, it's it's weird. 287 00:17:25,280 --> 00:17:29,840 Speaker 1: It kind of gets vaguely mentioned in relation to gamer 288 00:17:29,920 --> 00:17:32,720 Speaker 1: Gate as this weird thing that sort of happened before 289 00:17:32,720 --> 00:17:36,400 Speaker 1: gamer Gate that wasn't really relevant and didn't provide anybody 290 00:17:36,400 --> 00:17:39,720 Speaker 1: any tools or you know, it was, you know, just 291 00:17:39,800 --> 00:17:44,800 Speaker 1: kind of a blip as opposed to what it was, 292 00:17:44,920 --> 00:17:53,439 Speaker 1: which was a scary peek into the future. And again, 293 00:17:53,480 --> 00:17:56,720 Speaker 1: like I said, hindsight being once when you start to 294 00:17:57,119 --> 00:18:01,520 Speaker 1: look back on all of these four chances offer I'm sorry, 295 00:18:01,560 --> 00:18:06,399 Speaker 1: I can't say four chan without making that noise. You 296 00:18:06,440 --> 00:18:09,840 Speaker 1: have a specialise too, I do. Oh my gosh. Someone 297 00:18:09,880 --> 00:18:11,680 Speaker 1: else pointed out to me. It's like, do you realize 298 00:18:11,680 --> 00:18:14,159 Speaker 1: that you're just kind of just disgusted noise? What I 299 00:18:14,200 --> 00:18:21,720 Speaker 1: mean you say fortunate. I'm just sorry, it's automatic. I'm 300 00:18:21,760 --> 00:18:27,000 Speaker 1: working on it. When you try to kind of understand 301 00:18:28,680 --> 00:18:32,840 Speaker 1: how everything happened, you have to take all of it 302 00:18:33,480 --> 00:18:38,320 Speaker 1: into account. I really think that in Father's day and um, 303 00:18:38,359 --> 00:18:41,479 Speaker 1: you know, consequently you're you're slippy showing. We're a huge 304 00:18:42,400 --> 00:18:45,000 Speaker 1: part of it, and it's it can be frustrating to 305 00:18:45,119 --> 00:18:47,840 Speaker 1: see it left out of the history because it's like, Okay, 306 00:18:47,880 --> 00:18:53,680 Speaker 1: you're missing a really relevant chunk of understanding how all 307 00:18:53,720 --> 00:18:57,040 Speaker 1: of this mess happened even at a time where we're 308 00:18:57,040 --> 00:19:00,639 Speaker 1: having a conversations around women's experiences online and why do 309 00:19:00,760 --> 00:19:03,440 Speaker 1: you think your slip is showing and End Father's Day 310 00:19:03,520 --> 00:19:05,199 Speaker 1: and the way that women in folks of color have 311 00:19:05,200 --> 00:19:07,760 Speaker 1: been harassed online pretty much goes overlooked. Why do you 312 00:19:07,800 --> 00:19:11,600 Speaker 1: think that is not? Yeah? That and again it's frustrating, 313 00:19:11,640 --> 00:19:15,960 Speaker 1: and my theory remains it's because the targeted group at 314 00:19:16,000 --> 00:19:20,080 Speaker 1: the time for the End Father's Day of four Chan 315 00:19:20,200 --> 00:19:27,359 Speaker 1: operation were black women. It's honestly, that's that's my That's 316 00:19:28,400 --> 00:19:30,240 Speaker 1: I have no other answer at this point. Is the 317 00:19:30,359 --> 00:19:34,359 Speaker 1: six years I've watched this just kind of repeatedly happened. 318 00:19:35,119 --> 00:19:40,760 Speaker 1: And the only answer, unfortunately, that I have, is that, Okay, well, 319 00:19:41,040 --> 00:19:46,800 Speaker 1: this is being largely ignored and great because of who 320 00:19:46,840 --> 00:19:51,720 Speaker 1: the targets were, and the targets were of black women, 321 00:19:52,000 --> 00:19:56,080 Speaker 1: particularly black feminists. We'll be right back after this quick break. 322 00:20:06,760 --> 00:20:09,800 Speaker 1: People who are traditionally marginalized online, like black women, are 323 00:20:09,840 --> 00:20:13,320 Speaker 1: specifically impacted by things like disinformation and harassment on social 324 00:20:13,320 --> 00:20:16,360 Speaker 1: media the ultimate goal is to freak them out so 325 00:20:16,440 --> 00:20:18,760 Speaker 1: much that they'll shut down their social media and just 326 00:20:19,000 --> 00:20:23,840 Speaker 1: stop talking. Here's Dr Donovan again. Yeah, so we have 327 00:20:23,920 --> 00:20:27,520 Speaker 1: to remember that a lot of the ways in which 328 00:20:28,240 --> 00:20:34,399 Speaker 1: disinformation is carried through networks are also related to the 329 00:20:34,440 --> 00:20:39,200 Speaker 1: ways in which people are harassed online. You know. So, 330 00:20:39,240 --> 00:20:44,520 Speaker 1: if you're there's a concept called gender trolling, it's evolved 331 00:20:44,520 --> 00:20:50,359 Speaker 1: into trans trolling, race trolling, UM, queer trolling, where the 332 00:20:50,480 --> 00:20:56,080 Speaker 1: characteristics of your identity become the thing that they focus on, 333 00:20:56,359 --> 00:21:00,800 Speaker 1: and they'll, you know, they'll be a swarm of folks 334 00:21:00,520 --> 00:21:03,480 Speaker 1: uh that have coordinated in some other place, usually on 335 00:21:03,520 --> 00:21:08,680 Speaker 1: a message board, UH, and they will target specific public 336 00:21:08,760 --> 00:21:16,160 Speaker 1: figures or women, or transfolkes or prominent uh black activists 337 00:21:16,520 --> 00:21:19,199 Speaker 1: in order to get them to shut off their social media. 338 00:21:19,560 --> 00:21:25,159 Speaker 1: And they will use all kinds of horrendous images and 339 00:21:25,400 --> 00:21:29,240 Speaker 1: threats to try to get you to feel fear and 340 00:21:29,320 --> 00:21:33,040 Speaker 1: to shut it down. And we don't see that same 341 00:21:33,160 --> 00:21:37,520 Speaker 1: kind of level of threat making when it comes to 342 00:21:37,760 --> 00:21:42,080 Speaker 1: trolling male candidates, And that has to do with the 343 00:21:42,160 --> 00:21:48,000 Speaker 1: characteristics of the harassers themselves, which often see the harassment 344 00:21:48,119 --> 00:21:52,280 Speaker 1: as a form of activism and as a form of 345 00:21:52,520 --> 00:21:57,679 Speaker 1: defending themselves or defending their piece of the culture, and 346 00:21:57,720 --> 00:22:01,600 Speaker 1: so a lot of these people tend to the misogynists 347 00:22:01,640 --> 00:22:08,160 Speaker 1: as well as racists, and in their smaller online communities 348 00:22:08,200 --> 00:22:12,480 Speaker 1: where they don't think they're watch they'll talk openly about that, 349 00:22:12,760 --> 00:22:16,159 Speaker 1: and they'll talk openly about who they should target and 350 00:22:16,400 --> 00:22:19,959 Speaker 1: why and what the problem is. And I think at 351 00:22:20,000 --> 00:22:23,880 Speaker 1: this stage we've been through this enough to know it's 352 00:22:23,880 --> 00:22:29,680 Speaker 1: a serious problem, but it still happens every day, and 353 00:22:30,119 --> 00:22:33,200 Speaker 1: especially at this moment, we're seeing an incredible amount of 354 00:22:34,160 --> 00:22:40,280 Speaker 1: trolling around, you know, anti black racism. And the responsibility though, 355 00:22:41,040 --> 00:22:45,760 Speaker 1: for dealing with this lies with the platform companies first 356 00:22:45,760 --> 00:22:50,040 Speaker 1: and foremost. Twitter CEO Jack Dorsey hasn't always been the 357 00:22:50,080 --> 00:22:52,919 Speaker 1: most responsive to the misuse of the platform. You'd think 358 00:22:52,960 --> 00:22:55,240 Speaker 1: you'd be more concerned, but Chefinka says that wasn't the case. 359 00:22:55,640 --> 00:22:57,840 Speaker 1: Shee and the other black women targeted were pretty much 360 00:22:57,880 --> 00:23:02,840 Speaker 1: left on their own to figure it out. So did 361 00:23:02,840 --> 00:23:05,399 Speaker 1: the powers that be at Twitter or any other social 362 00:23:05,400 --> 00:23:10,400 Speaker 1: media company or any other official do anything to fix this? No, um, 363 00:23:10,480 --> 00:23:12,840 Speaker 1: but that the short answer there is no now. The 364 00:23:12,920 --> 00:23:18,000 Speaker 1: longer explanation is that we repeatedly brought this whole thing 365 00:23:18,119 --> 00:23:23,440 Speaker 1: to the attention of Twitter support. UM to Jack directly, 366 00:23:24,119 --> 00:23:27,399 Speaker 1: it's not like nobody knew what was happening and it 367 00:23:27,600 --> 00:23:32,320 Speaker 1: made the news, like, so it's not It's not as 368 00:23:32,320 --> 00:23:36,199 Speaker 1: though um he was ignorant. The general impression that I 369 00:23:36,240 --> 00:23:39,959 Speaker 1: got from Twitter support was that, oh, well, you know 370 00:23:40,119 --> 00:23:42,920 Speaker 1: this is We're so sorry, are our hands are tied 371 00:23:42,960 --> 00:23:46,440 Speaker 1: and blah blah blah. And I started looking into the 372 00:23:46,480 --> 00:23:49,000 Speaker 1: tech side of everything, and I realized that that wasn't 373 00:23:49,040 --> 00:23:55,160 Speaker 1: the truth. They absolutely have and had tools on hand 374 00:23:57,240 --> 00:24:00,960 Speaker 1: to stop this, and they just didn't. They just let 375 00:24:00,960 --> 00:24:03,040 Speaker 1: it happen, and they just let us clean up the 376 00:24:03,040 --> 00:24:06,720 Speaker 1: mess and defend our communities ourselves. As much as being 377 00:24:06,800 --> 00:24:09,280 Speaker 1: left to fend for her own community online stocked, it 378 00:24:09,359 --> 00:24:11,959 Speaker 1: did teach Affika that her online community could do a 379 00:24:11,960 --> 00:24:16,280 Speaker 1: lot with a little and while that wasn't cool at all, 380 00:24:17,440 --> 00:24:21,239 Speaker 1: and hopefully at some point in in you know, at 381 00:24:21,280 --> 00:24:24,680 Speaker 1: some point down the line, they will be sufficiently shamed 382 00:24:24,760 --> 00:24:28,840 Speaker 1: for it, because it was just really awful. We learned 383 00:24:29,040 --> 00:24:32,920 Speaker 1: what we could do on the ground with just of 384 00:24:32,920 --> 00:24:39,320 Speaker 1: of the very basic tool of like community organization and 385 00:24:39,400 --> 00:24:42,919 Speaker 1: a hashtag, we were able to do a whole lot 386 00:24:43,560 --> 00:24:47,920 Speaker 1: to just stop something that could have gotten way out 387 00:24:47,920 --> 00:24:51,320 Speaker 1: of hand. We outed it early, and we ended it early. 388 00:24:52,240 --> 00:24:56,520 Speaker 1: And if something had been done to make sure that 389 00:24:56,560 --> 00:25:00,520 Speaker 1: these fake accounts that we were reporting had been taking 390 00:25:00,520 --> 00:25:05,919 Speaker 1: out a commission, there would have been a lot less 391 00:25:05,960 --> 00:25:10,040 Speaker 1: for game or Gate to work with. They wouldn't have 392 00:25:10,040 --> 00:25:12,440 Speaker 1: had to they wouldn't have had the opportunity to just 393 00:25:12,480 --> 00:25:16,320 Speaker 1: go ahead and access those same tools that they'd already created. 394 00:25:16,960 --> 00:25:19,360 Speaker 1: So in a kind of way, it sounds like your 395 00:25:19,440 --> 00:25:23,720 Speaker 1: work with your slip is showing and your work organizing 396 00:25:23,760 --> 00:25:27,639 Speaker 1: community responses online was kind of this Canarian coal mine, 397 00:25:27,960 --> 00:25:31,240 Speaker 1: and you all did all that you could to prevent this, 398 00:25:31,520 --> 00:25:34,280 Speaker 1: to stamp this out. But if only the powers that 399 00:25:34,400 --> 00:25:38,440 Speaker 1: be at Twitter or elsewhere had done anything, then it 400 00:25:38,560 --> 00:25:41,080 Speaker 1: might not be the sort of wide scale situation that 401 00:25:41,160 --> 00:25:43,600 Speaker 1: it is now. That is exactly correct, and that I 402 00:25:44,000 --> 00:25:48,320 Speaker 1: know that sounds damning, but that's accurate. They could have 403 00:25:48,320 --> 00:25:55,000 Speaker 1: stopped it. They could still stop it. But the reason why, unfortunately, 404 00:25:55,040 --> 00:25:58,159 Speaker 1: and this was absolutely pointed out by people at the time, 405 00:25:58,800 --> 00:26:03,040 Speaker 1: um and people later taking a look the whole situation 406 00:26:03,160 --> 00:26:07,560 Speaker 1: from like, you know, the whole postmortem of the whole incident. 407 00:26:09,280 --> 00:26:15,440 Speaker 1: The reason why they didn't is because of the profit 408 00:26:15,520 --> 00:26:22,320 Speaker 1: model at the time was based on number of accounts 409 00:26:23,280 --> 00:26:30,000 Speaker 1: and interaction. So you know that when you're selling your 410 00:26:30,040 --> 00:26:34,280 Speaker 1: product basically too were and we're the product to advertisers 411 00:26:34,320 --> 00:26:37,439 Speaker 1: and whatever have you. The more users it looks like 412 00:26:37,480 --> 00:26:41,600 Speaker 1: you have, the better. So it really wasn't in Twitter's 413 00:26:41,640 --> 00:26:47,200 Speaker 1: best interests to say, okay, well, we have twenty accounts 414 00:26:47,240 --> 00:26:52,680 Speaker 1: with one IP address. That's suspicious and we should look 415 00:26:52,720 --> 00:26:56,160 Speaker 1: into it. And that's why they didn't. They didn't took 416 00:26:56,160 --> 00:26:59,840 Speaker 1: them a full two and a half years, I think, 417 00:27:00,160 --> 00:27:02,520 Speaker 1: uh to even really address it in a serious way. 418 00:27:02,520 --> 00:27:06,440 Speaker 1: And I think that was only after the whole congressional 419 00:27:08,840 --> 00:27:12,400 Speaker 1: me Like, I'm I'm pretty sure that was after everybody 420 00:27:12,480 --> 00:27:14,600 Speaker 1: who was like the head of social media got called 421 00:27:14,640 --> 00:27:19,760 Speaker 1: in front of Congress. Mm hmm, Like that's what it took. 422 00:27:22,359 --> 00:27:25,760 Speaker 1: So that pretty much brings us to today. Today, Twitter 423 00:27:25,880 --> 00:27:28,560 Speaker 1: leads all other social media platforms and the spread of 424 00:27:28,560 --> 00:27:32,000 Speaker 1: misleading information about coronavirus. According to a study by Oxford 425 00:27:32,040 --> 00:27:35,760 Speaker 1: researchers called Types, Sources and Claims of COVID nineteen misinformation, 426 00:27:36,040 --> 00:27:38,800 Speaker 1: and a study out of Carnegie Mellon found that most 427 00:27:38,800 --> 00:27:42,159 Speaker 1: of the accounts pushing this misleading content are actually convincing 428 00:27:42,200 --> 00:27:45,680 Speaker 1: looking bots using Twitter to prey on people. So division 429 00:27:45,760 --> 00:27:49,120 Speaker 1: and increased polarization. This isn't just an online thing either. 430 00:27:49,600 --> 00:27:53,240 Speaker 1: Kathleen Carly, the director for the Center for Informed Democracy 431 00:27:53,280 --> 00:27:57,800 Speaker 1: and Social Cybersecurity, says increased polarization will have a variety 432 00:27:57,840 --> 00:28:00,840 Speaker 1: of real world consequences and play and things like voting 433 00:28:00,840 --> 00:28:04,400 Speaker 1: behavior and hostility towards ethnic groups. And this summer, as 434 00:28:04,440 --> 00:28:07,119 Speaker 1: Black Lives Matter protests popped up all over the globe, 435 00:28:07,359 --> 00:28:10,359 Speaker 1: Twitter confirmed that multiple accounts posing as Black Lives Matter 436 00:28:10,400 --> 00:28:13,520 Speaker 1: activists were calling for violence and white suburbs. But those 437 00:28:13,520 --> 00:28:16,920 Speaker 1: accounts were actually run by white supremacist groups just posing 438 00:28:16,960 --> 00:28:21,360 Speaker 1: as activists and quote Antifa to cause chaos. Facebook under 439 00:28:21,400 --> 00:28:25,520 Speaker 1: fire again a Senate Intelligence Committee report claiming Russian agents 440 00:28:26,000 --> 00:28:29,560 Speaker 1: use social media sites like Facebook to target African Americans 441 00:28:29,560 --> 00:28:32,800 Speaker 1: in an effort to suppress voter turnout. We already know 442 00:28:32,880 --> 00:28:36,479 Speaker 1: that Russia use social media to interfere with the election, 443 00:28:37,240 --> 00:28:39,280 Speaker 1: and in case you needed a Senate report to confirm 444 00:28:39,320 --> 00:28:41,600 Speaker 1: with black women have been saying all along. A Senate 445 00:28:41,600 --> 00:28:44,960 Speaker 1: Inquiries cited an Oxford University report on Russian interference on 446 00:28:45,000 --> 00:28:48,720 Speaker 1: social media They found that campaigns targeted no single group 447 00:28:48,760 --> 00:28:51,360 Speaker 1: more than African Americans on social media. They posed as 448 00:28:51,440 --> 00:28:54,560 Speaker 1: black people and ran bony black activist groups to influence 449 00:28:54,640 --> 00:28:57,200 Speaker 1: Black voters to either stay home or vote for Trump 450 00:28:57,240 --> 00:29:00,760 Speaker 1: on election Day. The Senate Intelligence report says the posts 451 00:29:00,760 --> 00:29:04,520 Speaker 1: were aimed at making Americans suspicious of each other. Sound familiar. 452 00:29:05,360 --> 00:29:07,720 Speaker 1: These are the very same kind of tactics that black 453 00:29:07,720 --> 00:29:12,240 Speaker 1: women like Shafika were complaining about years earlier. Accounts posing 454 00:29:12,240 --> 00:29:15,440 Speaker 1: as black people and infiltrating our online communities to create 455 00:29:15,520 --> 00:29:18,680 Speaker 1: chaos and distrust. But because the people with power didn't 456 00:29:18,720 --> 00:29:21,200 Speaker 1: really do anything or take it seriously, it kind of 457 00:29:21,240 --> 00:29:24,880 Speaker 1: exposed this massive vulnerability. Think of it as an online 458 00:29:24,920 --> 00:29:28,600 Speaker 1: disinformation test balloon. It showed that these kinds of attacks 459 00:29:28,640 --> 00:29:31,280 Speaker 1: could happen and they'd pretty much go on addressed. Instead 460 00:29:31,320 --> 00:29:33,760 Speaker 1: of identifying and learning to spot tactics used to make 461 00:29:33,800 --> 00:29:36,520 Speaker 1: our social media communities less safe and less stable, the 462 00:29:36,560 --> 00:29:39,480 Speaker 1: powers that be just let it happen again and again 463 00:29:39,640 --> 00:29:42,280 Speaker 1: and again. I asked Shafika if she thinks that if 464 00:29:42,320 --> 00:29:44,400 Speaker 1: someone had listened to black women when they spoke up 465 00:29:44,400 --> 00:29:47,800 Speaker 1: about being targeted online, things might be different. Now it's 466 00:29:47,800 --> 00:29:55,880 Speaker 1: a tough question. For this is always going to be 467 00:29:55,920 --> 00:30:01,960 Speaker 1: a question that kind of hangs in my mind because, Um, 468 00:30:01,960 --> 00:30:06,000 Speaker 1: while I understand that black voters were absolutely targeted, I'm 469 00:30:06,160 --> 00:30:12,080 Speaker 1: not entirely sure that we were fooled, do you know 470 00:30:12,080 --> 00:30:17,680 Speaker 1: what I mean? Right, like honestly, because it seems like 471 00:30:17,720 --> 00:30:20,520 Speaker 1: to me we kind of all got out and voted anyway. 472 00:30:21,080 --> 00:30:23,600 Speaker 1: And it also seems like to me Donald Trump may 473 00:30:23,640 --> 00:30:26,160 Speaker 1: have lost the popular vote by three million votes, but 474 00:30:26,240 --> 00:30:28,360 Speaker 1: that's neither here nor there. I get not if you 475 00:30:28,400 --> 00:30:31,000 Speaker 1: ask himmy didn't he But we don't ask him things 476 00:30:31,040 --> 00:30:34,360 Speaker 1: because we like honest answers. But um, yeah, I mean, 477 00:30:35,000 --> 00:30:41,600 Speaker 1: just the fact that this happened, like it left us 478 00:30:41,760 --> 00:30:48,360 Speaker 1: arguably vulnerable, and that even even though I'm not sure 479 00:30:48,480 --> 00:30:52,120 Speaker 1: how how ultimately success successful, it was just the fact 480 00:30:52,160 --> 00:30:57,600 Speaker 1: that we had foreign agents targeting voting populations in the 481 00:30:57,680 --> 00:31:02,840 Speaker 1: United States of America should have been serious and do 482 00:31:03,160 --> 00:31:07,880 Speaker 1: cause for alarm, because even if it doesn't work, it's 483 00:31:07,920 --> 00:31:10,120 Speaker 1: like just it's just the fact that they tried and 484 00:31:10,160 --> 00:31:13,320 Speaker 1: that they could. What do you do it? Like, can 485 00:31:13,320 --> 00:31:18,120 Speaker 1: we get it together? It's because we left we left 486 00:31:18,120 --> 00:31:21,800 Speaker 1: a door open like this this was that was a 487 00:31:21,840 --> 00:31:25,080 Speaker 1: failure that was. I don't want to say it was 488 00:31:25,120 --> 00:31:27,280 Speaker 1: on me because I feel like it definitely wasn't on me, 489 00:31:27,280 --> 00:31:30,440 Speaker 1: and it definitely wasn't on you, but it was. It 490 00:31:30,560 --> 00:31:38,840 Speaker 1: was a failure um on the side of whatever agents 491 00:31:39,120 --> 00:31:43,680 Speaker 1: are supposed to be protecting us. And I guess that 492 00:31:44,080 --> 00:31:48,040 Speaker 1: offers that opens up a lot for speculations like, well, 493 00:31:48,520 --> 00:31:53,240 Speaker 1: you know who's who's looking after us now? But yeah, 494 00:31:53,320 --> 00:31:58,200 Speaker 1: that that was a glaring example of just kind of 495 00:31:58,240 --> 00:32:02,520 Speaker 1: the general failure to address something that did not have 496 00:32:02,600 --> 00:32:05,520 Speaker 1: to get as big as it got. More there are 497 00:32:05,560 --> 00:32:20,040 Speaker 1: no girls on the internet after this quick breakidential election 498 00:32:20,240 --> 00:32:23,920 Speaker 1: is a hundred twelve days away. Digital security experts agree 499 00:32:23,960 --> 00:32:26,560 Speaker 1: that American elections are vulnerable and not enough it's being 500 00:32:26,600 --> 00:32:29,560 Speaker 1: done about it. During Trump's and peachment hearing, Fiona Hill, 501 00:32:30,000 --> 00:32:33,000 Speaker 1: the former National Security Council advisor specializing in Russia and 502 00:32:33,040 --> 00:32:37,120 Speaker 1: European affairs, said, right now Russia's security services on their 503 00:32:37,160 --> 00:32:41,080 Speaker 1: proxies have gained up to repeat their interference in the election. 504 00:32:41,280 --> 00:32:43,719 Speaker 1: We're running out of time to stop them. So what 505 00:32:43,720 --> 00:32:47,400 Speaker 1: do we do? Dr Donovan says, As we get closer 506 00:32:47,440 --> 00:32:52,440 Speaker 1: to the election, we know that all different kinds of 507 00:32:52,480 --> 00:32:56,640 Speaker 1: tactics are going to get utilized, including potentially deep fakes 508 00:32:56,760 --> 00:33:00,880 Speaker 1: or cheap fags like manipulated video, manipulated audio. We're going 509 00:33:00,920 --> 00:33:07,040 Speaker 1: to see uh probably uh clips of people quoted out 510 00:33:07,040 --> 00:33:09,320 Speaker 1: of context. We've seen this happen to Joe Biden a 511 00:33:09,320 --> 00:33:12,160 Speaker 1: few different times. Then of course we've seen gas that 512 00:33:12,240 --> 00:33:16,640 Speaker 1: these done men are completely within context and a problem. Um, 513 00:33:17,520 --> 00:33:20,160 Speaker 1: you know, you can't you can't forget that. Every once 514 00:33:20,200 --> 00:33:21,840 Speaker 1: in a while you're watching it and you're like, this 515 00:33:21,920 --> 00:33:25,120 Speaker 1: is this can't be real and it's totally real. Well, 516 00:33:25,120 --> 00:33:27,920 Speaker 1: what's crazy to me about it is as a researcher, 517 00:33:27,960 --> 00:33:29,680 Speaker 1: you're supposed to be attuned to all of this, but 518 00:33:29,720 --> 00:33:31,880 Speaker 1: I still get fooled here and there. But the last 519 00:33:31,920 --> 00:33:33,720 Speaker 1: thing I'll say about the way in which I think 520 00:33:33,720 --> 00:33:40,120 Speaker 1: platform companies need to better serve our political elections and 521 00:33:40,160 --> 00:33:42,920 Speaker 1: the integrity of elections is that they need to hire 522 00:33:43,040 --> 00:33:48,280 Speaker 1: some serious specialists. They need to hire a whole army 523 00:33:48,360 --> 00:33:52,440 Speaker 1: of librarians to do content curations so that when people 524 00:33:52,480 --> 00:33:56,440 Speaker 1: are looking for information they find things that have been 525 00:33:56,440 --> 00:33:59,320 Speaker 1: fact checked that are true and correct. I think that 526 00:33:59,360 --> 00:34:02,680 Speaker 1: we have a to truth and part of the problem 527 00:34:02,760 --> 00:34:05,800 Speaker 1: is the way in which these algorithms are designed is 528 00:34:05,840 --> 00:34:08,680 Speaker 1: to bring up things that are quote unquote fresh and relevant. 529 00:34:09,239 --> 00:34:12,560 Speaker 1: And the problem with fresh and relevant content is that 530 00:34:12,640 --> 00:34:18,480 Speaker 1: disinformation is usually incredibly popular because there are people trying 531 00:34:18,520 --> 00:34:20,880 Speaker 1: to push it and there are people trying to dispute it, 532 00:34:21,239 --> 00:34:24,120 Speaker 1: and so as a result, it rises generally to the 533 00:34:24,120 --> 00:34:28,400 Speaker 1: top of search algorithms or trending algorithms very quickly because 534 00:34:28,480 --> 00:34:32,680 Speaker 1: of that. Uh that feature, But well, Twitter actually do 535 00:34:32,719 --> 00:34:35,960 Speaker 1: any of that. Shapika isn't super confident that the platform 536 00:34:36,000 --> 00:34:39,319 Speaker 1: will do anything at all. I haven't even thought about it, 537 00:34:39,360 --> 00:34:44,200 Speaker 1: and I guess that's sort of a reflection on my 538 00:34:44,280 --> 00:34:49,200 Speaker 1: general skepticism right now with not their ability but their 539 00:34:49,239 --> 00:34:54,040 Speaker 1: willingness to adjust this. I I I have a good 540 00:34:54,080 --> 00:34:57,960 Speaker 1: friend who said one of the smartest things I've ever 541 00:34:58,000 --> 00:35:00,719 Speaker 1: heard anybody say, and I quote it all the time, 542 00:35:01,440 --> 00:35:05,160 Speaker 1: but he said, when things look like they're not working out, 543 00:35:05,520 --> 00:35:07,800 Speaker 1: you can always trust that they're working out for somebody. 544 00:35:09,280 --> 00:35:12,880 Speaker 1: M hmmm, that's I'm going to leave that right there. 545 00:35:15,719 --> 00:35:20,799 Speaker 1: But it looks like you know things aren't working, you 546 00:35:20,840 --> 00:35:27,680 Speaker 1: start asking questions and as a whole rabbit hole. That's 547 00:35:27,719 --> 00:35:30,439 Speaker 1: the thing about the Internet. There's so much darkness lurking 548 00:35:30,440 --> 00:35:33,400 Speaker 1: in its corners just waiting to spill out. But or 549 00:35:33,440 --> 00:35:37,360 Speaker 1: there's darkness, there's light to where there's someone being ugly online, 550 00:35:37,640 --> 00:35:39,880 Speaker 1: there's someone else reaching out to make a genuine connection. 551 00:35:40,560 --> 00:35:43,279 Speaker 1: There's real community to be built and laughs to be had, 552 00:35:43,760 --> 00:35:46,080 Speaker 1: the kind of laughs that can sustain you through difficult times. 553 00:35:47,160 --> 00:35:50,360 Speaker 1: Being online is a constant tight rope walk of acknowledging 554 00:35:50,400 --> 00:35:52,720 Speaker 1: that darkness while still being able to see the corners 555 00:35:52,719 --> 00:35:56,520 Speaker 1: of light peeking through, and even while waiting through all 556 00:35:56,560 --> 00:35:59,680 Speaker 1: of that darkness and ugliness, it's the light that's really 557 00:35:59,719 --> 00:36:03,879 Speaker 1: sis A Shafika. After everything she's been through, she's still 558 00:36:03,920 --> 00:36:06,120 Speaker 1: grateful for Twitter as a platform and all the good 559 00:36:06,160 --> 00:36:09,880 Speaker 1: things that's brought to her life. Honestly, it really helps 560 00:36:10,360 --> 00:36:16,840 Speaker 1: that I have as strong and supportive community, both online 561 00:36:16,880 --> 00:36:22,400 Speaker 1: and off. I've really a super grateful for Twitter for 562 00:36:22,480 --> 00:36:25,200 Speaker 1: so many reasons, not the least of which is because 563 00:36:25,400 --> 00:36:30,759 Speaker 1: it's helped me expand my network and I've met amazing 564 00:36:31,800 --> 00:36:35,799 Speaker 1: people and connected people who are like me, people who 565 00:36:35,840 --> 00:36:39,160 Speaker 1: aren't like me, and gotten gotten to know so much 566 00:36:39,200 --> 00:36:43,520 Speaker 1: about them and learn about their lived experiences. And that 567 00:36:43,600 --> 00:36:47,560 Speaker 1: has saved me because it helps me kind of get 568 00:36:47,560 --> 00:36:52,320 Speaker 1: out of my own headspace or likewise, you know, connect 569 00:36:52,400 --> 00:36:57,000 Speaker 1: with people who understand what dred percent where I'm coming from. 570 00:36:57,040 --> 00:37:01,160 Speaker 1: And that's in a world where where you know, we're 571 00:37:01,239 --> 00:37:07,320 Speaker 1: frequently gas lit about the things that we see and 572 00:37:07,480 --> 00:37:13,160 Speaker 1: experience of That is absolutely invaluable. Oh and one more 573 00:37:13,280 --> 00:37:19,839 Speaker 1: thing that helps. It also helps that I'm funny, honestly, um, 574 00:37:19,880 --> 00:37:23,040 Speaker 1: having a sense of humor and a wit of we'll 575 00:37:23,080 --> 00:37:29,480 Speaker 1: get you through pretty much. I don't want to say 576 00:37:29,480 --> 00:37:31,480 Speaker 1: pretty much anything, but how about this. It's gotten me 577 00:37:31,520 --> 00:37:36,560 Speaker 1: through pretty much everything. And you've been through some stuff 578 00:37:37,320 --> 00:37:45,040 Speaker 1: I've been through. It got a story about an interesting 579 00:37:45,040 --> 00:37:46,959 Speaker 1: thing in tech, or just want to say hi. We'd 580 00:37:47,000 --> 00:37:49,800 Speaker 1: love to hear from you at Hello At tangoi dot com. 581 00:37:49,800 --> 00:37:51,759 Speaker 1: Disinformed is brought to you by There Are No Girls 582 00:37:51,760 --> 00:37:54,080 Speaker 1: on the Internet. It's a production of I Heart Radio 583 00:37:54,120 --> 00:37:57,920 Speaker 1: and Unbossed Creative Jonathan Strickland is our executive producer, Tary 584 00:37:57,920 --> 00:38:01,320 Speaker 1: Harrison is our supervising producer, and in Here Michamato is 585 00:38:01,360 --> 00:38:05,240 Speaker 1: our producer. I'm your host, Bridget Todd. For more great podcasts, 586 00:38:05,280 --> 00:38:07,440 Speaker 1: check out the I Heart Radio app, Apple podcast, or 587 00:38:07,440 --> 00:38:08,600 Speaker 1: wherever you get your podcasts.