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