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 --> 00:00:13,440 Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Todd and this 3 00:00:13,560 --> 00:00:17,880 Speaker 1: is there Are No Girls on the Internet. I have 4 00:00:17,920 --> 00:00:22,840 Speaker 1: some sad news to share. Shapika Hudson, whose work basically 5 00:00:22,880 --> 00:00:25,800 Speaker 1: created the field of study on how to mobilize against 6 00:00:25,840 --> 00:00:29,320 Speaker 1: bad actors on social media when she created the hashtag 7 00:00:29,680 --> 00:00:33,480 Speaker 1: your slip is Showing in twenty fourteen, tragically passed away 8 00:00:33,560 --> 00:00:36,800 Speaker 1: last week. Now we talk a lot about how the 9 00:00:36,840 --> 00:00:40,200 Speaker 1: work of making Internet spaces safer for everyone often falls 10 00:00:40,200 --> 00:00:44,520 Speaker 1: on black women like Shappeka, and it's often tough, thankless, ignored, 11 00:00:44,880 --> 00:00:49,080 Speaker 1: and sometimes even dangerous. Even though Shappeka's work changed the 12 00:00:49,080 --> 00:00:51,919 Speaker 1: way we think about the Internet, she did it all unpaid. 13 00:00:52,440 --> 00:00:56,480 Speaker 1: She died crowdfunding for healthcare costs. Shaffika wanted people to 14 00:00:56,520 --> 00:00:59,840 Speaker 1: know the realities of long COVID. Last year, she tweeted, 15 00:01:00,280 --> 00:01:03,800 Speaker 1: when long COVID affects kill me sooner than later, I 16 00:01:03,920 --> 00:01:06,480 Speaker 1: need you all to recognize and say loudly what it was, 17 00:01:07,000 --> 00:01:10,759 Speaker 1: adding this won't be considered a massive crime against humanity 18 00:01:11,160 --> 00:01:12,520 Speaker 1: until we're all gone. 19 00:01:13,120 --> 00:01:13,640 Speaker 2: Just tell them. 20 00:01:13,680 --> 00:01:17,560 Speaker 1: I thought and tried this very podcast, and this work 21 00:01:17,840 --> 00:01:21,520 Speaker 1: would never exist if not for Shafika, a funny, fierce 22 00:01:21,640 --> 00:01:24,600 Speaker 1: cat lady who made the Internet just a better place 23 00:01:24,640 --> 00:01:27,360 Speaker 1: to show up on, who built an entire school of 24 00:01:27,400 --> 00:01:30,840 Speaker 1: thought out of necessity that made the Internet safer and 25 00:01:30,920 --> 00:01:33,640 Speaker 1: better for all of us. She was one of the 26 00:01:33,760 --> 00:01:36,119 Speaker 1: very first interviews I ever did for their No Girls 27 00:01:36,120 --> 00:01:38,520 Speaker 1: on the Internet back in twenty twenty when we first launched, 28 00:01:38,880 --> 00:01:41,920 Speaker 1: and in honor of her brilliant, beautiful life cut short, 29 00:01:42,400 --> 00:01:49,760 Speaker 1: let's hear from her again, Shafika, I am so sorry. Okay, 30 00:01:49,800 --> 00:01:52,000 Speaker 1: So I could tell you this story one hundred times 31 00:01:52,040 --> 00:01:55,120 Speaker 1: and one hundred different ways. People just don't listen to women, 32 00:01:55,480 --> 00:02:00,360 Speaker 1: especially Black women, and it comes with big consequences. Years ago, 33 00:02:00,480 --> 00:02:04,200 Speaker 1: black feminists were experiencing a coordinated pattern of disinformation on Twitter. 34 00:02:04,760 --> 00:02:08,240 Speaker 1: They spoke up, but no one listened. That failure to 35 00:02:08,240 --> 00:02:10,840 Speaker 1: listen to black women had a big impact. It allowed 36 00:02:10,840 --> 00:02:14,240 Speaker 1: for the weaponization of online harassment tactics against other marginalized 37 00:02:14,280 --> 00:02:17,880 Speaker 1: people on social media and presents continued threats to our 38 00:02:17,919 --> 00:02:20,800 Speaker 1: democracy and safety. Okay, so let's just get this out 39 00:02:20,840 --> 00:02:23,639 Speaker 1: of the way right now. Twitter is a bucking cesspool. 40 00:02:23,840 --> 00:02:26,040 Speaker 1: If you spend any time there, you probably already know 41 00:02:26,120 --> 00:02:30,560 Speaker 1: this bad faith, commentary, reply, guys, trolls, harassment, It can 42 00:02:30,600 --> 00:02:33,720 Speaker 1: really just be an unpleasant place. In May, Twitter announced 43 00:02:33,720 --> 00:02:37,040 Speaker 1: they would start labeling tweets that spread misleading information. But 44 00:02:37,120 --> 00:02:39,600 Speaker 1: this comes years after black feminists raised the alarm about 45 00:02:39,639 --> 00:02:40,600 Speaker 1: it and were ignored. 46 00:02:41,200 --> 00:02:42,680 Speaker 2: These women weren't just being attacked. 47 00:02:42,840 --> 00:02:45,359 Speaker 1: They were learning about the tactics that bad actors used 48 00:02:45,360 --> 00:02:48,280 Speaker 1: to infiltrate online communities. They spoke up about what they 49 00:02:48,280 --> 00:02:51,720 Speaker 1: were experiencing online. So why did anyone listen? And what 50 00:02:51,800 --> 00:02:53,440 Speaker 1: might have happened if they had? 51 00:02:56,560 --> 00:03:02,160 Speaker 3: Shadika Hudson, freelancer tat Lady Sometimes activists. 52 00:03:03,120 --> 00:03:06,160 Speaker 1: Shapika had been using Twitter regularly since almost this very beginning, 53 00:03:06,320 --> 00:03:08,240 Speaker 1: where she spent most of her time online connecting with 54 00:03:08,280 --> 00:03:11,400 Speaker 1: other black feminists. In twenty fourteen year old job searching, 55 00:03:11,800 --> 00:03:14,480 Speaker 1: she noticed a hashtag that just did not make sense, 56 00:03:15,280 --> 00:03:18,440 Speaker 1: end Father's Day. The people pushing the end Father's Day 57 00:03:18,440 --> 00:03:21,799 Speaker 1: hashtag on Twitter appeared to be black feminists. They talked 58 00:03:21,800 --> 00:03:23,840 Speaker 1: about how we should have polished Father's Day because too 59 00:03:23,840 --> 00:03:26,280 Speaker 1: many black men day outside of their race, or because 60 00:03:26,360 --> 00:03:27,799 Speaker 1: black men don't support their children. 61 00:03:28,040 --> 00:03:29,799 Speaker 2: Stuff that just seemed really out there. 62 00:03:30,800 --> 00:03:33,480 Speaker 3: Unless have had like ten different tabs open because I 63 00:03:33,639 --> 00:03:37,920 Speaker 3: was also like doing a job search and just going 64 00:03:37,960 --> 00:03:41,600 Speaker 3: about my life, and it some one tweet caught my 65 00:03:41,640 --> 00:03:46,640 Speaker 3: attention because it was so completely off the wall, and 66 00:03:46,680 --> 00:03:49,720 Speaker 3: I don't know who retweeted it or like how it 67 00:03:49,840 --> 00:03:54,920 Speaker 3: even arrived in my timeline, but it wasn't anything that 68 00:03:55,080 --> 00:03:59,839 Speaker 3: any black feminist anywhere would say. It was like, what 69 00:03:59,880 --> 00:04:07,200 Speaker 3: was oh gosh, yeah, in Father's day. I wish these 70 00:04:07,240 --> 00:04:10,920 Speaker 3: white women would stop stealing our men. Something just completely 71 00:04:10,920 --> 00:04:15,120 Speaker 3: off the wall. They had nothing to do with anything, 72 00:04:15,840 --> 00:04:19,479 Speaker 3: and the avatar was someone who I didn't recognize. Now, 73 00:04:19,520 --> 00:04:22,520 Speaker 3: the thing about the black women's community on Twitter, the 74 00:04:22,560 --> 00:04:25,120 Speaker 3: thing about a lot of communities on Twitter, is that 75 00:04:25,440 --> 00:04:29,120 Speaker 3: you might not necessarily get along with everybody, but you 76 00:04:29,320 --> 00:04:33,120 Speaker 3: know whoeveryone is like, and if you haven't met them 77 00:04:33,240 --> 00:04:36,120 Speaker 3: or seen them out, or you know, done a tweet up, 78 00:04:36,480 --> 00:04:39,679 Speaker 3: hung out at a party, something someone you know has 79 00:04:40,200 --> 00:04:43,560 Speaker 3: And in this particular instance, I'm clicked on the person 80 00:04:43,720 --> 00:04:47,000 Speaker 3: well the accounts profile. I said, okay, who is this. 81 00:04:47,800 --> 00:04:51,280 Speaker 3: I've never seen this person and it looks like they 82 00:04:51,440 --> 00:04:56,440 Speaker 3: just joined like two days ago, and they're just tweeting 83 00:04:56,480 --> 00:04:59,800 Speaker 3: about this with this hashtag and they have you know, 84 00:04:59,800 --> 00:05:02,400 Speaker 3: the photo of a black woman, but it just nothing 85 00:05:02,520 --> 00:05:05,359 Speaker 3: adds up. So that drove me to click on the 86 00:05:05,400 --> 00:05:08,839 Speaker 3: hashtag and Father's Day, and lo and behold. When I 87 00:05:08,880 --> 00:05:12,279 Speaker 3: did a Twitter search, there's a bunch of accounts that 88 00:05:12,520 --> 00:05:17,480 Speaker 3: are saying things that are completely left, like not left, 89 00:05:17,560 --> 00:05:20,680 Speaker 3: like you know, politically, just kind of left, like left, 90 00:05:21,600 --> 00:05:25,560 Speaker 3: where are you coming from? Left? And I didn't recognize 91 00:05:25,640 --> 00:05:28,800 Speaker 3: any of them, so I at that point I just 92 00:05:28,920 --> 00:05:32,920 Speaker 3: kind of asked a general question from my timeline to say, Okay, 93 00:05:32,960 --> 00:05:35,960 Speaker 3: you guys, what's going on. I keep seeing this hashtag 94 00:05:36,000 --> 00:05:38,520 Speaker 3: and these accounts that I don't recognize, with people who 95 00:05:38,880 --> 00:05:42,320 Speaker 3: look like they just joined like five seconds ago. And 96 00:05:42,440 --> 00:05:44,800 Speaker 3: someone said, yeah, it looks like this is like some 97 00:05:44,920 --> 00:05:52,600 Speaker 3: kind of fortune thing. That's when I really started digging. 98 00:05:52,760 --> 00:05:56,200 Speaker 3: I said, Okay, well, this is really awful because they're 99 00:05:56,279 --> 00:06:00,120 Speaker 3: pretending to be black women who were saying these awful things, 100 00:06:00,160 --> 00:06:05,320 Speaker 3: and I'm smart enough to know that nothing here that 101 00:06:05,320 --> 00:06:11,159 Speaker 3: they're saying is even remotely what a real black feminist 102 00:06:11,200 --> 00:06:14,320 Speaker 3: would say. I honestly think the people who they fooled 103 00:06:14,320 --> 00:06:20,920 Speaker 3: immediately were already probably biased against feminists or black women, 104 00:06:21,040 --> 00:06:24,400 Speaker 3: or some combination of the two. I didn't get the 105 00:06:24,440 --> 00:06:26,760 Speaker 3: impression that they were fooling most of the people I followed, 106 00:06:26,839 --> 00:06:30,039 Speaker 3: is is what I mean. But they were getting some reaction. 107 00:06:31,000 --> 00:06:33,839 Speaker 1: That's when Shapika went from curious to piste. 108 00:06:34,440 --> 00:06:39,040 Speaker 3: I got so mad, Like I remember just being so angry. 109 00:06:39,080 --> 00:06:44,719 Speaker 3: I could feel my cheeks and my ears heat up, honestly, 110 00:06:44,920 --> 00:06:47,640 Speaker 3: like you know in the in the cartoons where the 111 00:06:47,760 --> 00:06:53,200 Speaker 3: character like starts seeming from their sp coming to your 112 00:06:53,200 --> 00:06:59,920 Speaker 3: ears as he hear the tea kettle whistle. I was curious. 113 00:07:00,000 --> 00:07:01,680 Speaker 3: So I was like, you know, it's not like we 114 00:07:01,760 --> 00:07:06,359 Speaker 3: don't get enough garbage online just being black women, you know, 115 00:07:06,680 --> 00:07:10,560 Speaker 3: with people just randomly showing up in our mentions to 116 00:07:10,720 --> 00:07:14,120 Speaker 3: argue with something that we said, not because they necessarily disagree, 117 00:07:14,160 --> 00:07:15,760 Speaker 3: but because that's what people do when you're a black 118 00:07:15,760 --> 00:07:21,560 Speaker 3: woman online. Apparently, because we don't deal with enough out 119 00:07:21,560 --> 00:07:23,880 Speaker 3: here in real life and online, we don't deal with enough. 120 00:07:23,880 --> 00:07:32,080 Speaker 3: We've got this whole silly operation thing happening. So I said, well, 121 00:07:32,320 --> 00:07:35,240 Speaker 3: let me just go ahead and take a look and 122 00:07:35,280 --> 00:07:37,880 Speaker 3: see what's really going on and see how bad this is. 123 00:07:38,080 --> 00:07:40,840 Speaker 3: And as I began to dig, I saw just how 124 00:07:40,960 --> 00:07:44,480 Speaker 3: bad it was, and I realized that I would not 125 00:07:44,600 --> 00:07:48,560 Speaker 3: be able to point out all of these accounts alone. 126 00:07:49,120 --> 00:07:51,200 Speaker 1: You know how in movies when a character discovers this 127 00:07:51,200 --> 00:07:53,880 Speaker 1: thing they've been investigating as much, much bigger than they realized. 128 00:07:54,120 --> 00:07:56,120 Speaker 1: There is no pepe silvia, and this thing goes all 129 00:07:56,160 --> 00:07:58,560 Speaker 1: the way to the top. Well, that's how she speaks out. 130 00:07:59,240 --> 00:08:02,000 Speaker 1: She knew the Twitter accounts Pushing and Father's Day weren't 131 00:08:02,000 --> 00:08:04,920 Speaker 1: actually black women. They were just impersonating black women, and 132 00:08:05,000 --> 00:08:07,600 Speaker 1: pretty badly at that. But there were too many of 133 00:08:07,640 --> 00:08:09,360 Speaker 1: them for this to be a one off thing. It 134 00:08:09,480 --> 00:08:12,080 Speaker 1: had to be coordinated, and there were also too many 135 00:08:12,080 --> 00:08:14,480 Speaker 1: for her to tackle alone. She wanted to give other 136 00:08:14,520 --> 00:08:17,480 Speaker 1: black feminists a tool to sniff out these imposters, so 137 00:08:17,520 --> 00:08:19,760 Speaker 1: she fought back with a hashtag of her own, your 138 00:08:19,760 --> 00:08:20,600 Speaker 1: slipp ish Showing. 139 00:08:21,160 --> 00:08:24,360 Speaker 3: I went ahead with your slippish showing. I might've run 140 00:08:24,800 --> 00:08:27,560 Speaker 3: another line like, I don't know your mess scare is 141 00:08:27,640 --> 00:08:29,360 Speaker 3: running something like that, but your slippy is showing just 142 00:08:29,400 --> 00:08:31,360 Speaker 3: seemed to work. It really just seemed to work. 143 00:08:31,720 --> 00:08:33,400 Speaker 1: Okay, So, if you're not a lady from the South, 144 00:08:33,440 --> 00:08:35,920 Speaker 1: the phrase your slipp ish showing might not mean anything 145 00:08:35,960 --> 00:08:38,440 Speaker 1: to you. Literally, it means when your slip is peeking 146 00:08:38,480 --> 00:08:40,880 Speaker 1: out from underneath your skirt or dress a big fashion 147 00:08:40,960 --> 00:08:43,760 Speaker 1: no no, but where I come from, that one phrase 148 00:08:44,000 --> 00:08:47,080 Speaker 1: really highlights us a versiveness of what I'll call auntie speak. 149 00:08:47,480 --> 00:08:49,560 Speaker 1: Think of it a bit like the phrase bless your heart. 150 00:08:50,000 --> 00:08:51,800 Speaker 1: A lady at church might tell you that your slip 151 00:08:51,800 --> 00:08:53,520 Speaker 1: is peeking out from the bottom of your skirt because 152 00:08:53,520 --> 00:08:56,720 Speaker 1: they care about you looking your best. Or they could 153 00:08:56,720 --> 00:08:58,520 Speaker 1: tell you your slip is showing because they don't like you, 154 00:08:58,559 --> 00:09:01,240 Speaker 1: and they're pointing out pop that you aren't looking as 155 00:09:01,280 --> 00:09:02,880 Speaker 1: good and put together as you think you are. 156 00:09:04,080 --> 00:09:05,800 Speaker 3: You know, just the sort of thing that one of 157 00:09:05,800 --> 00:09:08,560 Speaker 3: your aunties might say to you in church whence, oh, honey, 158 00:09:08,640 --> 00:09:12,839 Speaker 3: you need to you need to fix your slip. It's 159 00:09:12,880 --> 00:09:22,640 Speaker 3: showing except mean, because right, there's a there's a difference 160 00:09:22,640 --> 00:09:24,600 Speaker 3: between your slip is showing from one of your aunties 161 00:09:24,640 --> 00:09:28,520 Speaker 3: and your slip is showing from somebody who doesn't like you. 162 00:09:30,200 --> 00:09:32,720 Speaker 3: And that was what I was going with of, like, yeah, 163 00:09:32,760 --> 00:09:35,520 Speaker 3: your slip is showing. I'm telling you because I was 164 00:09:35,640 --> 00:09:39,640 Speaker 3: raised right, not because I particularly care about you being embarrassed. 165 00:09:43,559 --> 00:09:45,000 Speaker 3: I love that so much. 166 00:09:45,080 --> 00:09:49,959 Speaker 1: I love how you kind of use this Southern auntie 167 00:09:50,840 --> 00:09:51,920 Speaker 1: expression that. 168 00:09:51,840 --> 00:09:53,960 Speaker 3: We all sort of know what it means. What's also 169 00:09:54,000 --> 00:09:55,960 Speaker 3: funny is that I would imagine the people who are 170 00:09:56,000 --> 00:10:00,240 Speaker 3: impersonating black women probably that probably that nu once probably 171 00:10:00,240 --> 00:10:03,720 Speaker 3: goes right over their head. Yes, that was also one 172 00:10:03,720 --> 00:10:06,599 Speaker 3: of the things that I also delighted in, because of 173 00:10:06,640 --> 00:10:09,320 Speaker 3: course they wouldn't get it, because you'd have to. I mean, 174 00:10:09,400 --> 00:10:13,240 Speaker 3: you have to be somewhat embedded within certain communities to 175 00:10:13,320 --> 00:10:18,360 Speaker 3: pick up on the nuance, and they really weren't. 176 00:10:19,040 --> 00:10:21,559 Speaker 2: It's fitting that we're talking about getting the nuance. That's 177 00:10:21,559 --> 00:10:23,480 Speaker 2: certain something you can't really teach. 178 00:10:24,000 --> 00:10:26,480 Speaker 1: This would ultimately be the undoing of people and personating 179 00:10:26,520 --> 00:10:30,520 Speaker 1: black women online, their inability to authentically sound like black women. 180 00:10:31,000 --> 00:10:33,960 Speaker 1: They'd try to use aave or African American evernacular English, 181 00:10:34,120 --> 00:10:36,760 Speaker 1: but get the expressions all wrong in ways that might 182 00:10:36,800 --> 00:10:39,480 Speaker 1: as well be screaming, I am a white person pretending 183 00:10:39,520 --> 00:10:41,600 Speaker 1: to be a Black woman. This is where I should 184 00:10:41,640 --> 00:10:43,880 Speaker 1: probably say that. Around the same time, in twenty fourteen, 185 00:10:44,200 --> 00:10:46,920 Speaker 1: I noticed someone on Twitter using my photo and tweeting 186 00:10:46,960 --> 00:10:49,960 Speaker 1: confusing things about black people. I never knew who was 187 00:10:49,960 --> 00:10:51,840 Speaker 1: behind it or why it was happening, but if I 188 00:10:51,920 --> 00:10:54,240 Speaker 1: had to say, I would say it wasn't an actual 189 00:10:54,280 --> 00:10:56,520 Speaker 1: black woman, because the things they were saying were just 190 00:10:56,679 --> 00:10:59,840 Speaker 1: so out there, things like I'm going to be voting 191 00:10:59,880 --> 00:11:02,640 Speaker 1: for Trump because Hillary Clinton is whack y'all, things that 192 00:11:02,760 --> 00:11:07,240 Speaker 1: just didn't sound right because they're not speakers of aav 193 00:11:08,360 --> 00:11:09,360 Speaker 1: they're approximating. 194 00:11:09,720 --> 00:11:15,760 Speaker 3: The thing that like really really really seemed to like 195 00:11:16,920 --> 00:11:23,160 Speaker 3: immediately point them out was this consistent and ability to 196 00:11:23,440 --> 00:11:28,120 Speaker 3: understand and properly use the habitual B. They didn't get it. 197 00:11:28,520 --> 00:11:31,600 Speaker 3: They did not like they they would use the habitual 198 00:11:31,679 --> 00:11:38,120 Speaker 3: B just kind of like for the future tents, you 199 00:11:38,200 --> 00:11:40,240 Speaker 3: know what I mean. Like it was terrible, and a 200 00:11:40,240 --> 00:11:43,319 Speaker 3: lot of the time it was just like really obviously 201 00:11:43,440 --> 00:11:48,600 Speaker 3: racist word salad. Obviously racist word salad. I love it 202 00:11:48,640 --> 00:11:49,360 Speaker 3: my new band name. 203 00:11:50,200 --> 00:11:52,640 Speaker 1: Ultimately, it seemed like the point of End Father's Day 204 00:11:52,679 --> 00:11:55,280 Speaker 1: was to see what kind of discord bad actors organizing 205 00:11:55,360 --> 00:11:58,080 Speaker 1: on message boards like Forshan could sew with the feminist 206 00:11:58,080 --> 00:12:01,520 Speaker 1: online communities and to make actual feminists and our issues 207 00:12:01,679 --> 00:12:04,840 Speaker 1: look like petty, stupid man haters whose issues were so 208 00:12:04,920 --> 00:12:08,400 Speaker 1: outlandish they could never be taken seriously. It turns out 209 00:12:08,600 --> 00:12:12,640 Speaker 1: this is actually a pretty common disinformation tactic hijacking public 210 00:12:12,679 --> 00:12:16,319 Speaker 1: conversations about sensitive topics or wedge issues through medium manipulation 211 00:12:16,880 --> 00:12:18,679 Speaker 1: is a way of making people afraid of having an 212 00:12:18,679 --> 00:12:21,640 Speaker 1: opinion in public and ultimately trying to silence them. 213 00:12:22,480 --> 00:12:25,520 Speaker 4: I'm Joan Donovan and I'm the research director at the 214 00:12:25,600 --> 00:12:29,680 Speaker 4: Shorenstein Center on Media, Politics and Public Policy. 215 00:12:30,200 --> 00:12:32,920 Speaker 1: Doctor Donovan says, the same way that brands and politicians 216 00:12:32,960 --> 00:12:35,160 Speaker 1: realize the power of social media, the kind of people 217 00:12:35,160 --> 00:12:37,400 Speaker 1: who want to harass others did do it can have 218 00:12:37,440 --> 00:12:40,440 Speaker 1: a big impact, especially as we're using social media to 219 00:12:40,480 --> 00:12:44,240 Speaker 1: talk about thorny issues like race, gender, and sexuality, issues 220 00:12:44,240 --> 00:12:47,600 Speaker 1: that require nuance to discuss thoughtfully. It makes it tough 221 00:12:47,600 --> 00:12:50,240 Speaker 1: for anyone to have a good faith dialogue online. 222 00:12:51,000 --> 00:12:55,800 Speaker 4: Over time, just like the politicians learned to use social media, 223 00:12:56,240 --> 00:13:00,800 Speaker 4: we had white supremacists figure out that you don't actually 224 00:13:00,840 --> 00:13:04,600 Speaker 4: need to show up in public to have an impact 225 00:13:04,679 --> 00:13:08,960 Speaker 4: on people's lives, and so we saw networked and coordinated 226 00:13:09,040 --> 00:13:15,760 Speaker 4: harassment campaigns. It just continue, even to this day, continue 227 00:13:15,840 --> 00:13:21,319 Speaker 4: to be useful ways to shut down journalists, to impersonate 228 00:13:21,360 --> 00:13:28,160 Speaker 4: different groups, and to really cause a fracture in public 229 00:13:28,240 --> 00:13:33,800 Speaker 4: conversation about really important issues that require some level of nuance. 230 00:13:33,920 --> 00:13:37,720 Speaker 4: Some level of understanding and a lot of compassion to 231 00:13:37,840 --> 00:13:41,560 Speaker 4: talk about, you know, especially racism in this moment, and 232 00:13:41,720 --> 00:13:44,959 Speaker 4: people are reticent to talk about it because they're afraid 233 00:13:45,320 --> 00:13:50,720 Speaker 4: of saying something wrong, especially in the environments online where 234 00:13:51,120 --> 00:13:54,040 Speaker 4: if you do make a misstep you could get dragged, 235 00:13:54,080 --> 00:13:57,880 Speaker 4: you could get canceled. But also some of that might 236 00:13:57,960 --> 00:14:01,040 Speaker 4: be artificial. It might be the case that people do 237 00:14:01,120 --> 00:14:03,560 Speaker 4: sympathize with you, people do want to help you grow 238 00:14:03,600 --> 00:14:07,480 Speaker 4: and learn, but certain medium manipulators see that as an 239 00:14:07,480 --> 00:14:13,680 Speaker 4: opportunity to swarm in and really drive the witch as 240 00:14:13,679 --> 00:14:14,440 Speaker 4: deep as it can go. 241 00:14:15,160 --> 00:14:17,880 Speaker 1: A few right wing news outlets picked up the hashtag 242 00:14:17,920 --> 00:14:21,240 Speaker 1: and Father's Day and amplified it as a legitimate feminist take. 243 00:14:21,560 --> 00:14:23,760 Speaker 2: This is how Fox News covered it, Like some of 244 00:14:23,760 --> 00:14:26,040 Speaker 2: these tweets, heres from Tasha. She wrote in and everyone 245 00:14:26,120 --> 00:14:28,600 Speaker 2: knows we only need mothers, Why do we even need 246 00:14:28,680 --> 00:14:30,600 Speaker 2: Father's Day? Fathers are useless? 247 00:14:30,800 --> 00:14:32,040 Speaker 3: Hashtag and come. 248 00:14:31,920 --> 00:14:36,000 Speaker 1: On, oh, come on, you're this nasty feminist rhetoric that 249 00:14:36,040 --> 00:14:38,520 Speaker 1: they're not just like interested in ending Father's Day, interesting 250 00:14:38,600 --> 00:14:42,160 Speaker 1: ending men, that's really what they want. But Shafika says, 251 00:14:42,360 --> 00:14:44,680 Speaker 1: only the kind of people who were already predisposed to 252 00:14:44,680 --> 00:14:48,280 Speaker 1: be skeptical of women and feminists, especially black women, fell 253 00:14:48,320 --> 00:14:52,800 Speaker 1: for it. Well, it was actually at first, I remember 254 00:14:52,840 --> 00:14:56,360 Speaker 1: I was incredulous, Like, honestly, I was looking at people like, oh, 255 00:14:56,400 --> 00:15:01,560 Speaker 1: and Father's Day feminists take a terrible turn and blah blah, 256 00:15:01,560 --> 00:15:03,360 Speaker 1: and I was like, you got to be hitting. 257 00:15:04,240 --> 00:15:07,080 Speaker 3: But then I realized that no, they were completely serious. 258 00:15:08,080 --> 00:15:10,320 Speaker 3: And then it dawned on me that these were people 259 00:15:10,400 --> 00:15:17,320 Speaker 3: who could not possibly understand feminism, possibly women in general, 260 00:15:18,600 --> 00:15:25,120 Speaker 3: Black people her too much of anything outside of their 261 00:15:25,160 --> 00:15:28,840 Speaker 3: little Fox News bubble like that. That was the impression 262 00:15:28,840 --> 00:15:31,400 Speaker 3: that I got, Like, basically, if you fell for this, 263 00:15:32,080 --> 00:15:36,440 Speaker 3: it's because you already had a certain set of bigotries 264 00:15:36,480 --> 00:15:38,120 Speaker 3: in place to fall for it. 265 00:15:38,640 --> 00:15:41,480 Speaker 1: What's happening with black women online is much less widely 266 00:15:41,520 --> 00:15:44,560 Speaker 1: known than gamer Gate. Were angry men coordinated to harass 267 00:15:44,560 --> 00:15:47,440 Speaker 1: progressive voices online who were mostly women in the month's 268 00:15:47,440 --> 00:15:50,800 Speaker 1: following and Father's Day. Shafika thinks it was ignored because 269 00:15:50,800 --> 00:15:53,640 Speaker 1: the women who were targeted were black. Not only was 270 00:15:53,680 --> 00:15:55,440 Speaker 1: she helping to create a tool to stamp out this 271 00:15:55,520 --> 00:15:59,040 Speaker 1: kind of disinformation online, she also wanted to document that 272 00:15:59,040 --> 00:16:02,120 Speaker 1: it was happening. Wouldn't go forgotten or raised just because 273 00:16:02,160 --> 00:16:03,280 Speaker 1: it was happening to black women. 274 00:16:06,640 --> 00:16:10,160 Speaker 3: As you're probably aware of, a lot of us are 275 00:16:10,280 --> 00:16:14,520 Speaker 3: big on what we call receipts, So there are plenty 276 00:16:14,680 --> 00:16:17,880 Speaker 3: of receipts. We've got the screen cabs. You can't even 277 00:16:18,000 --> 00:16:21,640 Speaker 3: you know, delete the tweets because we got it, we 278 00:16:21,800 --> 00:16:26,640 Speaker 3: got the information. But yeah, I mean, that's that's been 279 00:16:26,680 --> 00:16:30,600 Speaker 3: a big part of it for me, and it's frustrating 280 00:16:31,240 --> 00:16:36,240 Speaker 3: for a lot of us to see essentially a history erased. 281 00:16:37,480 --> 00:16:41,840 Speaker 3: It's particularly distressing for me because you know, I'm not 282 00:16:41,920 --> 00:16:44,440 Speaker 3: I wouldn't consider myself a scholar at this particular point. 283 00:16:44,520 --> 00:16:49,800 Speaker 3: And my friend so True who also was absolutely integral 284 00:16:50,520 --> 00:16:54,240 Speaker 3: with formulating your slippy showing and how it kind of 285 00:16:54,280 --> 00:16:59,000 Speaker 3: played out and became a useful tool. But back when 286 00:16:59,000 --> 00:17:01,680 Speaker 3: I was a scholar, I understood that one of the 287 00:17:01,680 --> 00:17:05,400 Speaker 3: things that people do when they're trying to erase the 288 00:17:05,440 --> 00:17:11,000 Speaker 3: impact of a movement is they kind of start deleting history. 289 00:17:12,600 --> 00:17:18,320 Speaker 3: It's a huge feature of a ra Sure when people 290 00:17:18,440 --> 00:17:21,400 Speaker 3: talk about your slip is showing, if they talk about 291 00:17:21,400 --> 00:17:26,439 Speaker 3: it or if they mention it at all, it's weird. 292 00:17:26,520 --> 00:17:31,080 Speaker 3: It kind of gets vaguely mentioned in relation to gamer 293 00:17:31,160 --> 00:17:33,960 Speaker 3: Gate as this weird thing that sort of happened before 294 00:17:34,000 --> 00:17:37,600 Speaker 3: gamer Gate that wasn't really relevant and didn't provide anybody 295 00:17:37,680 --> 00:17:41,000 Speaker 3: any tools or you know, it was, you know, just 296 00:17:41,080 --> 00:17:46,080 Speaker 3: kind of a blip as opposed to what it was, 297 00:17:46,200 --> 00:17:54,639 Speaker 3: which was a scary peak into the future. And again, 298 00:17:54,760 --> 00:17:57,479 Speaker 3: like I said, hindsight being twenty twenty once, when you 299 00:17:57,600 --> 00:18:01,040 Speaker 3: start to look back on all of these four chan 300 00:18:02,160 --> 00:18:04,160 Speaker 3: for I'm sorry, I can't say four chan without making 301 00:18:04,160 --> 00:18:09,919 Speaker 3: that noise. You have a special four chan noise I do. 302 00:18:10,280 --> 00:18:12,200 Speaker 3: Oh my gosh. Someone else pointed out to me. It's like, 303 00:18:12,240 --> 00:18:13,840 Speaker 3: do you realize that you just kind of make this 304 00:18:13,960 --> 00:18:18,560 Speaker 3: disgusted noise? What I mean you say, I'm like, I 305 00:18:18,680 --> 00:18:26,800 Speaker 3: just sorry, it's automatic. I'm working on it. When you 306 00:18:26,840 --> 00:18:32,120 Speaker 3: try to kind of understand how everything happened, you have 307 00:18:32,160 --> 00:18:36,240 Speaker 3: to take all of it into account. I really think 308 00:18:36,960 --> 00:18:41,480 Speaker 3: that in Father's day, and you know, consequently your slip 309 00:18:41,480 --> 00:18:44,720 Speaker 3: is showing were a huge part of it, and it's 310 00:18:45,080 --> 00:18:47,640 Speaker 3: it can be frustrating to see it left out of 311 00:18:47,720 --> 00:18:50,000 Speaker 3: the history because it's like, Okay, you're missing a really 312 00:18:50,040 --> 00:18:57,000 Speaker 3: relevant chunk of understanding how all of this mess happened. 313 00:18:57,400 --> 00:18:59,720 Speaker 1: Even at a time where we're having a conversations around 314 00:19:00,000 --> 00:19:03,040 Speaker 1: women's experiences online. Why do you think your slip is 315 00:19:03,080 --> 00:19:05,560 Speaker 1: showing and end Father's Day and the way that women 316 00:19:05,600 --> 00:19:08,040 Speaker 1: and folks of color have been harassed online pretty much 317 00:19:08,080 --> 00:19:08,720 Speaker 1: goes overlooked. 318 00:19:08,720 --> 00:19:11,800 Speaker 3: Why do you think that is not? Yeah? That and 319 00:19:11,840 --> 00:19:15,520 Speaker 3: again it's frustrating, and my theory remains it's because the 320 00:19:15,600 --> 00:19:18,800 Speaker 3: targeted group at the time for the in Father's Day 321 00:19:19,080 --> 00:19:27,639 Speaker 3: of four chan operation were black women. It's honestly, that's 322 00:19:27,760 --> 00:19:31,240 Speaker 3: my That's I have no other answer at this point, 323 00:19:31,320 --> 00:19:33,679 Speaker 3: has been six years, I've watched this just kind of 324 00:19:34,240 --> 00:19:40,320 Speaker 3: repeatedly happen, and the only answer, unfortunately, that I have 325 00:19:40,600 --> 00:19:45,919 Speaker 3: is that, Okay, well, this is being largely ignored and 326 00:19:46,160 --> 00:19:51,480 Speaker 3: raped because of who the targets were, and the targets 327 00:19:51,480 --> 00:19:54,840 Speaker 3: were of black women, but particularly black feminists. 328 00:19:55,760 --> 00:20:07,399 Speaker 1: We'll be right back after this quick break. People who 329 00:20:07,440 --> 00:20:11,200 Speaker 1: are traditionally marginalized online, like black women, are specifically impacted 330 00:20:11,200 --> 00:20:14,200 Speaker 1: by things like disinformation and harassment on social media. 331 00:20:14,359 --> 00:20:16,280 Speaker 2: The ultimate goal is to freak them. 332 00:20:16,119 --> 00:20:18,480 Speaker 1: Out so much that they'll shut down their social media 333 00:20:18,600 --> 00:20:20,119 Speaker 1: and just stop talking. 334 00:20:20,920 --> 00:20:24,120 Speaker 2: Here's doctor Donovan again. Yeah, so we have. 335 00:20:24,119 --> 00:20:27,760 Speaker 4: To remember that a lot of the ways in which 336 00:20:28,440 --> 00:20:34,639 Speaker 4: disinformation is carried through networks are also related to the 337 00:20:34,680 --> 00:20:39,359 Speaker 4: ways in which people are harassed online. You know, So 338 00:20:39,440 --> 00:20:44,280 Speaker 4: if you're uh, there's a concept called gender trolling. It's 339 00:20:44,280 --> 00:20:51,560 Speaker 4: evolved into transtrolling, race trolling, queer trolling, where the characteristics 340 00:20:51,560 --> 00:20:56,240 Speaker 4: of your identity become the thing that they focus on, 341 00:20:56,600 --> 00:20:59,879 Speaker 4: and they'll, you know, they'll be a swarm of folk 342 00:21:01,040 --> 00:21:03,840 Speaker 4: that have coordinated in some other place, usually on a 343 00:21:03,880 --> 00:21:10,359 Speaker 4: message board, and they will target specific public figures or women, 344 00:21:10,840 --> 00:21:17,320 Speaker 4: or trans folks or prominent black activists in order to 345 00:21:17,359 --> 00:21:20,600 Speaker 4: get them to shut off their social media, and they 346 00:21:20,600 --> 00:21:26,359 Speaker 4: will use all kinds of horrendous images and threats to 347 00:21:26,480 --> 00:21:30,200 Speaker 4: try to get you to feel fear and to shut 348 00:21:30,359 --> 00:21:33,919 Speaker 4: it down. And we don't see that same kind of 349 00:21:34,080 --> 00:21:40,440 Speaker 4: level of threat making when it comes to trolling male candidates. 350 00:21:40,920 --> 00:21:42,240 Speaker 3: And that has to do with. 351 00:21:42,200 --> 00:21:47,560 Speaker 4: The characteristics of the harassers themselves, which often see the 352 00:21:47,640 --> 00:21:52,080 Speaker 4: harassment as a form of activism and as a form 353 00:21:52,200 --> 00:21:57,720 Speaker 4: of defending themselves or defending their piece of the culture. 354 00:21:57,800 --> 00:22:00,399 Speaker 4: And so a lot of these people to to be 355 00:22:00,800 --> 00:22:07,720 Speaker 4: misogynists as well as racists, and in their smaller online 356 00:22:07,720 --> 00:22:11,680 Speaker 4: communities where they don't think they're watch they'll talk openly 357 00:22:12,040 --> 00:22:15,680 Speaker 4: about that, and they'll talk openly about who they should 358 00:22:15,760 --> 00:22:19,800 Speaker 4: target and why and what the problem is. And I 359 00:22:19,840 --> 00:22:23,600 Speaker 4: think at this stage we've been through this enough to 360 00:22:23,720 --> 00:22:29,280 Speaker 4: know it's a serious problem. But it still happens every day, 361 00:22:29,720 --> 00:22:33,200 Speaker 4: and especially in this moment, we're seeing an incredible amount 362 00:22:33,240 --> 00:22:40,560 Speaker 4: of trolling around anti black racism. And the responsibility though, 363 00:22:41,240 --> 00:22:45,960 Speaker 4: for dealing with this, lies with the platform companies first 364 00:22:46,000 --> 00:22:46,680 Speaker 4: and foremost. 365 00:22:47,480 --> 00:22:51,000 Speaker 1: Twitter CEO Jack Dorsey hasn't always been the most responsive 366 00:22:51,040 --> 00:22:53,399 Speaker 1: to the misuse of the platform. You'd think you'd be 367 00:22:53,440 --> 00:22:55,960 Speaker 1: more concerned, but Shafika says that wasn't the case. She 368 00:22:56,080 --> 00:22:58,240 Speaker 1: and the other black women targeted were pretty much left 369 00:22:58,280 --> 00:23:03,119 Speaker 1: on their own to figure it out. So did the 370 00:23:03,160 --> 00:23:05,879 Speaker 1: powers that be at Twitter or any other social media 371 00:23:05,920 --> 00:23:08,560 Speaker 1: company or any other official do anything to fix this? 372 00:23:09,480 --> 00:23:13,040 Speaker 3: No, that the short answer there is no. Now. The 373 00:23:13,119 --> 00:23:18,240 Speaker 3: longer explanation is that we repeatedly brought this whole thing 374 00:23:18,359 --> 00:23:24,560 Speaker 3: to the attention of Twitter support to Jack directly. It's 375 00:23:24,640 --> 00:23:28,199 Speaker 3: not like nobody knew what was happening. It it made the 376 00:23:28,320 --> 00:23:34,880 Speaker 3: news like so it's not as though he was ignorant. 377 00:23:34,880 --> 00:23:39,880 Speaker 3: The general impression that I got from Twitter support was that, oh, well, 378 00:23:39,960 --> 00:23:42,840 Speaker 3: you know this is We're so sorry, our hands are 379 00:23:42,880 --> 00:23:46,320 Speaker 3: tied and blah blah blah, And I started looking into 380 00:23:46,560 --> 00:23:48,800 Speaker 3: the tech side of everything, and I realized that that 381 00:23:48,960 --> 00:23:54,919 Speaker 3: wasn't the truth. They absolutely had and had tools on 382 00:23:55,080 --> 00:24:00,960 Speaker 3: hand to stop this, and they just didn't. They just 383 00:24:01,040 --> 00:24:03,240 Speaker 3: let it happen. They just let us clean up the 384 00:24:03,280 --> 00:24:05,560 Speaker 3: mess and defend our communities ourselves. 385 00:24:06,280 --> 00:24:07,960 Speaker 1: As much as being left to fend for her own 386 00:24:07,960 --> 00:24:11,080 Speaker 1: community online sucked, it did teach Chapika that her online 387 00:24:11,119 --> 00:24:14,760 Speaker 1: community could do a lot with a little And while 388 00:24:14,760 --> 00:24:15,440 Speaker 1: that wasn't. 389 00:24:15,200 --> 00:24:21,280 Speaker 3: Cool at all, and hopefully at some point, and you know, 390 00:24:21,320 --> 00:24:24,280 Speaker 3: at some point down the line, they will be sufficiently 391 00:24:24,400 --> 00:24:28,600 Speaker 3: shamed for it because it was just really awful. We 392 00:24:28,720 --> 00:24:32,520 Speaker 3: learned what we could do on the ground with just 393 00:24:33,440 --> 00:24:40,360 Speaker 3: the very basic tool of like community organization and a hashtag, 394 00:24:41,160 --> 00:24:44,399 Speaker 3: we were able to do a whole lot to just 395 00:24:44,520 --> 00:24:48,520 Speaker 3: stop something that could have gotten way out of hand. 396 00:24:48,560 --> 00:24:52,600 Speaker 3: We outed it early and we ended it early. And 397 00:24:52,800 --> 00:24:57,040 Speaker 3: if something had been done to make sure that these 398 00:24:57,280 --> 00:25:00,840 Speaker 3: fake accounts that we were reporting had been taken on 399 00:25:00,920 --> 00:25:06,400 Speaker 3: a commission, there would have been a lot less for 400 00:25:06,920 --> 00:25:10,600 Speaker 3: gamer Gate to work with. They wouldn't have had to, 401 00:25:10,800 --> 00:25:13,200 Speaker 3: they wouldn't have had the opportunity to just go ahead 402 00:25:13,359 --> 00:25:16,560 Speaker 3: and access those same tools that they'd already created. 403 00:25:17,160 --> 00:25:19,600 Speaker 1: So in a kind of way, it sounds like your 404 00:25:19,680 --> 00:25:23,920 Speaker 1: work with your slip is showing and your work organizing 405 00:25:23,960 --> 00:25:27,240 Speaker 1: community responses online was kind of this canary and a 406 00:25:27,280 --> 00:25:30,199 Speaker 1: coal mine, and you all did all that you could 407 00:25:30,560 --> 00:25:33,760 Speaker 1: to prevent this, to stamp this out. But if only 408 00:25:33,840 --> 00:25:37,760 Speaker 1: the powers that be at Twitter or elsewhere had done anything, 409 00:25:38,359 --> 00:25:40,560 Speaker 1: then it might not be the sort of wide scale 410 00:25:40,640 --> 00:25:41,840 Speaker 1: situation that it is now. 411 00:25:41,960 --> 00:25:45,840 Speaker 3: That is exactly correct, and that I know that sounds damning, 412 00:25:46,000 --> 00:25:50,240 Speaker 3: but that's accurate. They could have stopped it. They could 413 00:25:50,400 --> 00:25:55,439 Speaker 3: still stop it. But the reason why, unfortunately, and this 414 00:25:55,600 --> 00:25:59,439 Speaker 3: was absolutely pointed out by people at the time and 415 00:25:59,560 --> 00:26:03,960 Speaker 3: people well later taking the whole situation from like, you know, 416 00:26:04,160 --> 00:26:09,960 Speaker 3: the whole post mortem of the whole incident, the reason 417 00:26:10,000 --> 00:26:17,200 Speaker 3: why they didn't is because of the profit model at 418 00:26:17,240 --> 00:26:24,920 Speaker 3: the time was based on number of accounts and interaction. 419 00:26:27,080 --> 00:26:31,200 Speaker 3: So you know that when you're selling your product, basically 420 00:26:31,760 --> 00:26:35,399 Speaker 3: we're we're the products to advertisers and whatever have you. 421 00:26:35,880 --> 00:26:38,800 Speaker 3: The more users, it looks like you have the better. 422 00:26:39,160 --> 00:26:45,000 Speaker 3: So it really wasn't in Twitter's best interests to say, Okay, well, 423 00:26:45,040 --> 00:26:51,800 Speaker 3: we have twenty accounts with one IP address that's suspicious 424 00:26:52,040 --> 00:26:55,280 Speaker 3: and we should look into it. And that's why they didn't. 425 00:26:55,760 --> 00:26:59,600 Speaker 3: They didn't took them a full two and a half years, 426 00:26:59,640 --> 00:27:02,720 Speaker 3: I think, to even really address it in a serious way. 427 00:27:02,720 --> 00:27:06,680 Speaker 3: And I think that was only after the whole congressional 428 00:27:09,040 --> 00:27:12,920 Speaker 3: me Like, I'm pretty sure that was after everybody who 429 00:27:13,000 --> 00:27:14,960 Speaker 3: was like the head of social media got called in 430 00:27:14,960 --> 00:27:20,000 Speaker 3: front of Congress. Mm hmm, Like that's what it took. 431 00:27:22,560 --> 00:27:24,159 Speaker 2: So that pretty much brings us to today. 432 00:27:25,080 --> 00:27:28,399 Speaker 1: Today, Twitter leads all other social media platforms and the 433 00:27:28,440 --> 00:27:31,680 Speaker 1: spread of misleading information about coronavirus, according to a study 434 00:27:31,680 --> 00:27:34,760 Speaker 1: by Oxford researchers called Types, Sources and Claims of COVID 435 00:27:34,840 --> 00:27:38,640 Speaker 1: nineteen misinformation, and a study out of Carnegie Mellon found 436 00:27:38,640 --> 00:27:41,360 Speaker 1: that most of the accounts pushing this misleading content are 437 00:27:41,400 --> 00:27:44,879 Speaker 1: actually convincing looking bots using Twitter to prey on people, 438 00:27:45,280 --> 00:27:47,080 Speaker 1: so division an increased polarization. 439 00:27:47,600 --> 00:27:49,360 Speaker 2: This isn't just an online saying either. 440 00:27:49,800 --> 00:27:53,439 Speaker 1: Kathleen Carly, the director for the Center for Informed Democracy 441 00:27:53,480 --> 00:27:58,040 Speaker 1: and Social Cybersecurity, says increased polarization will have a variety 442 00:27:58,080 --> 00:28:01,639 Speaker 1: of real world consequences and things like voting behavior and 443 00:28:01,680 --> 00:28:05,200 Speaker 1: hostility toward ethnic groups. And this summer, as Black Lives 444 00:28:05,240 --> 00:28:08,400 Speaker 1: Matter protests popped up all over the globe, Twitter confirmed 445 00:28:08,400 --> 00:28:11,400 Speaker 1: that multiple accounts posing as Black Lives Matter activists were 446 00:28:11,440 --> 00:28:14,360 Speaker 1: calling for violence in white suburbs. But those accounts were 447 00:28:14,359 --> 00:28:17,879 Speaker 1: actually run by white supremacist groups just posing as activists 448 00:28:17,880 --> 00:28:20,200 Speaker 1: and quote Antifa to cause chaos. 449 00:28:20,760 --> 00:28:24,679 Speaker 5: Facebook under fire again a Senate Intelligence Committee report claiming 450 00:28:24,760 --> 00:28:28,680 Speaker 5: Russian agents use social media sites like Facebook to target 451 00:28:28,720 --> 00:28:31,800 Speaker 5: African Americans in an effort to suppress voter turnout. 452 00:28:32,440 --> 00:28:35,480 Speaker 1: We already know that Russia used social media to interfere 453 00:28:35,480 --> 00:28:38,320 Speaker 1: with the twenty sixteen election, and in case you needed 454 00:28:38,360 --> 00:28:40,320 Speaker 1: a Senate report to confirm with black women have been 455 00:28:40,360 --> 00:28:43,680 Speaker 1: saying all along, a Senate inquiry cited an Oxford University 456 00:28:43,720 --> 00:28:47,000 Speaker 1: report on Russian interference on social media. They found that 457 00:28:47,040 --> 00:28:50,200 Speaker 1: campaigns targeted no single group more than African Americans. On 458 00:28:50,240 --> 00:28:53,000 Speaker 1: social media, they posed as black people and ran bony 459 00:28:53,040 --> 00:28:56,080 Speaker 1: black activist groups to influence Black voters to either stay 460 00:28:56,080 --> 00:28:59,240 Speaker 1: home or vote for Trump on election day. The Senate 461 00:28:59,280 --> 00:29:02,200 Speaker 1: Intelligence for says The posts were aimed at making Americans 462 00:29:02,200 --> 00:29:06,360 Speaker 1: suspicious of each other sound familiar. These are the very 463 00:29:06,440 --> 00:29:09,120 Speaker 1: same kind of tactics that black women like Shaffika were 464 00:29:09,120 --> 00:29:13,200 Speaker 1: complaining about years earlier, accounts posing as black people and 465 00:29:13,240 --> 00:29:17,280 Speaker 1: infistrating our online communities to create chaos and distrust. But 466 00:29:17,400 --> 00:29:19,720 Speaker 1: because the people with power didn't really do anything or 467 00:29:19,760 --> 00:29:23,240 Speaker 1: take it seriously, it kind of exposed this massive vulnerability. 468 00:29:23,840 --> 00:29:27,240 Speaker 1: Think of it as an online disinformation test balloon. It 469 00:29:27,360 --> 00:29:29,680 Speaker 1: showed that these kinds of attacks could happen and they'd 470 00:29:29,680 --> 00:29:32,720 Speaker 1: pretty much go unaddressed. Instead of identifying and learning the 471 00:29:32,800 --> 00:29:35,440 Speaker 1: spot tactics used to make our social media communities less 472 00:29:35,440 --> 00:29:37,920 Speaker 1: safe and less stable, the powers that be just let 473 00:29:37,920 --> 00:29:41,760 Speaker 1: it happen again and again and again. I asked Shafika 474 00:29:41,840 --> 00:29:43,480 Speaker 1: if she thinks that if someone had listened to black 475 00:29:43,480 --> 00:29:46,440 Speaker 1: women when they spoke up about being targeted online, things 476 00:29:46,520 --> 00:29:47,080 Speaker 1: might be different. 477 00:29:47,080 --> 00:29:48,960 Speaker 2: Now it's a tough question for her. 478 00:29:55,000 --> 00:29:57,680 Speaker 3: This is always going to be a question that kind 479 00:29:57,680 --> 00:30:03,240 Speaker 3: of hangs in my mind because while I understand the 480 00:30:03,320 --> 00:30:09,840 Speaker 3: black voters were absolutely targeted, I'm not entirely sure that 481 00:30:10,400 --> 00:30:14,480 Speaker 3: we were fooled. Do you know what I mean? Right? Like, 482 00:30:15,120 --> 00:30:18,600 Speaker 3: honestly because it seems like to me, we kind of 483 00:30:18,680 --> 00:30:22,200 Speaker 3: all got out and voted anyway. And it also seems 484 00:30:22,240 --> 00:30:25,000 Speaker 3: like to me, Donald Trump may have lost the popular 485 00:30:25,120 --> 00:30:27,600 Speaker 3: vote by three million posts, but that's neither here nor there. 486 00:30:27,680 --> 00:30:29,480 Speaker 3: I get not if you ask him, he didn't he 487 00:30:29,960 --> 00:30:32,800 Speaker 3: but we don't ask him things because we like honest answers. 488 00:30:33,160 --> 00:30:37,400 Speaker 3: But yeah, I mean, just the fact that this happened, 489 00:30:38,960 --> 00:30:47,640 Speaker 3: like it left us arguably vulnerable, and that even even 490 00:30:47,680 --> 00:30:51,800 Speaker 3: though I'm not sure how ultimately successful successful, it was 491 00:30:51,840 --> 00:30:55,640 Speaker 3: just the fact that we had foreign agents targeting voting 492 00:30:55,680 --> 00:31:01,360 Speaker 3: populations in the United States of America should have been 493 00:31:02,040 --> 00:31:07,360 Speaker 3: serious and due cause for alarm, because even if it 494 00:31:07,360 --> 00:31:09,560 Speaker 3: doesn't work, it's like just it's just the fact that 495 00:31:09,600 --> 00:31:13,120 Speaker 3: they tried and that they could. What do you do it? Like, 496 00:31:13,360 --> 00:31:18,000 Speaker 3: can we get it together? It's because we left we 497 00:31:18,080 --> 00:31:21,880 Speaker 3: left the door open like this. This was that was 498 00:31:21,920 --> 00:31:25,200 Speaker 3: a failure. That was I don't want to say it 499 00:31:25,240 --> 00:31:27,240 Speaker 3: was on me, because I feel like it definitely wasn't 500 00:31:27,280 --> 00:31:29,800 Speaker 3: on me, and it definitely wasn't on you, But it 501 00:31:29,840 --> 00:31:38,360 Speaker 3: was it was a failure on the side of whatever 502 00:31:38,520 --> 00:31:43,800 Speaker 3: agents are supposed to be protecting us. And I guess 503 00:31:43,800 --> 00:31:48,360 Speaker 3: that offers. That opens up a lot for speculations like, well, 504 00:31:48,720 --> 00:31:53,440 Speaker 3: you know who's who's looking after us now? But yeah, 505 00:31:53,520 --> 00:31:58,400 Speaker 3: that that was a glaring example of just kind of 506 00:31:58,440 --> 00:32:02,719 Speaker 3: the general failure. Who addressed something that did not have 507 00:32:02,800 --> 00:32:04,600 Speaker 3: to get as big as a gout. 508 00:32:05,280 --> 00:32:07,040 Speaker 1: More, there are no girls on the internet after this 509 00:32:07,200 --> 00:32:20,160 Speaker 1: quick break. The twenty twenty presidential election is one hundred 510 00:32:20,160 --> 00:32:23,880 Speaker 1: and twelve days away. Digital security experts agree that American 511 00:32:23,920 --> 00:32:26,560 Speaker 1: elections are vulnerable and not enough it's being done about it. 512 00:32:26,960 --> 00:32:30,720 Speaker 1: During Trump's impeachment hearing, Fiona Hill, the former National Security 513 00:32:30,720 --> 00:32:34,440 Speaker 1: Council advisor specializing in Russia and European affairs, said. 514 00:32:34,160 --> 00:32:37,720 Speaker 2: Right now Russia's security services and their proxies are geared 515 00:32:37,800 --> 00:32:40,440 Speaker 2: up to repeat their interference in the twenty twenty election. 516 00:32:40,680 --> 00:32:42,560 Speaker 3: We're running out of time to stop them. 517 00:32:42,840 --> 00:32:45,320 Speaker 2: So what do we do? Doctor Donovan says. 518 00:32:45,840 --> 00:32:48,440 Speaker 4: As we get closer to the election, we know that 519 00:32:49,600 --> 00:32:53,520 Speaker 4: all different kinds of tactics are going to get utilized, 520 00:32:53,600 --> 00:32:58,160 Speaker 4: including potentially deep fakes or cheap fakes, like manipulated video, 521 00:32:58,320 --> 00:33:05,360 Speaker 4: manipulated audio. We're going to see probably clips of people 522 00:33:05,960 --> 00:33:08,360 Speaker 4: quoted out of context. We've seen this happen to Joe 523 00:33:08,360 --> 00:33:11,120 Speaker 4: Biden a few different times. Then, of course we've seen 524 00:33:11,200 --> 00:33:15,800 Speaker 4: gas that these done are completely within context and a problem. 525 00:33:16,960 --> 00:33:19,720 Speaker 4: You know, you can't forget that every once in a 526 00:33:19,760 --> 00:33:21,640 Speaker 4: while you're watching it and you're like, this is this 527 00:33:21,720 --> 00:33:25,080 Speaker 4: can't be real and it's totally real. Well, what's crazy 528 00:33:25,120 --> 00:33:27,920 Speaker 4: to me about it is as a researcher, you're supposed 529 00:33:27,920 --> 00:33:29,480 Speaker 4: to be attuned to all of this, but I still 530 00:33:29,520 --> 00:33:31,640 Speaker 4: get fooled here and there. But the last thing I'll 531 00:33:31,680 --> 00:33:34,160 Speaker 4: say about the way in which I think platform companies 532 00:33:34,240 --> 00:33:40,280 Speaker 4: need to better serve our political elections and the integrity 533 00:33:40,320 --> 00:33:43,800 Speaker 4: of elections is that they need to hire some serious specialists. 534 00:33:44,160 --> 00:33:49,000 Speaker 4: They need to hire a whole army of librarians to 535 00:33:49,040 --> 00:33:53,840 Speaker 4: do content curations so that when people are looking for information, 536 00:33:54,040 --> 00:33:56,720 Speaker 4: they find things that have been fact checked that are 537 00:33:56,760 --> 00:33:59,480 Speaker 4: true and correct. I think that we have a right 538 00:33:59,560 --> 00:34:02,600 Speaker 4: to true and part of the problem is the way 539 00:34:02,600 --> 00:34:05,880 Speaker 4: in which these algorithms are designed is to bring up 540 00:34:05,920 --> 00:34:09,360 Speaker 4: things that are quote unquote fresh and relevant. And the 541 00:34:09,440 --> 00:34:13,040 Speaker 4: problem with fresh and relevant content is that disinformation is 542 00:34:13,640 --> 00:34:18,319 Speaker 4: usually incredibly popular because there are people trying to push 543 00:34:18,360 --> 00:34:20,839 Speaker 4: it and there are people trying to dispute it, and 544 00:34:20,920 --> 00:34:23,799 Speaker 4: so as a result, it rises generally to the top 545 00:34:23,840 --> 00:34:28,000 Speaker 4: of search algorithms or trending algorithms very quickly because of 546 00:34:28,040 --> 00:34:29,920 Speaker 4: that that feature. 547 00:34:30,680 --> 00:34:32,560 Speaker 2: But will Twitter actually do any of that? 548 00:34:33,320 --> 00:34:36,080 Speaker 1: Shapika isn't super confident that the platform will do anything 549 00:34:36,120 --> 00:34:39,000 Speaker 1: at all. I haven't even thought about it, and I 550 00:34:39,000 --> 00:34:44,799 Speaker 1: guess that's sort of a reflection on my general skepticism 551 00:34:46,000 --> 00:34:50,120 Speaker 1: right now with not their ability but their willingness to 552 00:34:50,280 --> 00:34:55,799 Speaker 1: address this. I have a good friend who's said one 553 00:34:55,840 --> 00:34:59,040 Speaker 1: of the smartest things I've ever heard anybody say, and 554 00:34:59,080 --> 00:35:02,399 Speaker 1: I quote it all the time, but he said, when 555 00:35:02,440 --> 00:35:05,560 Speaker 1: things look like they're not working out, you can always 556 00:35:05,600 --> 00:35:07,239 Speaker 1: trust that they're working out for somebody. 557 00:35:09,320 --> 00:35:15,440 Speaker 3: And I'm going to lead that right there. But it 558 00:35:15,440 --> 00:35:20,960 Speaker 3: looks like you know things aren't working, you start asking 559 00:35:21,040 --> 00:35:24,040 Speaker 3: questions and as a whole rabbit hole. 560 00:35:26,880 --> 00:35:29,560 Speaker 1: That's the thing about the Internet. There's so much darkness 561 00:35:29,600 --> 00:35:31,560 Speaker 1: lurking in its corner. It's just waiting to spill out. 562 00:35:32,760 --> 00:35:35,879 Speaker 1: Or there's darkness, there's light too, or there's someone being 563 00:35:36,040 --> 00:35:38,440 Speaker 1: ugly online, there's someone else reaching out to make a 564 00:35:38,480 --> 00:35:42,240 Speaker 1: genuine connection. There's real community to be built and laughs 565 00:35:42,239 --> 00:35:44,440 Speaker 1: to be had, the kind of laughs that can sustain 566 00:35:44,480 --> 00:35:45,520 Speaker 1: you through difficult times. 567 00:35:46,600 --> 00:35:48,680 Speaker 2: Being online is a constant. 568 00:35:48,200 --> 00:35:51,160 Speaker 1: Tight rope walk of acknowledging that darkness while still being 569 00:35:51,200 --> 00:35:54,680 Speaker 1: able to see the corners of light peeking through, and 570 00:35:54,760 --> 00:35:57,520 Speaker 1: even while wading through all of that darkness and ugliness. 571 00:35:58,040 --> 00:36:01,840 Speaker 1: It's the light that is really sustained Shaffika, after everything 572 00:36:01,880 --> 00:36:04,360 Speaker 1: she's been through, she's still grateful for Twitter as a 573 00:36:04,360 --> 00:36:06,600 Speaker 1: platform and all the good things that's brought to her life. 574 00:36:07,520 --> 00:36:12,600 Speaker 3: Honestly, it really helps that I have a strong and 575 00:36:12,640 --> 00:36:18,960 Speaker 3: supportive community, both online and off. I've really am super 576 00:36:19,680 --> 00:36:23,719 Speaker 3: grateful for Twitter for so many reasons, not the least 577 00:36:23,719 --> 00:36:27,480 Speaker 3: of which is because it's helped me expand my network, 578 00:36:28,400 --> 00:36:34,040 Speaker 3: and I've met amazing people and connected of people who 579 00:36:34,160 --> 00:36:37,680 Speaker 3: are like me people who aren't like me, and gotten 580 00:36:37,760 --> 00:36:41,120 Speaker 3: to know so much about them and learn about their 581 00:36:41,160 --> 00:36:46,120 Speaker 3: lived experiences. And that has saved me because it helps 582 00:36:46,160 --> 00:36:51,080 Speaker 3: me kind of get out of my own headspace or likewise, 583 00:36:51,120 --> 00:36:53,520 Speaker 3: you know, connect with people who understand one hundred percent 584 00:36:53,560 --> 00:36:59,440 Speaker 3: where I'm coming from. And that's in a world where 585 00:36:59,680 --> 00:37:05,880 Speaker 3: you know, we're frequently gas lit about the things that 586 00:37:05,960 --> 00:37:11,120 Speaker 3: we see and experience. That is absolutely invaluable. 587 00:37:11,960 --> 00:37:13,320 Speaker 2: Oh and one more thing that helps. 588 00:37:13,719 --> 00:37:19,959 Speaker 3: It also helps that I'm funny, honestly, having a sense 589 00:37:19,960 --> 00:37:23,360 Speaker 3: of humor and a wit of We'll get you through 590 00:37:25,360 --> 00:37:29,680 Speaker 3: pretty much. I don't want to say pretty much anything, 591 00:37:29,680 --> 00:37:31,840 Speaker 3: but how about this. It's gotten me through pretty. 592 00:37:31,600 --> 00:37:36,000 Speaker 1: Much everything, and you've been through some stuff. 593 00:37:36,560 --> 00:37:38,480 Speaker 3: I've been through it. 594 00:37:41,719 --> 00:37:43,520 Speaker 1: There are No Girls on the Internet. What's created by 595 00:37:43,520 --> 00:37:47,040 Speaker 1: me Frigetad. It's a production of iHeartRadio and Unbossed Creative 596 00:37:47,360 --> 00:37:50,359 Speaker 1: Jonathan Stricklan is our executive producer. Tari Harrison is our 597 00:37:50,360 --> 00:37:53,760 Speaker 1: producer and sound engineer. Michael Amato is our contributing producer. 598 00:37:54,000 --> 00:37:57,239 Speaker 1: I'm your host, Brigittad. For more podcasts from iHeart, check 599 00:37:57,239 --> 00:37:59,520 Speaker 1: out the iHeartRadio app, Apple Podcasts, or wherever you get 600 00:37:59,520 --> 00:38:04,560 Speaker 1: your podcast us. Thanks so much for listening to their 601 00:38:04,600 --> 00:38:06,399 Speaker 1: No Girls on the Internet. If you want to help 602 00:38:06,400 --> 00:38:09,360 Speaker 1: our podcast grow, rate and review us on Apple Podcasts.