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