1 00:00:00,200 --> 00:00:03,560 Speaker 1: Just a quick heads up. This episode talks about sexual violence. 2 00:00:07,280 --> 00:00:10,319 Speaker 1: You're listening to Disinformed, a mini series from There Are 3 00:00:10,320 --> 00:00:17,080 Speaker 1: No Girls on the Internet. I'm Bridget Todd. When I 4 00:00:17,120 --> 00:00:19,400 Speaker 1: was first coming of age online, we talked about the 5 00:00:19,440 --> 00:00:22,960 Speaker 1: Internet and the real world as two totally different spheres. 6 00:00:23,360 --> 00:00:25,119 Speaker 1: The internet was where you want to escape from the 7 00:00:25,200 --> 00:00:28,080 Speaker 1: real world. They were not connected. But by now it's 8 00:00:28,120 --> 00:00:30,040 Speaker 1: easy to see that that is no longer the case. 9 00:00:30,720 --> 00:00:33,080 Speaker 1: What happens online is what happens in the real world, 10 00:00:33,400 --> 00:00:36,199 Speaker 1: and this is especially true for younger folks. So what 11 00:00:36,320 --> 00:00:39,040 Speaker 1: happens if this generation is immersed in an Internet that's 12 00:00:39,040 --> 00:00:44,000 Speaker 1: serving them up increasingly extremest content. In twelve, feminist activist 13 00:00:44,120 --> 00:00:47,960 Speaker 1: Laura Bates created the Everyday Sexism Project, a database catalog 14 00:00:47,960 --> 00:00:51,200 Speaker 1: and women's experiences with sexism from around the world. After 15 00:00:51,320 --> 00:00:55,040 Speaker 1: she was targeted by misogynists online, she went undercover to 16 00:00:55,080 --> 00:00:58,920 Speaker 1: investigate the unseen, organized movement of thousands of men wishing 17 00:00:59,000 --> 00:01:02,319 Speaker 1: violence and worse on women, the terrorism that we're not 18 00:01:02,400 --> 00:01:05,600 Speaker 1: really talking about. She found that these men are taking 19 00:01:05,600 --> 00:01:09,279 Speaker 1: advantage of social media algorithms to recruit and radicalize boys 20 00:01:09,360 --> 00:01:12,880 Speaker 1: into a world of racism and misogyny. In online spaces 21 00:01:12,959 --> 00:01:16,520 Speaker 1: that are sometimes called the meno sphere. Her book Men 22 00:01:16,560 --> 00:01:19,160 Speaker 1: Who Hate Women chronicles what she learned about how it 23 00:01:19,200 --> 00:01:22,600 Speaker 1: operates and why we should all be paying attention. Well, 24 00:01:22,720 --> 00:01:26,800 Speaker 1: I think, as a woman who writes and speaks about feminism, 25 00:01:26,920 --> 00:01:31,000 Speaker 1: especially online, I was already aware of these communities. I 26 00:01:31,040 --> 00:01:34,600 Speaker 1: think that for most women in particularly feminists, more so 27 00:01:34,760 --> 00:01:37,800 Speaker 1: for trans women, for women of color online, these men 28 00:01:37,880 --> 00:01:40,080 Speaker 1: come to you, they make themselves known to you in 29 00:01:40,319 --> 00:01:42,840 Speaker 1: online abuse. So I've been aware of them for a 30 00:01:42,880 --> 00:01:45,880 Speaker 1: long time, these men and members of communities who have 31 00:01:46,000 --> 00:01:48,600 Speaker 1: been sending me perhaps two hundred rape and death threats 32 00:01:48,600 --> 00:01:51,760 Speaker 1: a day from the best part of the last ten years. 33 00:01:51,800 --> 00:01:53,960 Speaker 1: But for a very long time, there was an argument 34 00:01:53,960 --> 00:01:57,000 Speaker 1: in feminist communities that we shouldn't give them the oxygen 35 00:01:57,040 --> 00:01:59,280 Speaker 1: of publicity, and that was an argument I was I 36 00:01:59,320 --> 00:02:02,960 Speaker 1: was sympathetic to, I largely agreed with. But something changed 37 00:02:03,000 --> 00:02:05,160 Speaker 1: for me. I do a lot of work in schools 38 00:02:05,240 --> 00:02:08,040 Speaker 1: with young people, talking and working with young people around 39 00:02:08,040 --> 00:02:14,240 Speaker 1: sexual consent, healthy relationships, gender stereotypes, and suddenly, after visiting 40 00:02:14,280 --> 00:02:17,359 Speaker 1: perhaps two schools a week on average for about nine years, 41 00:02:17,440 --> 00:02:20,240 Speaker 1: in the last two years, there was a really marked 42 00:02:20,280 --> 00:02:23,359 Speaker 1: shift in the responses I was seeing from boys at schools. 43 00:02:23,919 --> 00:02:27,840 Speaker 1: And don't get me wrong, those conversations have always been awkward, 44 00:02:28,000 --> 00:02:32,200 Speaker 1: sometimes stilted, sometimes challenging, but they had also generally been 45 00:02:32,320 --> 00:02:35,280 Speaker 1: very rewarding conversations. And suddenly what I was finding was 46 00:02:35,320 --> 00:02:39,200 Speaker 1: that boys were showing up with a very determined resistance 47 00:02:39,280 --> 00:02:42,800 Speaker 1: to engaging at all. They were showing up already with 48 00:02:42,960 --> 00:02:46,200 Speaker 1: very extreme views about women and about feminism, that I 49 00:02:46,280 --> 00:02:48,799 Speaker 1: was a feminati bitch coming in to tell them why 50 00:02:48,800 --> 00:02:52,640 Speaker 1: I hated men, and they were showing up with misconceptions, 51 00:02:52,680 --> 00:02:56,639 Speaker 1: so really believing very deeply that there is a huge 52 00:02:56,680 --> 00:02:59,800 Speaker 1: feminist conspiracy at the heart of our government undermining white 53 00:02:59,800 --> 00:03:03,000 Speaker 1: men who are the real victims of today's society, quoting 54 00:03:03,000 --> 00:03:06,840 Speaker 1: the same full statistics about false rape allegations being rife, 55 00:03:06,880 --> 00:03:09,760 Speaker 1: about men being the majority of victims of domestic abuse, 56 00:03:10,280 --> 00:03:12,240 Speaker 1: And at that point I suddenly joined the dots and 57 00:03:12,280 --> 00:03:16,040 Speaker 1: realized that these boys were being radicalized by exposure to 58 00:03:16,120 --> 00:03:19,080 Speaker 1: the extremist online communities that I've been aware of as 59 00:03:19,080 --> 00:03:21,919 Speaker 1: a feminist for a long time. At that point, I thought, 60 00:03:21,919 --> 00:03:25,360 Speaker 1: if these groups are managing to reach teenage boys in 61 00:03:25,400 --> 00:03:29,359 Speaker 1: a quite scary and pervasive way with their online propaganda, 62 00:03:29,760 --> 00:03:32,800 Speaker 1: then actually perhaps not giving them the oxygen of publicity 63 00:03:32,840 --> 00:03:35,640 Speaker 1: isn't working. Perhaps it's actually allowing them to operate under 64 00:03:35,680 --> 00:03:38,920 Speaker 1: the radar and to have a quite major and worrying 65 00:03:39,000 --> 00:03:43,080 Speaker 1: impact without parents or educators or wider society having much 66 00:03:43,120 --> 00:03:45,640 Speaker 1: idea that these communities exist at all. And that was 67 00:03:45,720 --> 00:03:47,760 Speaker 1: what really pushed me into wanting to write the book, 68 00:03:47,760 --> 00:03:50,880 Speaker 1: because I felt that suddenly these communities were posing a 69 00:03:51,000 --> 00:03:55,240 Speaker 1: very real offline threat in terms of that grooming radicalization, 70 00:03:55,280 --> 00:03:57,840 Speaker 1: which of course we'd never use those terms to describe 71 00:03:58,080 --> 00:04:00,640 Speaker 1: when it's white men doing it, but also in terms 72 00:04:00,640 --> 00:04:02,840 Speaker 1: of, of of course their attacks, the fact that these men 73 00:04:02,840 --> 00:04:06,160 Speaker 1: were increasingly carrying out violent attacks in real life, and 74 00:04:06,240 --> 00:04:08,200 Speaker 1: yet many people didn't know that this was a form 75 00:04:08,240 --> 00:04:12,560 Speaker 1: of extremism that even existed. Yeah, I mean, you're that 76 00:04:12,680 --> 00:04:14,360 Speaker 1: is so true. It's actually one of the things that 77 00:04:14,440 --> 00:04:17,880 Speaker 1: kind of prompted me to start this podcast was, you know, 78 00:04:17,920 --> 00:04:19,440 Speaker 1: I think there had been a mass shooting in the 79 00:04:19,560 --> 00:04:22,359 Speaker 1: United States, and I thought, you know, just like in 80 00:04:22,480 --> 00:04:24,760 Speaker 1: many of the cases of mass violence, this person who 81 00:04:24,839 --> 00:04:28,080 Speaker 1: was the perpetrator had a history of domestic violence, allegations, 82 00:04:28,120 --> 00:04:30,240 Speaker 1: had a history of women speaking up and saying, hey, 83 00:04:30,240 --> 00:04:32,600 Speaker 1: this is someone who's been harassing me. But of course 84 00:04:32,640 --> 00:04:35,800 Speaker 1: we know those kinds of uh warning signs are often 85 00:04:35,839 --> 00:04:38,400 Speaker 1: ignored because it's women speaking up. And I thought, Wow, 86 00:04:38,440 --> 00:04:41,680 Speaker 1: if only we kind of took seriously the experiences folks 87 00:04:41,680 --> 00:04:44,919 Speaker 1: were having online and uh really thought about that seriously, 88 00:04:44,960 --> 00:04:48,160 Speaker 1: some of these mass incidents of violence could be prevented. 89 00:04:48,200 --> 00:04:51,080 Speaker 1: And so I'm happy to see you sort of digging 90 00:04:51,120 --> 00:04:53,599 Speaker 1: into that work. I'm wondering, could you tell me a 91 00:04:53,600 --> 00:04:55,920 Speaker 1: bit more about some of the ways that you've seen 92 00:04:56,160 --> 00:05:00,800 Speaker 1: young men be targeted, radicalized and sort sucked into these 93 00:05:00,800 --> 00:05:04,760 Speaker 1: online spaces that teach them some pretty tax it stuff. Yeah. Absolutely, 94 00:05:04,800 --> 00:05:09,839 Speaker 1: it's very sophisticated. They have extremely well honed radicalization techniques 95 00:05:10,360 --> 00:05:12,360 Speaker 1: and they're very open about it. So these men will 96 00:05:12,440 --> 00:05:16,320 Speaker 1: share their recruitment strategies with each other quite openly in 97 00:05:16,360 --> 00:05:21,320 Speaker 1: their online communities. They talk specifically about using memes, using 98 00:05:21,640 --> 00:05:26,360 Speaker 1: the cultural delivery methods cultural touch points funny Instagram means 99 00:05:26,640 --> 00:05:31,080 Speaker 1: viral YouTube videos, taking advantages of algorithms on platforms like 100 00:05:31,200 --> 00:05:35,240 Speaker 1: YouTube that will automatically direct young people to increasingly extreme content, 101 00:05:35,800 --> 00:05:39,440 Speaker 1: and they describe these delivery systems as in their own words, 102 00:05:39,480 --> 00:05:44,040 Speaker 1: adding cherry flavor to children's medicine. They deliberately target boys 103 00:05:44,040 --> 00:05:46,320 Speaker 1: as young as eleven, and they don't wait for boys 104 00:05:46,360 --> 00:05:48,680 Speaker 1: to come to them in their communities, that's quite a 105 00:05:48,720 --> 00:05:52,360 Speaker 1: common misconception. They find boys where boys are, so they 106 00:05:52,400 --> 00:05:56,280 Speaker 1: are in online gaming there in gaming strategy chat rooms. 107 00:05:56,720 --> 00:06:00,240 Speaker 1: They're showing up a lot on bodybuilding forums, which is 108 00:06:00,240 --> 00:06:02,559 Speaker 1: a very clever space for them to operate in because, 109 00:06:02,560 --> 00:06:04,840 Speaker 1: of course there they have a ready made group of 110 00:06:04,880 --> 00:06:08,560 Speaker 1: young men already um sort of self selecting as having 111 00:06:08,760 --> 00:06:12,120 Speaker 1: preoccupations with traditionally masculine ways of showing up in the 112 00:06:12,160 --> 00:06:15,800 Speaker 1: world and portraying themselves. So they are very clever about 113 00:06:15,800 --> 00:06:19,080 Speaker 1: finding these spaces where boys might be isolated and vulnerable 114 00:06:19,160 --> 00:06:23,200 Speaker 1: online and quite mainstream spaces, and then they groom them 115 00:06:23,279 --> 00:06:25,599 Speaker 1: very carefully and very gradually. So at first it's just 116 00:06:25,680 --> 00:06:28,320 Speaker 1: a fu misogynistic jokes here and there, and then if 117 00:06:28,360 --> 00:06:30,480 Speaker 1: a boy seems to respond to that, then maybe it's 118 00:06:30,480 --> 00:06:32,680 Speaker 1: a link to a pick up artist website or to 119 00:06:32,760 --> 00:06:35,520 Speaker 1: a YouTube video, and maybe from there it kind of 120 00:06:35,560 --> 00:06:38,800 Speaker 1: gets very gradually further down the rabbit hole, and they 121 00:06:38,960 --> 00:06:43,600 Speaker 1: very explicitly see recruiting boys into anti feminism and misogyny 122 00:06:43,640 --> 00:06:46,640 Speaker 1: as a gateway to white supremacy and near Nazism. So 123 00:06:46,760 --> 00:06:49,880 Speaker 1: they are seeing it as a kind of pathway for radicalization. 124 00:06:50,279 --> 00:06:52,600 Speaker 1: And some really interesting studies have been done that really 125 00:06:52,640 --> 00:06:55,400 Speaker 1: back that up. That many men who end up playing 126 00:06:55,640 --> 00:06:58,919 Speaker 1: pivotal roles in the white supremacist movement, for example, and 127 00:06:59,000 --> 00:07:01,600 Speaker 1: men who have made their name and kind of created 128 00:07:01,640 --> 00:07:06,320 Speaker 1: a following and cultivated particular skills of online abuse in 129 00:07:06,520 --> 00:07:10,440 Speaker 1: anti feminist movements like and Father's Day or Gaming Gates. 130 00:07:10,880 --> 00:07:12,680 Speaker 1: I'm not surprised here when you say this at all. 131 00:07:12,760 --> 00:07:15,960 Speaker 1: I know, after the insurrection at the Capitol in January, 132 00:07:16,480 --> 00:07:19,720 Speaker 1: so many of the men who were there and involved 133 00:07:20,160 --> 00:07:22,440 Speaker 1: were really well known to people. You know, they were 134 00:07:22,520 --> 00:07:26,400 Speaker 1: they had these big online personas, oftentimes like live streaming 135 00:07:26,400 --> 00:07:29,000 Speaker 1: what they did, and um a lot of them had 136 00:07:29,160 --> 00:07:33,640 Speaker 1: prior domestic violence and harassment of women allegations against them. 137 00:07:33,640 --> 00:07:36,920 Speaker 1: And so I do think it's difficult but but important 138 00:07:36,960 --> 00:07:40,400 Speaker 1: to demonstrate the ways that white supremacy and misogyny are 139 00:07:40,400 --> 00:07:42,640 Speaker 1: sort of what it's like that then diagram as a circle, 140 00:07:42,720 --> 00:07:44,960 Speaker 1: you know, it's it's one and the same, and they're 141 00:07:45,040 --> 00:07:48,360 Speaker 1: kind of very much linked. Absolutely, this came out so 142 00:07:48,440 --> 00:07:51,480 Speaker 1: clearly in researching the book. There's this public perception that 143 00:07:51,480 --> 00:07:54,520 Speaker 1: we're talking about these two separate groups, these two separate problems, 144 00:07:54,840 --> 00:07:57,560 Speaker 1: But in every way when you research this, you realize 145 00:07:57,600 --> 00:08:00,240 Speaker 1: that that's not right, that this is this is of 146 00:08:00,280 --> 00:08:03,480 Speaker 1: the same whole. So you're seeing, for example, the lexicon, 147 00:08:03,640 --> 00:08:06,240 Speaker 1: the kind of unique language of the manisphere, and male 148 00:08:06,280 --> 00:08:09,560 Speaker 1: supremacist communities showing up very much as part of the 149 00:08:09,640 --> 00:08:12,880 Speaker 1: language of white supremacy, the right, far right communities. You 150 00:08:12,880 --> 00:08:14,880 Speaker 1: can see it there in the language, but you can 151 00:08:14,920 --> 00:08:17,280 Speaker 1: see it also in the kind of fundamental roots of 152 00:08:17,280 --> 00:08:20,320 Speaker 1: their ideology in cells and not just furious that women 153 00:08:20,360 --> 00:08:23,320 Speaker 1: aren't sleeping with them, they're furious if women are sleeping 154 00:08:23,320 --> 00:08:25,880 Speaker 1: with non white men instead of them. That's what they 155 00:08:25,920 --> 00:08:28,360 Speaker 1: see to be something that absolutely outrages and that's a 156 00:08:28,400 --> 00:08:31,720 Speaker 1: repeated focus that they come back to. And white supremacists, 157 00:08:31,800 --> 00:08:35,880 Speaker 1: of course, are also focused obsessed with misogynistic issues. You know, 158 00:08:35,920 --> 00:08:38,640 Speaker 1: they're obsessed with the idea of birthrates, with the idea 159 00:08:38,679 --> 00:08:42,959 Speaker 1: of so called replacement theory. They obsess over keeping white 160 00:08:42,960 --> 00:08:46,200 Speaker 1: women in sexual slavery to breed a so called master race. 161 00:08:46,440 --> 00:08:50,240 Speaker 1: They're obsessed with the forced sterilization of women of color. 162 00:08:50,400 --> 00:08:53,880 Speaker 1: They talk about, for example, the idea that they see 163 00:08:53,880 --> 00:08:57,520 Speaker 1: white women as a kind of dehumanized, fragile commodity that 164 00:08:57,600 --> 00:09:02,200 Speaker 1: they see as being plundered by kind of invading immigrant men. 165 00:09:02,640 --> 00:09:04,720 Speaker 1: So very clearly, if you can look at that not 166 00:09:04,800 --> 00:09:07,080 Speaker 1: see misogyny, then you're only seeing half of the picture. 167 00:09:07,120 --> 00:09:08,680 Speaker 1: In the same way, if you couldn't look at what's 168 00:09:08,679 --> 00:09:10,560 Speaker 1: going on with in cells and the manisphere and not 169 00:09:10,640 --> 00:09:14,240 Speaker 1: recognized the racism inherent in that, then it really holds 170 00:09:14,320 --> 00:09:17,560 Speaker 1: us back, I think, from meaningfully tackling the problem. Yeah, 171 00:09:17,600 --> 00:09:19,240 Speaker 1: I would love to talk a little bit more about that. 172 00:09:19,320 --> 00:09:23,040 Speaker 1: You mentioned earlier that you look at platforms like YouTube, 173 00:09:23,200 --> 00:09:25,880 Speaker 1: sometimes they're serving up these ads, you know, through their 174 00:09:25,920 --> 00:09:28,600 Speaker 1: algorithm or through their recommended videos, I know through you know, 175 00:09:28,600 --> 00:09:31,320 Speaker 1: the research I've done for this show that Facebook's own 176 00:09:31,559 --> 00:09:35,520 Speaker 1: um own reports show internally that most times I think 177 00:09:35,520 --> 00:09:37,080 Speaker 1: it was eighty six percent of the time when somebody 178 00:09:37,200 --> 00:09:40,160 Speaker 1: joined the extremist group, it's because Facebook's algorithm has recommended 179 00:09:40,160 --> 00:09:42,240 Speaker 1: it for them. Um. I guess my question to you 180 00:09:42,440 --> 00:09:46,560 Speaker 1: is what do you think the responsibility of social media 181 00:09:46,600 --> 00:09:49,720 Speaker 1: platforms and platforms like YouTube? What role should they be 182 00:09:49,800 --> 00:09:53,400 Speaker 1: playing in curbing this kind of radicalization and men, well, 183 00:09:53,440 --> 00:09:55,480 Speaker 1: I think first of all, they and we have to 184 00:09:55,520 --> 00:09:58,880 Speaker 1: recognize their influence because I think for adults, they think 185 00:09:58,880 --> 00:10:01,600 Speaker 1: of YouTube in particular as the kind of benign home 186 00:10:01,679 --> 00:10:04,480 Speaker 1: to movie trailers and grumpy cap videos, and there's a 187 00:10:04,480 --> 00:10:06,880 Speaker 1: real lack of awareness that they have this pivotal role 188 00:10:06,960 --> 00:10:11,400 Speaker 1: that of US teams use YouTube, that they say explicitly 189 00:10:11,400 --> 00:10:13,760 Speaker 1: that it's where they're getting their news from as well, 190 00:10:14,160 --> 00:10:18,160 Speaker 1: and that YouTube has this this massive kind of stranglehold, 191 00:10:18,160 --> 00:10:20,760 Speaker 1: if you like. There are one point five billion YouTube 192 00:10:20,840 --> 00:10:22,560 Speaker 1: uses in the world, so that's more than the number 193 00:10:22,600 --> 00:10:26,240 Speaker 1: of households that own televisions, and YouTube accounts for thirty 194 00:10:26,280 --> 00:10:30,680 Speaker 1: seven percent of mobile internet traffic internationally, and of the 195 00:10:30,720 --> 00:10:33,600 Speaker 1: YouTube videos that are watched are recommended to users by 196 00:10:33,600 --> 00:10:36,480 Speaker 1: the algorithm. So if you put those two statistics together, 197 00:10:36,520 --> 00:10:40,280 Speaker 1: you realize that a quarter of all international mobile traffic 198 00:10:40,800 --> 00:10:44,440 Speaker 1: is accounted for by this one algorithm designed by a 199 00:10:44,520 --> 00:10:49,319 Speaker 1: workforce that's sixty percent male and five percent black being 200 00:10:49,360 --> 00:10:52,240 Speaker 1: being telling people what to watch. And so suddenly that 201 00:10:52,320 --> 00:10:55,280 Speaker 1: becomes a power that should be interrogated, that should be 202 00:10:55,320 --> 00:10:58,280 Speaker 1: held to account, that should be accountable for the impact 203 00:10:58,320 --> 00:11:01,120 Speaker 1: it's having, and what I find really frustrating is that 204 00:11:01,120 --> 00:11:03,920 Speaker 1: there tends to be this tendency from so social media 205 00:11:03,960 --> 00:11:06,400 Speaker 1: companies to go, hey, this is so difficult. You know, 206 00:11:06,520 --> 00:11:09,080 Speaker 1: this is a huge thing. There's not much we can do. 207 00:11:09,160 --> 00:11:12,240 Speaker 1: The algorithm wasn't built to do this, and of course 208 00:11:12,280 --> 00:11:14,160 Speaker 1: I'm sure that's true, but I don't think it's a 209 00:11:14,160 --> 00:11:16,560 Speaker 1: coincidence either that it was built by a community of 210 00:11:16,559 --> 00:11:19,120 Speaker 1: people that is not particularly diverse and that didn't foresee 211 00:11:19,160 --> 00:11:22,160 Speaker 1: these issues happening. The argument that we can't hold them 212 00:11:22,200 --> 00:11:24,720 Speaker 1: accountable and that there's simply nothing they can do because 213 00:11:24,720 --> 00:11:27,880 Speaker 1: it's too difficult, it's absolute nonsense. When you're talking about 214 00:11:27,920 --> 00:11:30,680 Speaker 1: companies that have an income that's bigger than the income 215 00:11:30,720 --> 00:11:34,040 Speaker 1: of some small countries. Of course they have the money 216 00:11:34,040 --> 00:11:36,440 Speaker 1: to tackle this, right if they really cared, if it 217 00:11:36,520 --> 00:11:40,040 Speaker 1: was a priority, they could hire twenty thousand human moderators 218 00:11:40,080 --> 00:11:43,640 Speaker 1: and upscale them and train them in coordination with specific 219 00:11:43,720 --> 00:11:47,000 Speaker 1: community groups who are affected by these issues. There's a 220 00:11:47,080 --> 00:11:49,520 Speaker 1: lack of will there. We have to acknowledge that that 221 00:11:49,520 --> 00:11:52,520 Speaker 1: that's what's going on. Yeah, something that you brought up 222 00:11:52,559 --> 00:11:55,320 Speaker 1: that I really harp on a lot, and I think 223 00:11:55,400 --> 00:11:59,439 Speaker 1: it's complicated, but I think it's really important to understand. 224 00:12:00,040 --> 00:12:01,959 Speaker 1: I talk a lot on this show about the importance 225 00:12:02,000 --> 00:12:06,559 Speaker 1: of having inclusive teams in tech spaces and why underrepresented folks, 226 00:12:06,559 --> 00:12:09,600 Speaker 1: so women, transpolkes, queer folks, people of color, black folks, 227 00:12:09,840 --> 00:12:13,680 Speaker 1: all need to feel like it's our do like tech 228 00:12:13,760 --> 00:12:15,520 Speaker 1: is our domain, Like we need to be able to 229 00:12:15,559 --> 00:12:18,200 Speaker 1: take up space in those in those areas, not just 230 00:12:18,240 --> 00:12:20,120 Speaker 1: because it's the right thing to do or because it's 231 00:12:20,120 --> 00:12:22,800 Speaker 1: you know, because I'm a black woman and I want 232 00:12:22,800 --> 00:12:24,840 Speaker 1: to be in these spaces, but because of exactly what 233 00:12:24,880 --> 00:12:27,920 Speaker 1: you just described. When you don't have inclusive teams building 234 00:12:28,000 --> 00:12:31,440 Speaker 1: the the tools that really do run our world and 235 00:12:31,480 --> 00:12:34,520 Speaker 1: really do control how and what we we you know, 236 00:12:35,040 --> 00:12:37,960 Speaker 1: come across in terms of the media landscape, that is 237 00:12:37,960 --> 00:12:40,240 Speaker 1: when things get missed, right, And so if you have 238 00:12:40,360 --> 00:12:42,360 Speaker 1: a team that is building an algorithm that is going 239 00:12:42,400 --> 00:12:44,520 Speaker 1: to control so much of what we consume, and that 240 00:12:44,559 --> 00:12:47,679 Speaker 1: team is mostly white and mostly male, it's not surprising 241 00:12:47,679 --> 00:12:50,880 Speaker 1: to me that these things go on to create unforeseen 242 00:12:50,960 --> 00:12:53,720 Speaker 1: harms that this team that was mostly homogeneous did not 243 00:12:53,800 --> 00:12:57,480 Speaker 1: account for. And so, you know, I think it's critical 244 00:12:57,559 --> 00:13:01,040 Speaker 1: the way that the way that underrepresent and voices and 245 00:13:01,120 --> 00:13:04,559 Speaker 1: people and identities are seen in these spaces. It's critical to, 246 00:13:04,720 --> 00:13:06,400 Speaker 1: I think, get into a place where tech is not 247 00:13:06,480 --> 00:13:11,080 Speaker 1: causing so much harm because it's right now, I feel 248 00:13:11,120 --> 00:13:13,320 Speaker 1: like it's a bit out of control, and I think 249 00:13:13,360 --> 00:13:16,720 Speaker 1: that sometimes that can be kind of a complicated chain 250 00:13:16,800 --> 00:13:20,080 Speaker 1: of things to to sell someone on. Absolutely, and I 251 00:13:20,120 --> 00:13:23,920 Speaker 1: really think that that lack of representation is deliberately and 252 00:13:24,240 --> 00:13:27,960 Speaker 1: actively exacerbated by the lack of transparency and the lack 253 00:13:28,000 --> 00:13:31,920 Speaker 1: of accountability that these platforms have on dealing with harassment. 254 00:13:32,320 --> 00:13:35,080 Speaker 1: Because if you have a company, and this is absolutely 255 00:13:35,120 --> 00:13:38,000 Speaker 1: the m O of most of the major social media platforms, 256 00:13:38,280 --> 00:13:41,679 Speaker 1: instead of having a clear strategy on harassment and on 257 00:13:41,720 --> 00:13:44,280 Speaker 1: these issues that works for everybody, what they do is 258 00:13:44,320 --> 00:13:46,760 Speaker 1: they judge each case separately. And what that means is 259 00:13:46,800 --> 00:13:49,200 Speaker 1: that when a case hits the media and hits the headlines, 260 00:13:49,480 --> 00:13:51,640 Speaker 1: they take action because they want to clear up, they 261 00:13:51,679 --> 00:13:54,440 Speaker 1: want to protect their reputation. So that means that the 262 00:13:54,480 --> 00:13:57,520 Speaker 1: cases that get acted on are dependent on media attention. 263 00:13:57,880 --> 00:14:00,480 Speaker 1: And we know that media attention is likely to be 264 00:14:00,480 --> 00:14:04,800 Speaker 1: bestowed on a young, privileged, white, attractive, non disabled, CIS 265 00:14:04,880 --> 00:14:06,880 Speaker 1: gender woman who has a problem so you get someone 266 00:14:06,920 --> 00:14:10,800 Speaker 1: like Taylor Swift, she's being abused online. Suddenly Instagram is 267 00:14:10,840 --> 00:14:14,000 Speaker 1: taking action. It's announcing new policies, it's announcing tools all 268 00:14:14,000 --> 00:14:16,480 Speaker 1: over the place to protect her and to deal with it. 269 00:14:16,760 --> 00:14:18,920 Speaker 1: But if you have a woman whose profile is less 270 00:14:18,960 --> 00:14:22,560 Speaker 1: likely to be picked up via our racist and misogynistic media, 271 00:14:23,040 --> 00:14:25,200 Speaker 1: that means that Facebook or whoever it is, they're not 272 00:14:25,200 --> 00:14:27,120 Speaker 1: going to get a call from a journalist saying what 273 00:14:27,160 --> 00:14:29,480 Speaker 1: are you doing about the harassment of this particular woman. 274 00:14:29,880 --> 00:14:32,280 Speaker 1: And that means that those voices that we most desperately 275 00:14:32,320 --> 00:14:35,640 Speaker 1: needed in those spaces are the ones being driven offline 276 00:14:35,680 --> 00:14:38,520 Speaker 1: and being driven out of those spaces. So it's such 277 00:14:38,560 --> 00:14:41,760 Speaker 1: a vicious circle, and it really is kind of self perpetuating. 278 00:14:41,840 --> 00:14:58,760 Speaker 1: I think let's take a quick break. Basically, the only 279 00:14:58,800 --> 00:15:01,120 Speaker 1: way to get social media panies to take it seriously 280 00:15:01,120 --> 00:15:03,800 Speaker 1: when you're being harassed online is to be famous enough 281 00:15:03,840 --> 00:15:06,840 Speaker 1: for it to generate negative media attention. It's clear to 282 00:15:06,880 --> 00:15:09,600 Speaker 1: me that tech companies are not optimizing their platforms to 283 00:15:09,640 --> 00:15:12,560 Speaker 1: be safer from the jump. Instead, they only take action 284 00:15:12,600 --> 00:15:15,760 Speaker 1: when they're reacting to bad press. I wonder if it's 285 00:15:15,760 --> 00:15:19,160 Speaker 1: because they are optimizing their platforms for harasses if they 286 00:15:19,160 --> 00:15:21,960 Speaker 1: are optimizing their platforms because it works in their favor, 287 00:15:22,040 --> 00:15:25,120 Speaker 1: that actually, these vast networks of men who are spending 288 00:15:25,160 --> 00:15:27,440 Speaker 1: a huge amount of their time online as staying on 289 00:15:27,480 --> 00:15:32,440 Speaker 1: those platforms. I do wonder if there is a deliberate 290 00:15:32,480 --> 00:15:35,040 Speaker 1: decision going on here about which groups of users they're 291 00:15:35,040 --> 00:15:41,560 Speaker 1: protecting and at what cost to other groups. Have you 292 00:15:41,560 --> 00:15:45,320 Speaker 1: ever felt like negative, inflammatory, or polarizing content gets more 293 00:15:45,360 --> 00:15:48,240 Speaker 1: attention on your social media feed? It might not just 294 00:15:48,320 --> 00:15:51,880 Speaker 1: be in your head. According to Angry by Design Toxic 295 00:15:51,960 --> 00:15:55,440 Speaker 1: Communication and Technical Architectures in the Journal of Humanities and 296 00:15:55,480 --> 00:15:59,480 Speaker 1: Social Science, communication platforms like YouTube and Facebook privilege in 297 00:15:59,560 --> 00:16:03,560 Speaker 1: thediary content setting up a stimulus response loop that promotes 298 00:16:03,600 --> 00:16:08,080 Speaker 1: outrage expression. So rather than seeing positive content, algorithms keep 299 00:16:08,160 --> 00:16:10,640 Speaker 1: us angry and on edge by surfacing up a steady 300 00:16:10,640 --> 00:16:15,160 Speaker 1: diet of outrage. These algorithms are optimizing for content that 301 00:16:15,200 --> 00:16:16,920 Speaker 1: gets a lot of cliques and attention, and that that 302 00:16:17,040 --> 00:16:20,040 Speaker 1: content happens to be very negative, And so part of 303 00:16:20,040 --> 00:16:23,680 Speaker 1: me always wonders, you know, is that why like the 304 00:16:23,720 --> 00:16:25,960 Speaker 1: fact that I see the tweets that are that are 305 00:16:26,000 --> 00:16:30,080 Speaker 1: inflammatory or super negative or super you know, um partisan 306 00:16:30,200 --> 00:16:32,800 Speaker 1: or whatever. The is the reason I see those more 307 00:16:32,880 --> 00:16:35,480 Speaker 1: is because they just get more attention. Therefore, these algorithms 308 00:16:35,520 --> 00:16:37,680 Speaker 1: are always going to privilege that kind of content, And 309 00:16:38,040 --> 00:16:40,800 Speaker 1: you know, part of me feels like, well, maybe it's 310 00:16:40,880 --> 00:16:44,520 Speaker 1: just but maybe it's just the platforms showing us what 311 00:16:44,600 --> 00:16:48,800 Speaker 1: we what we as like, you know, messy humans just 312 00:16:48,880 --> 00:16:50,800 Speaker 1: want to see, right, Like, oh, I'm not interested in 313 00:16:50,840 --> 00:16:53,320 Speaker 1: the positive content. I want to see the negative content. 314 00:16:53,640 --> 00:16:56,760 Speaker 1: But even still, it's like, is that is is that 315 00:16:56,920 --> 00:16:59,320 Speaker 1: an online space where I want to be, where folks 316 00:16:59,320 --> 00:17:01,640 Speaker 1: want to be, a base where the content that it 317 00:17:01,680 --> 00:17:04,040 Speaker 1: often serves you with content that is really negative and 318 00:17:04,040 --> 00:17:06,000 Speaker 1: that content that makes you feel good. It just really 319 00:17:06,680 --> 00:17:10,439 Speaker 1: I don't know, it makes me wonder how personally for me, 320 00:17:10,520 --> 00:17:12,879 Speaker 1: like why I spend time on spaces that make me 321 00:17:12,960 --> 00:17:17,920 Speaker 1: feel so miserable? Sometimes? Yeah, absolutely, And that was the 322 00:17:18,280 --> 00:17:20,359 Speaker 1: experience of so many of the women I interviewed for 323 00:17:20,400 --> 00:17:22,439 Speaker 1: the book, and in many cases, the choice eventually for 324 00:17:22,480 --> 00:17:25,120 Speaker 1: their own mental health was that they removed themselves from 325 00:17:25,119 --> 00:17:27,920 Speaker 1: those spaces. And I think it's really interesting that the 326 00:17:28,000 --> 00:17:31,919 Speaker 1: average white man's experience of social media is not necessarily 327 00:17:31,920 --> 00:17:34,800 Speaker 1: a net negative in the same way, um there have 328 00:17:34,880 --> 00:17:38,560 Speaker 1: been so many examples where white men versus their female 329 00:17:38,640 --> 00:17:42,000 Speaker 1: colleagues or their colleagues of color have created similar accounts 330 00:17:42,000 --> 00:17:45,320 Speaker 1: and have released similar kinds of information or opinions, and 331 00:17:45,400 --> 00:17:48,360 Speaker 1: the results in terms of the responses that they've received 332 00:17:48,400 --> 00:17:51,520 Speaker 1: have been so dramatically different. Um. And I think that 333 00:17:51,880 --> 00:17:54,000 Speaker 1: it's really interesting to think as well about whole all 334 00:17:54,040 --> 00:17:57,080 Speaker 1: of this intersects with a kind of capitalist urge for 335 00:17:57,119 --> 00:17:59,879 Speaker 1: social media to be selling us things, because we know 336 00:18:00,040 --> 00:18:04,480 Speaker 1: absolutely from kind of former engineers that YouTube givem Schaslow 337 00:18:04,560 --> 00:18:06,119 Speaker 1: is a good example of this, who I quote in 338 00:18:06,160 --> 00:18:08,240 Speaker 1: the book. He's a guy who comes forward and says, look, 339 00:18:08,520 --> 00:18:12,000 Speaker 1: the YouTube algorithm is not about optimizing for you to 340 00:18:12,040 --> 00:18:16,560 Speaker 1: see the most high quality content or the most relevant content. 341 00:18:16,880 --> 00:18:19,360 Speaker 1: It's about whatever we'll keep you watching for the longest, 342 00:18:19,400 --> 00:18:21,720 Speaker 1: because that's all that matters to the algorithm is getting 343 00:18:21,720 --> 00:18:24,520 Speaker 1: you to see as many adverts as possible, essentially, and 344 00:18:24,560 --> 00:18:26,679 Speaker 1: we know that the things that keep people watching for 345 00:18:26,720 --> 00:18:31,400 Speaker 1: the longest are increasingly extreme content. And as many researchers 346 00:18:31,520 --> 00:18:33,720 Speaker 1: pointed out, that's not such a big deal if you're 347 00:18:33,960 --> 00:18:38,240 Speaker 1: looking for a video about you know, um cooking, and 348 00:18:38,280 --> 00:18:40,360 Speaker 1: then suddenly you're being taken to ourn or you can 349 00:18:40,400 --> 00:18:43,080 Speaker 1: eat contest video. But it does become serious if you're 350 00:18:43,080 --> 00:18:46,040 Speaker 1: looking at something about women and suddenly you're being told 351 00:18:46,040 --> 00:18:47,800 Speaker 1: that the gender pay gap is a myth, and from 352 00:18:47,840 --> 00:18:50,159 Speaker 1: there that actually women are lying bitches who make up 353 00:18:50,200 --> 00:18:52,760 Speaker 1: rape allegations, and from there that maybe it will be 354 00:18:52,760 --> 00:18:55,720 Speaker 1: better off if women were forced into sexual slavery. There 355 00:18:55,800 --> 00:18:58,479 Speaker 1: suddenly it becomes pretty important that that's what's going on. 356 00:18:59,119 --> 00:19:01,560 Speaker 1: You know, you mentioned some of the experiences the women 357 00:19:01,640 --> 00:19:03,440 Speaker 1: that you interviewed for your book shared with you, or 358 00:19:03,480 --> 00:19:06,480 Speaker 1: that eventually they're just they leave these spaces, they leave 359 00:19:06,560 --> 00:19:10,280 Speaker 1: social media. You know, we talk so much about things 360 00:19:10,320 --> 00:19:12,919 Speaker 1: like d platforming and people being kicked off you know, 361 00:19:13,000 --> 00:19:16,760 Speaker 1: social media or you know, quote cancel culture, and we 362 00:19:16,800 --> 00:19:20,040 Speaker 1: never sort of talk about the issues of the women 363 00:19:20,119 --> 00:19:23,520 Speaker 1: and the voices who are just kept from speaking, just 364 00:19:23,640 --> 00:19:26,199 Speaker 1: kept from putting their opinions out there, who are pushed 365 00:19:26,200 --> 00:19:28,639 Speaker 1: off of these spaces because they are no longer safe. 366 00:19:29,040 --> 00:19:32,280 Speaker 1: I feel that that often gets missed in the conversation 367 00:19:32,359 --> 00:19:34,320 Speaker 1: that like, isn't that a form of d platforming. Isn't 368 00:19:34,359 --> 00:19:39,000 Speaker 1: that a speech issue that these women are functionally unable 369 00:19:39,200 --> 00:19:43,080 Speaker 1: to to, you know, express themselves. It's ironic that the 370 00:19:43,240 --> 00:19:46,679 Speaker 1: freedom of speech debate is usually dominated by people who 371 00:19:46,760 --> 00:19:50,439 Speaker 1: are claiming to have been silenced, often in media, major 372 00:19:50,680 --> 00:19:55,600 Speaker 1: media platforms, writing speaking out about their silencing, usually white 373 00:19:55,600 --> 00:19:58,840 Speaker 1: men speaking out about how white men can't say anything anymore, 374 00:19:59,400 --> 00:20:02,439 Speaker 1: leaving them saying it, and it's like on every on 375 00:20:02,560 --> 00:20:07,040 Speaker 1: every every paper, also being picked up by those same algorithms. 376 00:20:07,400 --> 00:20:09,479 Speaker 1: But we don't tend to think about kind of freedom 377 00:20:09,480 --> 00:20:12,399 Speaker 1: of speech in the in the negative, like freedom of 378 00:20:12,440 --> 00:20:14,160 Speaker 1: speech from people who don't get to speak to tell 379 00:20:14,200 --> 00:20:16,240 Speaker 1: us that they've lost their voices in the first place. 380 00:20:16,640 --> 00:20:19,280 Speaker 1: And I think there's a risk for our political discourse 381 00:20:19,320 --> 00:20:21,399 Speaker 1: as well, that we have a generation of young people 382 00:20:21,480 --> 00:20:25,600 Speaker 1: who are honing their political skills online. They're often described 383 00:20:25,640 --> 00:20:30,040 Speaker 1: as a politically disengaged or apathetic generation, but actually I 384 00:20:30,080 --> 00:20:32,520 Speaker 1: think it's a generation who are just politically active in 385 00:20:32,640 --> 00:20:34,720 Speaker 1: very new and different ways, and a lot of that 386 00:20:34,800 --> 00:20:37,400 Speaker 1: involved technology. So these are young people who are debating 387 00:20:37,440 --> 00:20:41,160 Speaker 1: on Twitter, who are cutting their teeth in political debate online. 388 00:20:41,640 --> 00:20:46,080 Speaker 1: And if those online platforms are unsafe for women and girls, 389 00:20:46,119 --> 00:20:50,240 Speaker 1: for various minoritized communities, then we are kind of weeding 390 00:20:50,280 --> 00:20:53,000 Speaker 1: them out from the public discourse Before they ever get 391 00:20:53,040 --> 00:20:56,359 Speaker 1: the point where they might reach those public spaces later 392 00:20:56,440 --> 00:20:59,000 Speaker 1: on where suddenly will really feel the loss of them. 393 00:20:59,240 --> 00:21:01,080 Speaker 1: And it might be that we don't realize that that's 394 00:21:01,080 --> 00:21:04,120 Speaker 1: happening until it's too late. That's such a good point. 395 00:21:04,160 --> 00:21:06,760 Speaker 1: I hadn't even hadn't even really thought about that that. 396 00:21:07,240 --> 00:21:10,320 Speaker 1: You know, what does a landscape looks look like when 397 00:21:10,720 --> 00:21:14,640 Speaker 1: a big section of that generation is just quiet and 398 00:21:14,640 --> 00:21:17,240 Speaker 1: and go silent. You know, that's a real that's a 399 00:21:17,280 --> 00:21:33,600 Speaker 1: real problem. More after a quick break, let's get right 400 00:21:33,600 --> 00:21:36,159 Speaker 1: back into it. I got my first computer when I 401 00:21:36,200 --> 00:21:39,160 Speaker 1: was in junior high. But most younger folks have lived 402 00:21:39,160 --> 00:21:41,520 Speaker 1: their entire lives online and ways that I could never 403 00:21:41,600 --> 00:21:44,320 Speaker 1: imagine when I first got online. So if the internet 404 00:21:44,359 --> 00:21:46,520 Speaker 1: is the backdrop upon which we're all living our lives, 405 00:21:46,880 --> 00:21:49,840 Speaker 1: what happens when that backdrop is really harmful or toxic? 406 00:21:50,720 --> 00:21:53,720 Speaker 1: What's it doing to a generation of younger folks who 407 00:21:53,720 --> 00:21:56,760 Speaker 1: are coming up completely immersed in it and we've never 408 00:21:56,800 --> 00:21:59,400 Speaker 1: really seen it. Because we are at kind of hovering 409 00:21:59,400 --> 00:22:02,639 Speaker 1: on the edge of the first real generation coming of 410 00:22:02,680 --> 00:22:05,760 Speaker 1: age who have lived their whole lives on social media, 411 00:22:06,200 --> 00:22:09,320 Speaker 1: we have this kind of unique political moment that never 412 00:22:09,359 --> 00:22:11,560 Speaker 1: really gets picked up on this. I find it wild 413 00:22:11,600 --> 00:22:13,719 Speaker 1: that we're living at this moment in history has never 414 00:22:13,800 --> 00:22:16,200 Speaker 1: happened before and will never happen again, and yet it's 415 00:22:16,200 --> 00:22:19,880 Speaker 1: never really discussed where a generation of non digital natives 416 00:22:19,960 --> 00:22:23,359 Speaker 1: is parenting and educating a generation of digital natives, and 417 00:22:23,400 --> 00:22:26,440 Speaker 1: there is this chasm, there, this huge gap in culture 418 00:22:26,440 --> 00:22:28,399 Speaker 1: and understanding of what their world is like, what the 419 00:22:28,480 --> 00:22:30,800 Speaker 1: day to day landscape of their online world is like 420 00:22:31,280 --> 00:22:34,160 Speaker 1: amongst parents who grew up in a pre Internet age, 421 00:22:34,480 --> 00:22:37,080 Speaker 1: and that generation will becoming of age, and what will 422 00:22:37,080 --> 00:22:40,879 Speaker 1: that look like when our political representatives are being drawn 423 00:22:41,080 --> 00:22:43,600 Speaker 1: from a generation who have literally grown up with the 424 00:22:43,840 --> 00:22:48,399 Speaker 1: sheer daily bombardment of racism and misogyny and transphobia that 425 00:22:48,560 --> 00:22:50,879 Speaker 1: comes with living your life on the Internet in the 426 00:22:50,880 --> 00:22:53,119 Speaker 1: way that young people do today. And I don't think 427 00:22:53,160 --> 00:22:55,080 Speaker 1: we'll know what that looks like until we get there, 428 00:22:55,119 --> 00:22:58,399 Speaker 1: And I think when we do, it will be a shock. Wow. 429 00:22:58,480 --> 00:23:01,960 Speaker 1: I yeah, I hadn't even thought about that, but you 430 00:23:02,000 --> 00:23:05,400 Speaker 1: know it's true, and I do think. I think about 431 00:23:05,400 --> 00:23:07,040 Speaker 1: this a lot. So I don't I don't know if 432 00:23:07,040 --> 00:23:09,760 Speaker 1: you're a parent. I don't have children, but I have 433 00:23:09,840 --> 00:23:12,480 Speaker 1: a lot of young people in my life, and I 434 00:23:12,520 --> 00:23:15,639 Speaker 1: think about how hard it is, how hard it must be, 435 00:23:15,760 --> 00:23:18,920 Speaker 1: or how different it must be to be wading through 436 00:23:19,359 --> 00:23:23,560 Speaker 1: this very complex online experience that young people so they 437 00:23:23,560 --> 00:23:25,439 Speaker 1: have to wade through, and like, as you said, that 438 00:23:25,480 --> 00:23:30,520 Speaker 1: experience is often racially charged, politically charged. It's often I 439 00:23:30,560 --> 00:23:34,560 Speaker 1: think that it very much overlaps with like real life. 440 00:23:34,600 --> 00:23:36,120 Speaker 1: You know. I think when I was coming up, coming 441 00:23:36,160 --> 00:23:38,439 Speaker 1: up online, people were like, Oh, your online world and 442 00:23:38,480 --> 00:23:41,399 Speaker 1: your real world. Today it's very clear that those are 443 00:23:41,440 --> 00:23:43,760 Speaker 1: those overlap. You know, things that you say online get 444 00:23:43,760 --> 00:23:45,800 Speaker 1: you fired from your job or in trouble at school, 445 00:23:46,400 --> 00:23:50,800 Speaker 1: and they're largely wading through it with parents who have 446 00:23:50,920 --> 00:23:53,240 Speaker 1: not had that experience. You're exactly right. I hadn't even 447 00:23:53,280 --> 00:23:57,760 Speaker 1: thought about that. It's and I think the people we 448 00:23:57,800 --> 00:23:59,480 Speaker 1: see it in the way that we report on things 449 00:23:59,480 --> 00:24:01,640 Speaker 1: that happened live, we don't take them seriously, we don't 450 00:24:01,640 --> 00:24:04,159 Speaker 1: look seriously at them, and as you were saying in 451 00:24:04,200 --> 00:24:07,840 Speaker 1: the beginning of our interview, that lack of scrutiny allows 452 00:24:07,880 --> 00:24:10,000 Speaker 1: for these things to just go on check. And so 453 00:24:10,359 --> 00:24:12,840 Speaker 1: you know, if you spend if you're a reporter who 454 00:24:12,840 --> 00:24:15,359 Speaker 1: spent all this time being like, oh, kids, on the internet. 455 00:24:15,400 --> 00:24:17,600 Speaker 1: What do they do and taken Selfie's like, oh, just 456 00:24:17,920 --> 00:24:21,720 Speaker 1: just just trivializing it. When it really becomes an issue 457 00:24:21,760 --> 00:24:24,479 Speaker 1: that we should pay attention to, you know, it can 458 00:24:24,520 --> 00:24:28,280 Speaker 1: be a problem. Absolutely. One of the experts I interviewed 459 00:24:28,280 --> 00:24:31,720 Speaker 1: for the book, Dr Carlin Ferman. She said that one 460 00:24:31,760 --> 00:24:33,840 Speaker 1: of the things that she really sees in her work 461 00:24:33,880 --> 00:24:37,480 Speaker 1: with young people around sexual violence is that it's extremely 462 00:24:37,560 --> 00:24:40,000 Speaker 1: rare for her to come across a case of sexual 463 00:24:40,080 --> 00:24:43,640 Speaker 1: violence that doesn't have an online component, and that adults 464 00:24:43,680 --> 00:24:47,639 Speaker 1: find it difficult to recognize that connection, but for young people, 465 00:24:47,680 --> 00:24:50,480 Speaker 1: there is no meaningful distinction between their real lives and 466 00:24:50,480 --> 00:24:53,960 Speaker 1: their online lives. And she said that the normalization of 467 00:24:54,080 --> 00:24:56,920 Speaker 1: violence in the online world is having a massive impact 468 00:24:56,960 --> 00:25:00,199 Speaker 1: in young people's perceptions of violence offline. And that's very 469 00:25:00,280 --> 00:25:02,120 Speaker 1: much something that I see in my work as well. 470 00:25:02,160 --> 00:25:04,520 Speaker 1: When I'm working with young people, it's really common to 471 00:25:04,560 --> 00:25:06,720 Speaker 1: hear them say things like rape as a compliment. Really, 472 00:25:07,000 --> 00:25:10,040 Speaker 1: it's not raper she enjoys it. You hear young people 473 00:25:10,040 --> 00:25:12,880 Speaker 1: repeating stuff that they've seen an online porn and assuming 474 00:25:12,920 --> 00:25:15,480 Speaker 1: that that's how they'll be called upon to behave in 475 00:25:15,520 --> 00:25:19,159 Speaker 1: a sexual relationship. So um, For example, I've visited a 476 00:25:19,160 --> 00:25:21,359 Speaker 1: school where they've had a rape case involving a fourteen 477 00:25:21,440 --> 00:25:24,200 Speaker 1: year old boy, and a teacher had said, why didn't 478 00:25:24,200 --> 00:25:26,680 Speaker 1: you stop when she was crying? And he had said, 479 00:25:26,960 --> 00:25:29,959 Speaker 1: because it's normal for girls to cry during sex, because 480 00:25:30,160 --> 00:25:32,119 Speaker 1: that's what he'd seen online. And so much of what 481 00:25:32,200 --> 00:25:37,840 Speaker 1: they're very mainstream, readily available online pornography is showing kids 482 00:25:37,880 --> 00:25:41,600 Speaker 1: that sex apparently is something very violent and aggressive that's 483 00:25:41,600 --> 00:25:45,120 Speaker 1: done by men to women. Again, it's racialized. There are 484 00:25:45,119 --> 00:25:49,120 Speaker 1: these kind of fetishized racial stereotypes there. It very much 485 00:25:49,119 --> 00:25:52,160 Speaker 1: suggests that it's normal for women to be hurt, humiliated, 486 00:25:52,160 --> 00:25:55,280 Speaker 1: and degraded during sex. And so I'm seeing young people 487 00:25:55,280 --> 00:26:00,320 Speaker 1: who've experienced quite extreme sexual abuse and wouldn't recognize what's 488 00:26:00,320 --> 00:26:02,840 Speaker 1: happened to them is wrong, or that they would ever 489 00:26:02,880 --> 00:26:05,080 Speaker 1: have the right to tell someone, or that support would 490 00:26:05,080 --> 00:26:08,080 Speaker 1: be available, because it's so normalized by what they're seeing online, 491 00:26:08,440 --> 00:26:10,879 Speaker 1: and there's a real barrier to dealing with that. When 492 00:26:10,920 --> 00:26:14,600 Speaker 1: the majority of the parents I speak to, their conception 493 00:26:14,760 --> 00:26:17,400 Speaker 1: of what online porn looks like is a kind of 494 00:26:17,480 --> 00:26:21,720 Speaker 1: Playboy centerfold, but online it's like FHM photos, but you 495 00:26:21,760 --> 00:26:25,000 Speaker 1: see them on a website. There's very little recognition of 496 00:26:25,000 --> 00:26:27,359 Speaker 1: of what the reality of that landscape looks like for 497 00:26:27,400 --> 00:26:31,560 Speaker 1: the teenagers who immersed in it on a daily basis. Wow. Yeah, 498 00:26:31,640 --> 00:26:35,840 Speaker 1: I mean, it's got to be so incredibly difficult to 499 00:26:35,960 --> 00:26:38,560 Speaker 1: navigate as a young person. And then you know, talking 500 00:26:38,600 --> 00:26:41,920 Speaker 1: to your parents about porn and sex is already so hard, 501 00:26:42,359 --> 00:26:45,119 Speaker 1: and then that they had there be that kind of 502 00:26:45,480 --> 00:26:48,919 Speaker 1: extreme barrier in terms of like understand your parents ability 503 00:26:48,920 --> 00:26:51,399 Speaker 1: to even understand what you're experiencing. That's that's kind to 504 00:26:51,440 --> 00:26:54,760 Speaker 1: be so hard. I don't envy, but I mean I 505 00:26:54,760 --> 00:26:56,639 Speaker 1: always I say this in so many different ways, like 506 00:26:56,800 --> 00:26:58,840 Speaker 1: I don't envy kids today who have to navigate this 507 00:26:59,000 --> 00:27:02,560 Speaker 1: largely on their own. It must be very complicated. Yeah, 508 00:27:02,720 --> 00:27:05,000 Speaker 1: me neither. And then when you add into that mix 509 00:27:05,160 --> 00:27:07,600 Speaker 1: the fact that you've got this generation of teenage boys 510 00:27:07,680 --> 00:27:11,679 Speaker 1: being actively groomed and sought after buy these extremist groups 511 00:27:12,200 --> 00:27:15,960 Speaker 1: really sort of training them and radicalizing them to dehumanize 512 00:27:15,960 --> 00:27:19,840 Speaker 1: their female peers, to objectify their female peers, then that 513 00:27:19,920 --> 00:27:23,280 Speaker 1: kind of almost sets a match to the whole tinder pile, 514 00:27:23,359 --> 00:27:26,920 Speaker 1: and up it goes. In Atlanta last month, eight people 515 00:27:26,920 --> 00:27:30,840 Speaker 1: were murdered by a gunman at three spas. Initially, Sheriff's 516 00:27:30,840 --> 00:27:34,159 Speaker 1: Captain J. Baker parroted the perpetrator's own words that his 517 00:27:34,240 --> 00:27:37,159 Speaker 1: heinous crimes were not motivated by race. But six of 518 00:27:37,200 --> 00:27:40,919 Speaker 1: his victims are Asian women, whom he conflated with sexual temptation. 519 00:27:41,880 --> 00:27:44,600 Speaker 1: So why the rush to not describe his actions as 520 00:27:44,600 --> 00:27:49,080 Speaker 1: a racially motivated and gendered act of terror against Asian women. Well, 521 00:27:49,080 --> 00:27:51,600 Speaker 1: the investigators they interviewed him this morning, and they got 522 00:27:51,600 --> 00:27:55,520 Speaker 1: that impression that yes, he understood um, the gravity of it, 523 00:27:55,760 --> 00:27:58,200 Speaker 1: and he was pretty much fed up and it ben't 524 00:27:58,240 --> 00:28:00,199 Speaker 1: kind of at the end of his rope. And and 525 00:28:00,240 --> 00:28:01,719 Speaker 1: if they was a really bad day for him and 526 00:28:01,840 --> 00:28:05,280 Speaker 1: this is what he did, I'm not going to go 527 00:28:05,320 --> 00:28:06,919 Speaker 1: to I don't know if ether are more slow or not. 528 00:28:07,440 --> 00:28:10,640 Speaker 1: We see this time and time again. Yet when we 529 00:28:10,680 --> 00:28:14,040 Speaker 1: talk about these these these issues, like in Atlanta, that 530 00:28:14,280 --> 00:28:16,600 Speaker 1: awful sheriff said, oh, well, this guy had a bad day. 531 00:28:16,640 --> 00:28:18,440 Speaker 1: He said he was a sex adic blah blah blah. 532 00:28:18,640 --> 00:28:20,480 Speaker 1: I've never seen a situation where they just take the 533 00:28:20,520 --> 00:28:24,320 Speaker 1: perpetrator at their word, like, oh, not motivated by race 534 00:28:25,080 --> 00:28:28,240 Speaker 1: checks out. You know, I wonder, why do you think 535 00:28:28,280 --> 00:28:31,960 Speaker 1: it is so difficult for people to recognize this as 536 00:28:32,080 --> 00:28:35,640 Speaker 1: extremist or even in some cases like terrorism, we almost 537 00:28:35,680 --> 00:28:41,000 Speaker 1: never describe the kind of havoc that extremist extremist views 538 00:28:41,120 --> 00:28:44,400 Speaker 1: on women and folks of color. Domestically in the United States, 539 00:28:44,440 --> 00:28:47,120 Speaker 1: we almost never talked about that as terrorism, Like why 540 00:28:47,120 --> 00:28:49,240 Speaker 1: do you think that is? I think as a society 541 00:28:49,280 --> 00:28:53,560 Speaker 1: we are very comfortable with homogenizing other groups than white 542 00:28:53,600 --> 00:28:56,880 Speaker 1: men um, but we tend to afford white men the 543 00:28:56,960 --> 00:29:01,240 Speaker 1: dignity of discrete and unique identities. And so when a 544 00:29:01,280 --> 00:29:04,560 Speaker 1: white man commits a mass atrocity because he doesn't fit 545 00:29:04,640 --> 00:29:08,120 Speaker 1: the stereotype our society has of a terrorist, we look 546 00:29:08,200 --> 00:29:11,080 Speaker 1: for other reasons, and we dig into mental health, and 547 00:29:11,120 --> 00:29:14,880 Speaker 1: we see these these kind of almost glowing photographs of 548 00:29:14,960 --> 00:29:17,520 Speaker 1: him as a cherubic child, and we get a quote 549 00:29:17,520 --> 00:29:19,200 Speaker 1: from his neighbor to say that he was a really 550 00:29:19,280 --> 00:29:21,480 Speaker 1: nice guy who helped out with d I Y around 551 00:29:21,480 --> 00:29:24,360 Speaker 1: the neighborhood, and and hey, something from his granny to 552 00:29:24,440 --> 00:29:26,880 Speaker 1: say how sweet he was as a boy, And we 553 00:29:27,000 --> 00:29:29,800 Speaker 1: portray these men as we we can't. It's almost like 554 00:29:29,840 --> 00:29:32,360 Speaker 1: as a society, we can't compute the fact that this 555 00:29:32,400 --> 00:29:35,719 Speaker 1: could have happened because we're not able to recognize white 556 00:29:35,720 --> 00:29:37,920 Speaker 1: men as a group who can be radicalized and who 557 00:29:37,920 --> 00:29:40,720 Speaker 1: can carry out extremist acts. Even when we reach a 558 00:29:40,760 --> 00:29:44,880 Speaker 1: situation where they actually become the majority of perpetrators of 559 00:29:45,120 --> 00:29:48,160 Speaker 1: mass shootings, we still fail to recognize it. And I 560 00:29:48,200 --> 00:29:51,200 Speaker 1: think partly it's an inability to kind of see those 561 00:29:51,200 --> 00:29:53,360 Speaker 1: white men in that way when the media reports on 562 00:29:53,400 --> 00:29:55,720 Speaker 1: them in such a kind of forgiving and normalizing and 563 00:29:55,800 --> 00:29:58,800 Speaker 1: excusing way. But it's also about who the victims are 564 00:29:58,800 --> 00:30:01,880 Speaker 1: and a society tea that is used to seeing those 565 00:30:01,960 --> 00:30:05,640 Speaker 1: folks as victims as well. In the case of men 566 00:30:05,680 --> 00:30:08,720 Speaker 1: who have gone out and massacred women because there are women. 567 00:30:08,760 --> 00:30:11,640 Speaker 1: For example, we haven't called these men terrorists. We never 568 00:30:11,680 --> 00:30:15,120 Speaker 1: called Elliot Roger a terrorist. Alec Menassian, the Toronto van 569 00:30:15,160 --> 00:30:18,560 Speaker 1: attacker who murdered ten people and injured six, of the 570 00:30:18,560 --> 00:30:22,200 Speaker 1: people who murdered being women, who explicitly told police I've 571 00:30:22,320 --> 00:30:24,960 Speaker 1: murdered these women because I hate women, because women won't 572 00:30:24,960 --> 00:30:26,920 Speaker 1: have sex with me. And the police came out and 573 00:30:26,960 --> 00:30:29,080 Speaker 1: gave a press conference and said there's no evidence of 574 00:30:29,200 --> 00:30:32,240 Speaker 1: terrorism here. The city is safe. And I think in 575 00:30:32,360 --> 00:30:38,440 Speaker 1: part it's because brutality against people of color, and violence 576 00:30:38,480 --> 00:30:42,320 Speaker 1: against women are the wallpaper of our daily lives. These 577 00:30:42,320 --> 00:30:46,000 Speaker 1: are things that have become almost normal. If one in 578 00:30:46,080 --> 00:30:48,080 Speaker 1: three women on the planet is raped or beaten in 579 00:30:48,080 --> 00:30:52,080 Speaker 1: her lifetime, then a man violently murdering women isn't exceptional, 580 00:30:52,400 --> 00:30:55,000 Speaker 1: and so we struggle to recognize it as terrorism. And 581 00:30:55,000 --> 00:30:57,560 Speaker 1: I think the police and the criminal justice system are 582 00:30:57,600 --> 00:31:00,680 Speaker 1: at fault here. The media is at fault here. The 583 00:31:00,720 --> 00:31:02,880 Speaker 1: way that all of us talk about these crimes and 584 00:31:02,920 --> 00:31:06,200 Speaker 1: about these criminals are at fault um. And unless we 585 00:31:06,240 --> 00:31:09,800 Speaker 1: start to recognize these particular forms of terrorism and extremism, 586 00:31:09,800 --> 00:31:12,240 Speaker 1: it's very difficult to see how we'll move forward. Because 587 00:31:12,360 --> 00:31:15,280 Speaker 1: right now, because we don't call those men terror list terrorists, 588 00:31:15,520 --> 00:31:18,720 Speaker 1: we don't look at their often online journeys that have 589 00:31:18,880 --> 00:31:22,120 Speaker 1: filled them with hate as forms of radicalization. And if 590 00:31:22,120 --> 00:31:24,440 Speaker 1: we don't do that, it means that nobody is looking 591 00:31:24,480 --> 00:31:27,040 Speaker 1: at the grooming of the next generation of these attackers. 592 00:31:27,080 --> 00:31:30,160 Speaker 1: Nobody is looking at boys. When I was researching the book, 593 00:31:30,160 --> 00:31:32,640 Speaker 1: I rang up a lot of counter extremist counter terror 594 00:31:32,760 --> 00:31:36,600 Speaker 1: organizations globally, and I was really shocked that when I 595 00:31:36,680 --> 00:31:38,680 Speaker 1: used the word in cell typically there would be a 596 00:31:38,720 --> 00:31:40,400 Speaker 1: long silence on the other end of the phone, and 597 00:31:40,440 --> 00:31:42,480 Speaker 1: then somebody would say, can you can you spell that 598 00:31:42,600 --> 00:31:46,479 Speaker 1: for me? There was a US Government Accountability Office report 599 00:31:46,520 --> 00:31:48,840 Speaker 1: which was literally looking at the U. S government's response 600 00:31:48,920 --> 00:31:52,040 Speaker 1: to forms of terrorism, and in the period of the report, 601 00:31:52,120 --> 00:31:55,680 Speaker 1: there were three massacres carried out by male supremacists by 602 00:31:55,680 --> 00:31:58,719 Speaker 1: people with kind of in cell backgrounds online and they 603 00:31:58,760 --> 00:32:01,320 Speaker 1: weren't included in the report. But over this ten year 604 00:32:01,360 --> 00:32:05,479 Speaker 1: period they were tracing and carefully tracking people with extreme 605 00:32:05,560 --> 00:32:08,760 Speaker 1: views on federal ownership of public land or extreme views 606 00:32:08,800 --> 00:32:12,360 Speaker 1: on the environment, and um shockingly, in the period of 607 00:32:12,360 --> 00:32:15,200 Speaker 1: the report, no one was killed by people with those views, 608 00:32:15,440 --> 00:32:17,960 Speaker 1: So we're looking at the wrong people. And you know, 609 00:32:18,280 --> 00:32:21,760 Speaker 1: you look at someone like Donald Trump describing Black Lives 610 00:32:21,760 --> 00:32:25,520 Speaker 1: Matter activists as extremists, as terrorists, and you realize it 611 00:32:25,560 --> 00:32:28,920 Speaker 1: matters if the people who are choosing who is designated 612 00:32:28,960 --> 00:32:32,040 Speaker 1: as a terrorist or an extremist isn't representative of the 613 00:32:32,080 --> 00:32:34,920 Speaker 1: communities that they're serving and has the ability to carry 614 00:32:34,960 --> 00:32:37,360 Speaker 1: deep prejudice in them. So I guess it also comes 615 00:32:37,400 --> 00:32:40,560 Speaker 1: back to that issue we discussed earlier about representation in 616 00:32:40,600 --> 00:32:43,840 Speaker 1: the people who are policing and governing our countries and 617 00:32:43,880 --> 00:32:46,640 Speaker 1: how we respond to this threat. You know, the people 618 00:32:46,680 --> 00:32:49,880 Speaker 1: who are talking about it, who are investigating it, who 619 00:32:49,920 --> 00:32:53,600 Speaker 1: are you know, mired in it, they also have things 620 00:32:53,680 --> 00:32:55,760 Speaker 1: they can't see. Well, we all have biases, and so 621 00:32:56,280 --> 00:32:59,360 Speaker 1: it really is I think it's critical that we think 622 00:32:59,400 --> 00:33:03,640 Speaker 1: about inclusion in terms of you know this, If we 623 00:33:03,720 --> 00:33:05,800 Speaker 1: don't think about it and aren't able to talk about it, 624 00:33:05,800 --> 00:33:08,360 Speaker 1: it will hurt us all as a society. I completely agree. 625 00:33:08,680 --> 00:33:10,480 Speaker 1: Um And just as a side note, you know your 626 00:33:10,480 --> 00:33:14,040 Speaker 1: work with Everyday Feminism creating a database for women to 627 00:33:14,640 --> 00:33:18,160 Speaker 1: talk about their experiences with sexism and misogyny online. Even 628 00:33:18,240 --> 00:33:21,360 Speaker 1: even even things like that, where you're building these spaces 629 00:33:21,360 --> 00:33:23,640 Speaker 1: in these platforms where you can sort of clip the 630 00:33:23,640 --> 00:33:26,840 Speaker 1: script a little bit and say like, hey, we're here, 631 00:33:26,880 --> 00:33:30,280 Speaker 1: we have experiences that we want to talk about online. 632 00:33:30,720 --> 00:33:34,239 Speaker 1: You might think that the online experience is just you know, 633 00:33:34,640 --> 00:33:38,040 Speaker 1: dominated by men, but actually here is a slice of 634 00:33:38,040 --> 00:33:40,040 Speaker 1: what we deal with or what we experience. I think 635 00:33:40,600 --> 00:33:44,720 Speaker 1: doing that kind of archival chronicling, storytelling work is also 636 00:33:44,840 --> 00:33:47,160 Speaker 1: very important in terms of getting us to like a 637 00:33:47,240 --> 00:33:50,880 Speaker 1: culture shift where we think of these spaces as you know, 638 00:33:52,040 --> 00:33:54,560 Speaker 1: places where we are and places where we're you know, 639 00:33:55,280 --> 00:33:58,520 Speaker 1: showing up and taking up space. If that makes sense, absolutely, 640 00:33:58,800 --> 00:34:01,000 Speaker 1: I hope so. I hope the Internet is in a 641 00:34:01,040 --> 00:34:03,440 Speaker 1: transitional phase. You know. I feel like the Internet is 642 00:34:03,440 --> 00:34:06,640 Speaker 1: in its infancy. It's basically still teething. I had, These 643 00:34:06,640 --> 00:34:10,360 Speaker 1: are teething problems. It's so new, relatively speaking. Surely we 644 00:34:10,440 --> 00:34:13,160 Speaker 1: can't carry on like this. Surely it can't be forever 645 00:34:13,280 --> 00:34:17,680 Speaker 1: a space where where women, where people of color, where 646 00:34:17,760 --> 00:34:22,759 Speaker 1: transfolk have to practically brace themselves to step in. You know, 647 00:34:22,880 --> 00:34:26,800 Speaker 1: that can't be sustainable in the long run. I really 648 00:34:27,160 --> 00:34:29,480 Speaker 1: hope and believe that this can't just be the way 649 00:34:29,480 --> 00:34:32,000 Speaker 1: that things are. This has to be a transitional period. 650 00:34:32,719 --> 00:34:38,600 Speaker 1: I hope you're right. I hope you're right. I want 651 00:34:38,640 --> 00:34:40,160 Speaker 1: to take a moment to thank all of y'all for 652 00:34:40,200 --> 00:34:42,400 Speaker 1: the reviews that many of you have left. They really 653 00:34:42,400 --> 00:34:44,960 Speaker 1: do help the show grow, and yes, I personally read 654 00:34:45,040 --> 00:34:48,560 Speaker 1: each and everyone. I wanted to highlight one particular review 655 00:34:48,560 --> 00:34:51,240 Speaker 1: because they really stuck with me. This is from listener 656 00:34:51,360 --> 00:34:54,360 Speaker 1: Lisa Hazn. I've been listening for a while but hadn't 657 00:34:54,360 --> 00:34:57,640 Speaker 1: bothered rating the show and noticed the weirdly high proportion 658 00:34:57,719 --> 00:35:00,879 Speaker 1: of one star reviews scrolled through. When I couldn't believe 659 00:35:00,960 --> 00:35:03,799 Speaker 1: anything shocks me anymore. But did some guy really just 660 00:35:03,840 --> 00:35:06,799 Speaker 1: say a black woman's podcast is as bad as Jim Crow? 661 00:35:07,400 --> 00:35:09,440 Speaker 1: How does someone go about the trouble to find the show, 662 00:35:09,760 --> 00:35:12,439 Speaker 1: leave a rating type that out hits end and never 663 00:35:12,520 --> 00:35:15,520 Speaker 1: have a single moment of self awareness. Jokes on them though, 664 00:35:15,760 --> 00:35:19,000 Speaker 1: people like that are just enthusiastically proving the point that 665 00:35:19,080 --> 00:35:21,600 Speaker 1: this podcast is necessary and more people with the birth 666 00:35:21,680 --> 00:35:26,000 Speaker 1: perspectives need to have platforms. And Lisa, I just have 667 00:35:26,120 --> 00:35:29,000 Speaker 1: to tell you this review made me legit laugh out 668 00:35:29,000 --> 00:35:31,920 Speaker 1: loud in my first few weeks of making this podcast. 669 00:35:32,040 --> 00:35:35,480 Speaker 1: It's true someone did leave a review comparing the podcast 670 00:35:35,600 --> 00:35:38,879 Speaker 1: to Jim Crow, which I gotta say feels a little 671 00:35:38,920 --> 00:35:42,680 Speaker 1: bit harsh. And Lisa, your review reminded me of how 672 00:35:42,680 --> 00:35:45,440 Speaker 1: hard it was to keep pushing through even when vocal 673 00:35:45,440 --> 00:35:48,359 Speaker 1: critics like that left that kind of feedback. But reading 674 00:35:48,360 --> 00:35:51,399 Speaker 1: your review today, you know, fifty episodes in, just made 675 00:35:51,440 --> 00:35:53,920 Speaker 1: me really happy that I stuck with it, and I 676 00:35:53,960 --> 00:35:56,279 Speaker 1: wanted to share this for anybody who's maybe thinking about 677 00:35:56,280 --> 00:35:59,720 Speaker 1: starting a podcast of their own, or making anything at all. Really, 678 00:36:00,280 --> 00:36:04,200 Speaker 1: just ignore the trolls, ignore the haters, ignore that discouraging 679 00:36:04,239 --> 00:36:06,320 Speaker 1: little voice in your head that tells you to stop 680 00:36:06,400 --> 00:36:10,040 Speaker 1: and just keep going to Lisa. Thank you for the reminder. 681 00:36:16,040 --> 00:36:19,160 Speaker 1: If you enjoyed this podcast, please help us grow by subscribing. 682 00:36:23,520 --> 00:36:25,520 Speaker 1: Got a story about an interesting thing in tech, or 683 00:36:25,560 --> 00:36:27,399 Speaker 1: just want to say hi. We'd love to hear from 684 00:36:27,400 --> 00:36:30,640 Speaker 1: you at Hello at tango dot com. Disinformed is brought 685 00:36:30,640 --> 00:36:32,160 Speaker 1: to you by There Are No Girls on the Internet. 686 00:36:32,440 --> 00:36:35,640 Speaker 1: It's a production of iHeart Radio and unbost Creative Jonathan 687 00:36:35,680 --> 00:36:38,840 Speaker 1: Strickland is our executive producer. Tory Harrison is our supervising 688 00:36:38,880 --> 00:36:42,879 Speaker 1: producer and engineer. Michael Lamatto is our contributing producer. I'm 689 00:36:42,920 --> 00:36:45,799 Speaker 1: your host Bridget Todd. For more great podcast check out 690 00:36:45,800 --> 00:36:48,160 Speaker 1: the iHeart Radio app, Apple Podcast, or wherever you get 691 00:36:48,200 --> 00:36:48,880 Speaker 1: your podcasts.