1 00:00:00,360 --> 00:00:03,320 Speaker 1: Hi, bridgett Here. The other podcast that I make with 2 00:00:03,400 --> 00:00:08,080 Speaker 1: Mozilla about AI called IRL was nominated for an Anthem Award. 3 00:00:08,560 --> 00:00:10,360 Speaker 1: It would mean so much to me if you could 4 00:00:10,400 --> 00:00:13,160 Speaker 1: take just a few seconds and vote for IRL to win. 5 00:00:13,640 --> 00:00:16,079 Speaker 1: Just go to tangoti dot com slash I r L 6 00:00:16,560 --> 00:00:18,440 Speaker 1: or use the link in our show notes. You can 7 00:00:18,520 --> 00:00:21,480 Speaker 1: vote until December twenty first. Thank you so much. It 8 00:00:21,520 --> 00:00:27,880 Speaker 1: means a lot. There are No Girls on the Internet. 9 00:00:27,880 --> 00:00:35,280 Speaker 1: As a production of iHeartRadio and Unbossed Creative, I'm bridget 10 00:00:35,360 --> 00:00:37,479 Speaker 1: Todd and this is there are no Girls on the Internet. 11 00:00:40,080 --> 00:00:42,479 Speaker 1: I am here with my producer Joey. Joey, it's nice 12 00:00:42,479 --> 00:00:43,000 Speaker 1: to see you back. 13 00:00:43,040 --> 00:00:45,480 Speaker 2: We missed you, Hi, it's nice with you too. 14 00:00:46,360 --> 00:00:49,160 Speaker 1: So as y'all know, we are trying to start every 15 00:00:49,200 --> 00:00:51,520 Speaker 1: episode with a little bit of light banter, just sort 16 00:00:51,560 --> 00:00:54,760 Speaker 1: of ease us into the episode. I tried my hardest 17 00:00:54,760 --> 00:00:59,040 Speaker 1: to do it last week. We bank Yeah. 18 00:01:00,800 --> 00:01:00,880 Speaker 3: Uh. 19 00:01:01,120 --> 00:01:04,800 Speaker 1: Listener Susan actually emailed us with a great banter suggestion 20 00:01:05,319 --> 00:01:07,600 Speaker 1: of using the time to sort of get to know 21 00:01:07,800 --> 00:01:10,280 Speaker 1: our producers. Because we hear from Joey, we hear from Mike. 22 00:01:10,880 --> 00:01:13,280 Speaker 1: Listeners don't necessarily know who these folks are, even though 23 00:01:13,319 --> 00:01:16,160 Speaker 1: I feel like, you know, I'm talking to them all 24 00:01:16,160 --> 00:01:19,000 Speaker 1: the time. So to do that, let's play a little 25 00:01:19,040 --> 00:01:22,039 Speaker 1: bit of my favorite game, would you Rather? Would you rather? 26 00:01:22,080 --> 00:01:24,280 Speaker 1: As one of my favorite games. I play it endlessly 27 00:01:24,360 --> 00:01:26,200 Speaker 1: on car trips. I've annoyed all my friends. I all 28 00:01:26,280 --> 00:01:29,920 Speaker 1: text would you rather? Okay? Would you rather? Be able 29 00:01:29,920 --> 00:01:32,880 Speaker 1: to use the internet only for twenty minutes every day? 30 00:01:33,480 --> 00:01:36,440 Speaker 1: Or you can use it like normal? But your boss 31 00:01:36,480 --> 00:01:39,839 Speaker 1: gets a detailed report of everything you did online every day? 32 00:01:41,480 --> 00:01:45,800 Speaker 3: Oh my god, I haven't think of all that now, 33 00:01:46,120 --> 00:01:49,800 Speaker 3: Like what do I do on the internet? I feel 34 00:01:49,800 --> 00:01:55,200 Speaker 3: like I need to go through Okay, that's rough, Like 35 00:01:56,400 --> 00:01:58,280 Speaker 3: this is my problem. I get way too strong, like 36 00:01:58,360 --> 00:02:00,520 Speaker 3: I love these things, but also and then I spend 37 00:02:00,520 --> 00:02:01,160 Speaker 3: too much time. 38 00:02:01,040 --> 00:02:04,200 Speaker 2: Being like but what would I do? What if this 39 00:02:04,400 --> 00:02:08,920 Speaker 2: was real? I don't know. I uh, but I think. 40 00:02:11,200 --> 00:02:13,440 Speaker 3: My boss is pretty chill. That's what I so I'm 41 00:02:13,440 --> 00:02:16,240 Speaker 3: thinking of, like my boss currently. Hi, Maya, if you're 42 00:02:16,280 --> 00:02:22,000 Speaker 3: listening to this, But like I wouldn't like I feel 43 00:02:22,040 --> 00:02:24,640 Speaker 3: like that would be fine, Like I wouldn't like there's 44 00:02:24,639 --> 00:02:26,520 Speaker 3: nothing I write, yeah, Like she would just sort of 45 00:02:26,520 --> 00:02:28,919 Speaker 3: be like all right, whatever, Like Maya wouldn't even look 46 00:02:29,000 --> 00:02:31,120 Speaker 3: do you spend like an embarrassing amount of time on 47 00:02:31,160 --> 00:02:31,680 Speaker 3: like Twitter? 48 00:02:31,919 --> 00:02:33,720 Speaker 2: So like I don't, like, I. 49 00:02:33,760 --> 00:02:35,919 Speaker 3: Don't really know if I want that like out into 50 00:02:35,960 --> 00:02:37,920 Speaker 3: the world. 51 00:02:38,240 --> 00:02:39,120 Speaker 2: I'm gonna say. 52 00:02:39,040 --> 00:02:42,480 Speaker 3: Tentatively, I think my boss gets a detailed report on 53 00:02:42,560 --> 00:02:47,240 Speaker 3: what internet just because I can't like the idea of 54 00:02:47,280 --> 00:02:49,200 Speaker 3: only having twenty minutes on the Internet, Like I'm like, 55 00:02:49,240 --> 00:02:52,240 Speaker 3: that is not I don't beyond even just like the 56 00:02:52,280 --> 00:02:56,000 Speaker 3: normal stuff I like need to look up, Like I 57 00:02:56,040 --> 00:02:58,040 Speaker 3: don't know how to like function, Like I don't think 58 00:02:58,080 --> 00:03:00,359 Speaker 3: I've ever cooked a meal without ever having to look 59 00:03:00,440 --> 00:03:06,360 Speaker 3: up something like really stupid, Like I don't know. Yeah, yeah, 60 00:03:06,400 --> 00:03:09,080 Speaker 3: that's my answer. What about you, bridget what about you? 61 00:03:09,280 --> 00:03:13,840 Speaker 1: Well, I'm my own boss. Really, I will say, you're 62 00:03:13,880 --> 00:03:16,600 Speaker 1: to your point about like the things that I have 63 00:03:16,680 --> 00:03:20,560 Speaker 1: to google when I'm cooking, like a tablespoon is you 64 00:03:20,560 --> 00:03:23,399 Speaker 1: know how many cups into tables? Like things I feel 65 00:03:23,400 --> 00:03:25,440 Speaker 1: like I should have learned in grammar school, Like if 66 00:03:25,480 --> 00:03:28,640 Speaker 1: I if people, if if another human got to see 67 00:03:29,080 --> 00:03:31,160 Speaker 1: the level of a nane things that I have to 68 00:03:31,240 --> 00:03:33,480 Speaker 1: check via Google, I will be very embarrassed. 69 00:03:33,840 --> 00:03:34,639 Speaker 2: Oh the other one. 70 00:03:34,680 --> 00:03:38,200 Speaker 3: Recently, I've been working on a project currently. That's making 71 00:03:38,240 --> 00:03:40,720 Speaker 3: me go through some like Jstore articles and some like 72 00:03:40,800 --> 00:03:43,520 Speaker 3: really academic stuff, which is super fun. 73 00:03:43,560 --> 00:03:46,000 Speaker 2: I love doing that stuff, like honestly, like that's kind 74 00:03:46,000 --> 00:03:47,520 Speaker 2: of shit. I like, I'll nerd out over. 75 00:03:48,760 --> 00:03:51,800 Speaker 3: However, it's been a minute since I've read like academic articles, 76 00:03:51,880 --> 00:03:54,560 Speaker 3: and I don't There's a lot of words that I've 77 00:03:54,600 --> 00:03:58,360 Speaker 3: had to google just like that are like weird words. 78 00:03:58,000 --> 00:04:00,400 Speaker 2: Like like but yeah, like I I feel like my 79 00:04:00,600 --> 00:04:02,320 Speaker 2: my recently. 80 00:04:02,400 --> 00:04:04,400 Speaker 3: It's either that or like if I'm writing something and 81 00:04:04,400 --> 00:04:06,320 Speaker 3: I'll be like, I keep spelling this word wrong. Those 82 00:04:06,320 --> 00:04:09,280 Speaker 3: are usually like stupid ones like that's usually like I 83 00:04:09,280 --> 00:04:11,800 Speaker 3: can't remember how to spell like schedule or something like 84 00:04:11,880 --> 00:04:12,320 Speaker 3: I don't know. 85 00:04:14,400 --> 00:04:16,320 Speaker 2: That might be a little embarrassing, but you know what, 86 00:04:17,200 --> 00:04:19,960 Speaker 2: yeah I can't. I can't. I don't know what words mean. 87 00:04:20,120 --> 00:04:24,800 Speaker 1: I think, okay, so here there are no girls on 88 00:04:24,800 --> 00:04:27,599 Speaker 1: the internet. We have been watching the Republican debate so 89 00:04:27,640 --> 00:04:32,120 Speaker 1: that y'all don't have to. It has been interesting and frustrating. 90 00:04:32,360 --> 00:04:35,640 Speaker 1: I would say like eighty percent of what comes up 91 00:04:35,680 --> 00:04:38,120 Speaker 1: on the on the Republican Debate so far has been 92 00:04:38,200 --> 00:04:41,679 Speaker 1: like stunts or scams or grand standing or conspiracy theories, 93 00:04:41,760 --> 00:04:45,280 Speaker 1: and then maybe like twenty percent is actual policy discussions 94 00:04:45,640 --> 00:04:48,400 Speaker 1: genuinely I've had. When I watched it this week, I 95 00:04:48,440 --> 00:04:50,120 Speaker 1: had to turn it off for a little while and 96 00:04:50,160 --> 00:04:53,360 Speaker 1: like collect myself and then return to it. So technology 97 00:04:53,520 --> 00:04:56,479 Speaker 1: has come up in most of these debates, specifically whether 98 00:04:56,640 --> 00:04:59,440 Speaker 1: or not TikTok should be banned. Former South Carolina Governor 99 00:04:59,520 --> 00:05:02,480 Speaker 1: Nikki Hayley has been all in on banning TikTok, and 100 00:05:02,560 --> 00:05:05,960 Speaker 1: in this week's debate, she dropped a startling statistic that 101 00:05:06,160 --> 00:05:10,160 Speaker 1: just thirty minutes spent on TikTok makes people seventeen percent 102 00:05:10,480 --> 00:05:11,800 Speaker 1: more anti semitic. 103 00:05:12,720 --> 00:05:15,880 Speaker 4: We really do need to ban TikTok once and for all. 104 00:05:15,960 --> 00:05:19,280 Speaker 4: And let me tell you why. For every thirty minutes 105 00:05:19,920 --> 00:05:25,679 Speaker 4: that someone watches TikTok every day, they become seventeen percent 106 00:05:25,880 --> 00:05:29,240 Speaker 4: more anti Semitic, more pro hamas based on doing. 107 00:05:29,040 --> 00:05:33,960 Speaker 1: That, which is like a pretty big claim. So whenever 108 00:05:34,000 --> 00:05:36,960 Speaker 1: I hear a candidate make like a clear specific claim 109 00:05:37,040 --> 00:05:39,479 Speaker 1: like that, you gotta dig into it to find more, right, 110 00:05:39,600 --> 00:05:41,560 Speaker 1: because it should be pretty cut and dry, right, like 111 00:05:41,680 --> 00:05:44,240 Speaker 1: find the stat confirm whether it's accurate or not. So 112 00:05:44,279 --> 00:05:46,360 Speaker 1: I thought this is going to be a straightforward fact 113 00:05:46,400 --> 00:05:49,000 Speaker 1: check for y'all when I tell you this was so 114 00:05:49,120 --> 00:05:52,599 Speaker 1: much more complicated. So that seventeen percent figure that Haley 115 00:05:52,640 --> 00:05:56,159 Speaker 1: cited actually came from a data analysis shared on Twitter 116 00:05:56,200 --> 00:05:59,840 Speaker 1: on November thirtieth by Anthony Goldbloom. Goldbloom is the former 117 00:06:00,080 --> 00:06:03,279 Speaker 1: CEO of a data science platform called Cagold. I looked 118 00:06:03,320 --> 00:06:06,680 Speaker 1: into Goldbloom. He is like a macro economist from Australia. 119 00:06:06,760 --> 00:06:12,520 Speaker 1: So first, Hayley misstated what Goldbloom's analysis found. Gold Bloom's 120 00:06:12,520 --> 00:06:15,560 Speaker 1: analysis suggests that people who spend thirty minutes a day 121 00:06:15,600 --> 00:06:18,280 Speaker 1: or more on TikTok are seventeen percent more likely than 122 00:06:18,279 --> 00:06:21,560 Speaker 1: others to hold antisemitic or anti Israel views. We'll come 123 00:06:21,560 --> 00:06:23,760 Speaker 1: back to that point in just a moment. For comparison, 124 00:06:23,839 --> 00:06:26,960 Speaker 1: According to this analysis, use of Instagram for thirty minutes 125 00:06:26,960 --> 00:06:29,520 Speaker 1: today was associated with a six percent increase in the 126 00:06:29,560 --> 00:06:31,960 Speaker 1: likelihood of anti Semitic views, and the use of Twitter 127 00:06:32,000 --> 00:06:34,599 Speaker 1: for thirty minutes today was associated with a two percent 128 00:06:34,640 --> 00:06:38,320 Speaker 1: increase in likelihood. Side note, When Goldbloom tweeted this, you 129 00:06:38,360 --> 00:06:41,880 Speaker 1: know Elon Musk was in the thread being like interesting. 130 00:06:42,120 --> 00:06:45,400 Speaker 1: This says that actually Twitter is not a big driver 131 00:06:45,520 --> 00:06:48,200 Speaker 1: of anti Semitism. You don't say that's great for me, 132 00:06:49,040 --> 00:06:54,200 Speaker 1: unsurprising Gloom. So Hayley has definitely misstated the conclusions of 133 00:06:54,240 --> 00:06:57,960 Speaker 1: this analysis. She said during the debate, for every thirty 134 00:06:58,000 --> 00:07:01,000 Speaker 1: minutes that somebody watched TikTok, and that's simply not with 135 00:07:01,080 --> 00:07:04,320 Speaker 1: this analysis suggests the state of conclusions of the analysis 136 00:07:04,360 --> 00:07:07,200 Speaker 1: are that people who spend thirty minutes or more on 137 00:07:07,240 --> 00:07:10,680 Speaker 1: TikTok every day are seventeen percent more likely to hold 138 00:07:10,800 --> 00:07:14,000 Speaker 1: anti Semitic attitudes. What it does not suggest is that 139 00:07:14,040 --> 00:07:17,880 Speaker 1: spending thirty minutes on TikTok will bolster someone's anti semitic 140 00:07:17,960 --> 00:07:22,840 Speaker 1: use by seventeen by sev which is like, like, that's 141 00:07:22,840 --> 00:07:24,240 Speaker 1: like a real like how this. 142 00:07:24,280 --> 00:07:25,360 Speaker 2: Would get seventeen? 143 00:07:25,520 --> 00:07:27,920 Speaker 3: I want I want to like, I would love for 144 00:07:27,960 --> 00:07:33,400 Speaker 3: an interviewer to ask Indicky Haley how exactly somebody gets 145 00:07:33,400 --> 00:07:35,640 Speaker 3: sevent Like, what is the metric to measure that? What 146 00:07:35,760 --> 00:07:39,280 Speaker 3: is seventeen more anti semitic? Seventeen percent more anti semitic 147 00:07:39,320 --> 00:07:40,560 Speaker 3: than what you previously were. 148 00:07:40,920 --> 00:07:44,200 Speaker 1: That's yeah, So I think that's why what you said. 149 00:07:44,240 --> 00:07:47,560 Speaker 1: I think that's exactly why people are really responding to 150 00:07:47,640 --> 00:07:50,720 Speaker 1: this on Twitter, that people are like, like, what is 151 00:07:50,760 --> 00:07:53,240 Speaker 1: the metric that you're using? Like, how are you quantifying this? 152 00:07:53,600 --> 00:07:55,520 Speaker 1: Had I been the moderator of that debate, I would 153 00:07:55,520 --> 00:07:58,000 Speaker 1: have definitely pressed her because it's like you can't drop 154 00:07:58,040 --> 00:08:00,000 Speaker 1: a stat like that and then you just like, oh, well, 155 00:08:00,680 --> 00:08:03,200 Speaker 1: seventeen percent, my am I anyway, moving on, like that's 156 00:08:03,240 --> 00:08:05,960 Speaker 1: really a stat that like, she clearly got it from somewhere, 157 00:08:06,000 --> 00:08:08,480 Speaker 1: and so I do think the moderators it would have 158 00:08:08,480 --> 00:08:10,760 Speaker 1: behooved them to ask a little bit more about where 159 00:08:10,840 --> 00:08:13,880 Speaker 1: that stat came from. So she is just flat out 160 00:08:14,080 --> 00:08:17,760 Speaker 1: misstating what that analysis actually said. But then there's the 161 00:08:17,840 --> 00:08:20,960 Speaker 1: issue of the analysis itself that is a bit more complicated. 162 00:08:21,120 --> 00:08:22,920 Speaker 1: So I want to be super clear that I am 163 00:08:23,000 --> 00:08:25,920 Speaker 1: like no researcher. I don't have an educational background in 164 00:08:25,960 --> 00:08:29,040 Speaker 1: this at all. But our producer Mike has a PhD 165 00:08:29,160 --> 00:08:31,480 Speaker 1: and does survey research for a living. I had Mike 166 00:08:31,520 --> 00:08:33,560 Speaker 1: take a look at the analysis and he raised a 167 00:08:33,559 --> 00:08:37,520 Speaker 1: few questions. First, Goldbloom does not describe his methods in 168 00:08:37,559 --> 00:08:40,120 Speaker 1: the survey, so we don't know a ton about how 169 00:08:40,160 --> 00:08:43,000 Speaker 1: he came to these conclusions around that seventeen percent figure. 170 00:08:43,200 --> 00:08:45,880 Speaker 1: It's entirely possible that these methods are perfectly sound. But 171 00:08:45,960 --> 00:08:48,520 Speaker 1: because when he shared his analysis, he did not also 172 00:08:48,640 --> 00:08:51,160 Speaker 1: share his methods, we have no idea and we can't 173 00:08:51,200 --> 00:08:53,240 Speaker 1: even look into it to say. This was not a 174 00:08:53,280 --> 00:08:56,400 Speaker 1: study published in a scientific journal. It is just an 175 00:08:56,400 --> 00:08:59,720 Speaker 1: analysis that somebody posted on Twitter and GitHub, So definitely 176 00:08:59,760 --> 00:09:02,240 Speaker 1: like keep that in mind when thinking about this. The 177 00:09:02,320 --> 00:09:05,600 Speaker 1: stat second is the way that Goldbloom framed the survey 178 00:09:05,720 --> 00:09:08,240 Speaker 1: questions to determine how often that people responding to the 179 00:09:08,280 --> 00:09:12,040 Speaker 1: survey use TikTok. According to his data, ninety percent of 180 00:09:12,080 --> 00:09:15,280 Speaker 1: all respondents use TikTok at least once a day, which 181 00:09:15,320 --> 00:09:18,920 Speaker 1: is certainly not representative of the larger United States population. 182 00:09:19,200 --> 00:09:21,680 Speaker 1: He also did not control for age. You know, we 183 00:09:21,760 --> 00:09:24,240 Speaker 1: know that folks who use TikTok tend to skew younger, 184 00:09:24,440 --> 00:09:26,920 Speaker 1: and we also know that younger people tend to be 185 00:09:26,960 --> 00:09:30,480 Speaker 1: more sympathetic to Palestinians. So in his correlational study, in 186 00:09:30,520 --> 00:09:33,160 Speaker 1: which he didn't control for age at all, it is 187 00:09:33,200 --> 00:09:35,680 Speaker 1: possible that the entire effect is driven about the fact 188 00:09:35,679 --> 00:09:37,760 Speaker 1: that the people who use TikTok tend to be younger 189 00:09:37,760 --> 00:09:40,679 Speaker 1: than the people who don't. We just don't know. So 190 00:09:40,920 --> 00:09:42,920 Speaker 1: another points that I wanted to make which this is 191 00:09:42,960 --> 00:09:47,839 Speaker 1: coming from noted non researcher me Fridgid and not researcher Mike. 192 00:09:48,160 --> 00:09:51,840 Speaker 1: Is the way that Goldbloom correlates anti Israel views with 193 00:09:51,880 --> 00:09:55,600 Speaker 1: anti Semitic views on Twitter, Goldbloom fleshed this out saying 194 00:09:55,800 --> 00:09:58,640 Speaker 1: TikTok users are more likely to believe Jewish people are 195 00:09:58,640 --> 00:10:02,160 Speaker 1: dishonest in business, are disloyal to America, and have too 196 00:10:02,200 --> 00:10:04,960 Speaker 1: much power in media. They're also more likely to disagree 197 00:10:05,000 --> 00:10:07,199 Speaker 1: that Israel has a right to defend itself against those 198 00:10:07,200 --> 00:10:09,120 Speaker 1: who want to destroy it. And I guess I just 199 00:10:09,160 --> 00:10:12,960 Speaker 1: have questions about this framing that puts very clear anti 200 00:10:12,960 --> 00:10:16,679 Speaker 1: Semitic tropes like Jewish people have too much power alongside 201 00:10:16,760 --> 00:10:20,280 Speaker 1: specific policy opinions about the state of Israel to make 202 00:10:20,320 --> 00:10:24,240 Speaker 1: the overall claim about TikTok users being anti Semitic. I 203 00:10:24,280 --> 00:10:28,200 Speaker 1: just have questions about how those survey questions are being 204 00:10:28,280 --> 00:10:30,520 Speaker 1: understood in this in this analysis. 205 00:10:30,920 --> 00:10:34,960 Speaker 2: Yeah, that's definitely like the biggest dread flag for me. 206 00:10:37,360 --> 00:10:43,720 Speaker 3: Also not I'm also not a preno professional researcher. I 207 00:10:43,760 --> 00:10:46,360 Speaker 3: did how every good a journalism school, and they teach 208 00:10:46,400 --> 00:10:50,320 Speaker 3: to us a lot about you know, not asking leading 209 00:10:50,400 --> 00:10:54,080 Speaker 3: questions or not how framing questions in a specific way 210 00:10:54,200 --> 00:10:56,920 Speaker 3: like kind of determines how Like I don't know, I 211 00:10:57,080 --> 00:10:59,439 Speaker 3: learned that at like ap stats too, Like that's a 212 00:10:59,440 --> 00:11:02,640 Speaker 3: pretty big concept. And this is like a what it 213 00:11:02,679 --> 00:11:08,520 Speaker 3: does whenever you have something that's talking about anti semitism 214 00:11:08,760 --> 00:11:12,280 Speaker 3: and you are also roping in the most basic questions 215 00:11:12,280 --> 00:11:16,240 Speaker 3: about Israel and the Israeli military and like yeah, like 216 00:11:16,280 --> 00:11:23,240 Speaker 3: this said specific policy questions, Like that's that is so 217 00:11:23,320 --> 00:11:28,160 Speaker 3: much more complicated than just simply asking about anti semitism. 218 00:11:29,000 --> 00:11:31,760 Speaker 3: And also then what it ends up doing is falsely 219 00:11:31,760 --> 00:11:36,439 Speaker 3: equivocating any sort of criticism of Israel with anti semitism, 220 00:11:36,440 --> 00:11:37,240 Speaker 3: which is not true. 221 00:11:38,320 --> 00:11:41,720 Speaker 2: It is not like look like I we're in the middle. 222 00:11:41,760 --> 00:11:44,520 Speaker 3: I mean, and I think now, especially now we're seeing this, 223 00:11:44,640 --> 00:11:47,079 Speaker 3: like there's a lot of like people that are Jewish 224 00:11:47,120 --> 00:11:50,400 Speaker 3: that are very critical of Israel or Zionism or like 225 00:11:50,440 --> 00:11:54,440 Speaker 3: the Israeli state. There are people I know that Like, 226 00:11:54,520 --> 00:11:56,000 Speaker 3: I mean, I don't know, I have family members that 227 00:11:56,040 --> 00:11:59,559 Speaker 3: aren't even like super that are definitely like Zionists, that 228 00:11:59,600 --> 00:12:00,640 Speaker 3: are definitely more. 229 00:12:00,880 --> 00:12:03,880 Speaker 2: Pro Israel, but like they're very critical of the Israeli state. 230 00:12:03,960 --> 00:12:07,800 Speaker 3: Because the estate is currently like has a super far 231 00:12:07,880 --> 00:12:12,040 Speaker 3: right government in charge. But yeah, no, that's that is 232 00:12:12,080 --> 00:12:14,440 Speaker 3: a weird Like I think that that to me, just 233 00:12:14,480 --> 00:12:18,360 Speaker 3: hearing that he put those two things together and use 234 00:12:18,480 --> 00:12:20,760 Speaker 3: that as one sort of set of data and to 235 00:12:20,840 --> 00:12:24,120 Speaker 3: make like one assumption about whether or not people are 236 00:12:24,120 --> 00:12:28,920 Speaker 3: anti smatic and hold antismatic views, that to me just 237 00:12:29,000 --> 00:12:32,000 Speaker 3: qualifies the entire results of the survey, because if you're 238 00:12:32,040 --> 00:12:35,000 Speaker 3: if you're not making that distinction, you're not going to 239 00:12:35,040 --> 00:12:36,120 Speaker 3: get accurate results. 240 00:12:36,360 --> 00:12:40,760 Speaker 1: Yeah, and I want to be clear, like I am fully, 241 00:12:40,920 --> 00:12:44,840 Speaker 1: fully ready to believe that it's possible that TikTok drives 242 00:12:44,880 --> 00:12:48,560 Speaker 1: harmful attitudes like anti semitism. We already know that TikTok, 243 00:12:48,640 --> 00:12:51,640 Speaker 1: Like this is something that happens on social media platforms. 244 00:12:51,679 --> 00:12:54,000 Speaker 1: This is not like a surprise media matters that an 245 00:12:54,000 --> 00:12:57,080 Speaker 1: analysis that found that TikTok recommends users content that is 246 00:12:57,120 --> 00:13:01,920 Speaker 1: misogynistic and deeply transphobic and and uses that kind of 247 00:13:02,760 --> 00:13:06,600 Speaker 1: uses transphobia and misogyny as a entry point to lead 248 00:13:06,600 --> 00:13:09,800 Speaker 1: them down far right rabbit holes. And so that is 249 00:13:09,840 --> 00:13:12,160 Speaker 1: the thing, right, But I wanted to bring this up 250 00:13:12,240 --> 00:13:14,559 Speaker 1: because I do think it says something about our current 251 00:13:14,640 --> 00:13:17,280 Speaker 1: like political and media climate right now, it is very 252 00:13:17,360 --> 00:13:20,199 Speaker 1: weird to me that someone running for office, running for 253 00:13:20,400 --> 00:13:24,920 Speaker 1: president could clearly and specifically state something as an an 254 00:13:25,520 --> 00:13:29,920 Speaker 1: a clear fact based on just an analysis that somebody 255 00:13:29,960 --> 00:13:32,640 Speaker 1: put out there on Twitter, and that that claim would 256 00:13:32,679 --> 00:13:35,679 Speaker 1: be this difficult and complicated to verify. You know, I've 257 00:13:35,720 --> 00:13:38,920 Speaker 1: seen people like dismissing Haley's claims, which I also like 258 00:13:39,000 --> 00:13:41,719 Speaker 1: kind of understand because it does. Like I was when 259 00:13:41,760 --> 00:13:43,520 Speaker 1: I was like looking into this, I was ready to 260 00:13:43,520 --> 00:13:47,520 Speaker 1: be like wanted Nikki Haley completely misunderstand or like, why 261 00:13:47,640 --> 00:13:51,160 Speaker 1: did she pull this completely ridiculous stat from thought that 262 00:13:51,200 --> 00:13:52,600 Speaker 1: I thought it was going to be like a made 263 00:13:52,679 --> 00:13:56,560 Speaker 1: up stat that she just like fabricated. And I'm realizing 264 00:13:56,640 --> 00:14:00,280 Speaker 1: now that like that isn't necessarily the case. And I 265 00:14:00,320 --> 00:14:04,800 Speaker 1: actually do think it matters how platforms are shaping and 266 00:14:04,880 --> 00:14:10,640 Speaker 1: stoking social attitudes. But I wish that we could have 267 00:14:10,720 --> 00:14:14,160 Speaker 1: that conversation in a way that doesn't misstate findings or 268 00:14:14,200 --> 00:14:18,120 Speaker 1: that does it spotlight you know, analysis that's just murky 269 00:14:18,240 --> 00:14:19,920 Speaker 1: murky in the sense of like it's not clear how 270 00:14:19,960 --> 00:14:22,200 Speaker 1: it was how it was gathered, right, So, like I 271 00:14:22,240 --> 00:14:25,080 Speaker 1: don't think that this is actually helping us to get 272 00:14:25,080 --> 00:14:27,760 Speaker 1: there and I actually would be curious to have that conversation. 273 00:14:28,640 --> 00:14:31,080 Speaker 3: Yeah, no, definitely, I think this is a weird one 274 00:14:31,080 --> 00:14:33,360 Speaker 3: because I also, Yeah, I think my immediate i'd seen that, 275 00:14:33,440 --> 00:14:35,160 Speaker 3: Like I saw that clip on Twitter, and my immediate 276 00:14:35,160 --> 00:14:37,760 Speaker 3: response was just like, oh my god, Like, of course 277 00:14:38,000 --> 00:14:39,720 Speaker 3: Nikia Harley is saying some weird stuff. 278 00:14:40,880 --> 00:14:44,160 Speaker 2: But yeah, no, it definitely is indicative of something. 279 00:14:44,560 --> 00:14:48,480 Speaker 3: And I think it also like the way the fact 280 00:14:48,480 --> 00:14:52,760 Speaker 3: that like something like TikTok that's just a platform has 281 00:14:52,760 --> 00:14:55,840 Speaker 3: been politicized so much, like that makes it so hard 282 00:14:55,920 --> 00:14:59,400 Speaker 3: to actually analyze like what we're talking about and analyze 283 00:14:59,400 --> 00:15:01,880 Speaker 3: the way that yeah, like what there is a conversation 284 00:15:01,920 --> 00:15:04,160 Speaker 3: to be had about these sites like stoking hate and 285 00:15:04,320 --> 00:15:06,560 Speaker 3: like not to say any semitism, but yeah, like racism 286 00:15:06,560 --> 00:15:11,840 Speaker 3: and transphobia, homophobia, misogyny. There is an there's a conversation 287 00:15:11,920 --> 00:15:14,320 Speaker 3: to be had there, and it's important that we like 288 00:15:15,120 --> 00:15:18,800 Speaker 3: figure out ways to address that problem. But like there's 289 00:15:18,800 --> 00:15:21,320 Speaker 3: so many intersexting issues here. A it's the fact that 290 00:15:21,400 --> 00:15:23,840 Speaker 3: TikTok's been politicized, and it's so hard to actually do 291 00:15:23,880 --> 00:15:26,640 Speaker 3: that research when it's been politicized in this way. 292 00:15:26,680 --> 00:15:28,080 Speaker 2: And then on the other hand, like, I think this. 293 00:15:28,120 --> 00:15:31,920 Speaker 3: Is why it is so important to distinguish between what 294 00:15:32,160 --> 00:15:35,680 Speaker 3: is actually anti Semitic and what is simply criticism of 295 00:15:35,720 --> 00:15:41,280 Speaker 3: Israel that's not necessarily anti smetic, because it's so like 296 00:15:41,360 --> 00:15:43,240 Speaker 3: I feel like I'm having this This has been a 297 00:15:43,320 --> 00:15:46,120 Speaker 3: frustrating thing lately when trying to find just like statistics 298 00:15:46,160 --> 00:15:48,920 Speaker 3: and info about anti semitism in particular, is it so 299 00:15:49,000 --> 00:15:53,400 Speaker 3: much of it is so hard to like really get 300 00:15:53,560 --> 00:15:57,320 Speaker 3: real clear answers just because yeah, so many people, whether 301 00:15:57,960 --> 00:16:01,920 Speaker 3: just sort of by assumption or like with a specific agenda, 302 00:16:02,040 --> 00:16:07,040 Speaker 3: have included criticism of Israel or have included criticism of 303 00:16:07,080 --> 00:16:11,160 Speaker 3: Zionism within their analysis of anti semitism. 304 00:16:11,960 --> 00:16:12,520 Speaker 1: And I'm not. 305 00:16:12,440 --> 00:16:15,040 Speaker 3: Saying, like again not saying that aren't criticisms are all 306 00:16:15,040 --> 00:16:18,480 Speaker 3: of Israel and Zionism out there that turned into antisemitic 307 00:16:18,880 --> 00:16:19,680 Speaker 3: talking points. 308 00:16:20,040 --> 00:16:22,440 Speaker 2: Obviously that is a thing too, and that is bad. 309 00:16:22,440 --> 00:16:25,160 Speaker 3: But like those you should be addressing those as anti semitism, 310 00:16:25,280 --> 00:16:27,080 Speaker 3: and because of the anti semitism that go, not because 311 00:16:27,080 --> 00:16:32,000 Speaker 3: they're just criticizing a government that is separate from just 312 00:16:32,040 --> 00:16:34,360 Speaker 3: an a group of people that share an identity. 313 00:16:34,960 --> 00:16:38,320 Speaker 2: But yeah, that is it's weird. It is a weird, 314 00:16:38,480 --> 00:16:39,920 Speaker 2: Like it's one. 315 00:16:39,800 --> 00:16:41,200 Speaker 3: Of those I feel like this is another one of 316 00:16:41,240 --> 00:16:43,440 Speaker 3: those things where like some of the other Republican parties 317 00:16:43,480 --> 00:16:45,760 Speaker 3: and something really weird and it's like a meme for 318 00:16:45,800 --> 00:16:47,920 Speaker 3: a bit and then you like we all think about 319 00:16:47,960 --> 00:16:50,640 Speaker 3: it like a week later and are like, oh, oh no, 320 00:16:50,840 --> 00:16:53,000 Speaker 3: this is bad, like these are and again like this 321 00:16:53,040 --> 00:16:56,080 Speaker 3: is somebody running for president of the United States. 322 00:16:56,720 --> 00:16:57,000 Speaker 1: Yeah. 323 00:16:57,120 --> 00:17:00,240 Speaker 2: Also, I'm sorry, I don't like this is the like 324 00:17:00,280 --> 00:17:01,240 Speaker 2: the Republican Party. 325 00:17:01,520 --> 00:17:04,280 Speaker 3: This is y'all are the like Jewish space Lasers party, 326 00:17:04,400 --> 00:17:07,280 Speaker 3: Like you can't be out here being like it's chip 327 00:17:07,359 --> 00:17:10,119 Speaker 3: took that's making people antise medic Like I don't know, 328 00:17:10,240 --> 00:17:15,760 Speaker 3: that's the calls were coming from inside the house, Like yeah. 329 00:17:14,440 --> 00:17:16,639 Speaker 1: I was literally just thinking that when you were saying that, 330 00:17:16,680 --> 00:17:18,720 Speaker 1: I was thinking this. I was thinking the same line 331 00:17:18,800 --> 00:17:26,160 Speaker 1: like me coming from some of the houses on that one. 332 00:17:27,320 --> 00:17:43,520 Speaker 1: Let's take a quick break at her back, so we 333 00:17:43,600 --> 00:17:46,159 Speaker 1: got to talk about this Amazon lawsuit. Quick heads up 334 00:17:46,200 --> 00:17:50,600 Speaker 1: at this conversation does involve the sexual exploitation of a minor. 335 00:17:50,720 --> 00:17:54,240 Speaker 1: So two years ago, a Brazilian teenager was participating in 336 00:17:54,280 --> 00:17:57,080 Speaker 1: a foreign exchange student program, so she was living with 337 00:17:57,119 --> 00:18:00,159 Speaker 1: a host family in West Virginia, and while she was there, 338 00:18:00,600 --> 00:18:03,359 Speaker 1: she discovered that her host family or someone in the 339 00:18:03,440 --> 00:18:07,680 Speaker 1: host family had put a spy camera purchased from Amazon 340 00:18:07,960 --> 00:18:10,679 Speaker 1: in her bathroom to obtain intimate footage of her in 341 00:18:10,720 --> 00:18:14,000 Speaker 1: the bathroom. So now she is suing Amazon for selling 342 00:18:14,040 --> 00:18:18,160 Speaker 1: a product that was advertised for potentially illicit uses and 343 00:18:18,400 --> 00:18:21,639 Speaker 1: was allegedly flagged to Amazon's product safety team, which her 344 00:18:21,680 --> 00:18:24,840 Speaker 1: suit says inspected the sale of that camera on their 345 00:18:24,880 --> 00:18:28,640 Speaker 1: platform three times and still failed to prevent the severe 346 00:18:29,000 --> 00:18:32,360 Speaker 1: foreseeable harms that still affect her today. I'm sure those 347 00:18:32,359 --> 00:18:35,840 Speaker 1: harms do affect her today. That is horrible. So Amazon 348 00:18:35,840 --> 00:18:37,879 Speaker 1: filed a motion to dismiss the case back in March, 349 00:18:38,160 --> 00:18:41,080 Speaker 1: but this week the judge hearing the case did not 350 00:18:41,359 --> 00:18:44,840 Speaker 1: buy most of Amazon's arguments, so the case will proceed 351 00:18:44,880 --> 00:18:47,360 Speaker 1: to discovery and maybe even a trial. The judge let 352 00:18:47,560 --> 00:18:52,680 Speaker 1: or claims stand negligence, product viability, torts of outrage which 353 00:18:52,760 --> 00:18:56,520 Speaker 1: is the intentional infliction of emotional distress, and civil conspiracy, 354 00:18:56,880 --> 00:19:00,280 Speaker 1: but the judge did dismiss our racketeering claim against ammaz On. 355 00:19:00,640 --> 00:19:03,760 Speaker 1: So this case is super disturbing to me, and it 356 00:19:03,840 --> 00:19:07,800 Speaker 1: also highlights that Amazon's marketplace does knowingly sell things that 357 00:19:07,840 --> 00:19:12,000 Speaker 1: are pretty obviously not just used for illegal harmful behavior, 358 00:19:12,200 --> 00:19:15,960 Speaker 1: but basically like advertised that they can be used for 359 00:19:16,040 --> 00:19:19,240 Speaker 1: that illegal harmful behavior. The camera that her host family 360 00:19:19,480 --> 00:19:23,199 Speaker 1: had bought on Amazon is called hidden Clothes hook camera. 361 00:19:23,480 --> 00:19:25,800 Speaker 1: It's a camera that it's meant to look like a 362 00:19:25,920 --> 00:19:28,600 Speaker 1: hook that you would hang a towel on in a bathroom. 363 00:19:28,880 --> 00:19:31,520 Speaker 1: And in fact, images that appear in the marketing on 364 00:19:31,560 --> 00:19:34,920 Speaker 1: Amazon's website advertising the camera show a bunch of bath 365 00:19:35,040 --> 00:19:37,840 Speaker 1: towels and they have the copy that says quote it 366 00:19:37,920 --> 00:19:43,480 Speaker 1: won't attract any attention underline and a very ordinary hook yuck. 367 00:19:44,080 --> 00:19:47,640 Speaker 1: So it's pretty clear like but what they are advertising 368 00:19:47,720 --> 00:19:51,080 Speaker 1: right like side note on this, I have seen products 369 00:19:51,080 --> 00:19:53,960 Speaker 1: for sale on Amazon that could be used for other 370 00:19:54,080 --> 00:19:57,240 Speaker 1: kinds of illicit purposes, but they actually are not super 371 00:19:57,400 --> 00:20:00,520 Speaker 1: explicit about this. Like when you buy a sk on 372 00:20:00,600 --> 00:20:04,240 Speaker 1: Amazon that could be used to weigh drugs, for instance, 373 00:20:04,440 --> 00:20:07,960 Speaker 1: on Amazon, they'll show like a tiny piece of green 374 00:20:08,119 --> 00:20:11,359 Speaker 1: lettuce being late being weighed on that scale, not like 375 00:20:11,400 --> 00:20:13,960 Speaker 1: a piece of marijuana, because they don't they don't want 376 00:20:13,960 --> 00:20:16,760 Speaker 1: to advertise it can be used for weighing marijuana, so 377 00:20:16,800 --> 00:20:20,160 Speaker 1: they it's like a roundabout way of them not advertising 378 00:20:20,200 --> 00:20:21,800 Speaker 1: that this can be used for less of behavior. 379 00:20:22,320 --> 00:20:23,200 Speaker 2: I'm not gonna lie. 380 00:20:23,440 --> 00:20:27,280 Speaker 3: I totally forgot that it's still technically illegal to buy weed. 381 00:20:27,080 --> 00:20:31,680 Speaker 2: In most states. What I was like, Oh, yeah, yeah, 382 00:20:31,680 --> 00:20:32,080 Speaker 2: all right. 383 00:20:33,880 --> 00:20:37,480 Speaker 1: Every time I leave like one of the handful of 384 00:20:37,520 --> 00:20:39,840 Speaker 1: cities and I spend a lot of time in like DC, 385 00:20:40,640 --> 00:20:45,080 Speaker 1: New York City, Brooklyn, Virginia, I'm like, oh, yeah, I 386 00:20:45,119 --> 00:20:47,040 Speaker 1: don't find myself in those places often. But when I am, 387 00:20:47,040 --> 00:20:49,440 Speaker 1: I'm like, oh yeah, y'all, don't do that down here. 388 00:20:49,720 --> 00:20:52,920 Speaker 2: Yeah. No, it's so mad. It is like, I don't 389 00:20:52,960 --> 00:20:54,359 Speaker 2: know a whole other TANGI yeah, I know. 390 00:20:54,359 --> 00:20:57,600 Speaker 3: I had a friend visiting greasily from a state where 391 00:20:57,600 --> 00:21:00,560 Speaker 3: it is not legal, and they were just like, like 392 00:21:00,600 --> 00:21:03,399 Speaker 3: we could just there's just like a store across the street. 393 00:21:03,440 --> 00:21:06,280 Speaker 2: We had got to Yeah, I know. It's anyways. 394 00:21:06,440 --> 00:21:09,120 Speaker 1: So I do think that marketing is a pretty good 395 00:21:09,200 --> 00:21:12,240 Speaker 1: argument for the fact that Amazon was well aware what 396 00:21:12,359 --> 00:21:15,400 Speaker 1: this product was being used for and like how it 397 00:21:15,440 --> 00:21:18,040 Speaker 1: was being like what use case it was being advertised 398 00:21:18,320 --> 00:21:21,560 Speaker 1: to be used for, which is exactly what the lawsuit 399 00:21:21,640 --> 00:21:24,320 Speaker 1: argues to the lawsuit says that Amazon's product safety team 400 00:21:24,400 --> 00:21:26,560 Speaker 1: was aware that the close hook camera was intended for 401 00:21:26,640 --> 00:21:29,800 Speaker 1: unlawful purposes, citing exactly what I just read the marketing 402 00:21:29,840 --> 00:21:32,119 Speaker 1: that says that the hidden camera will not attract attention 403 00:21:32,200 --> 00:21:34,960 Speaker 1: from the victim, who would presumably hang a towel to 404 00:21:35,000 --> 00:21:38,320 Speaker 1: be used in drying her undressed body. The complaint reads, 405 00:21:38,560 --> 00:21:41,520 Speaker 1: the intended use of this spy camera, as explicitly portrayed 406 00:21:41,560 --> 00:21:44,840 Speaker 1: on Amazon's online retail store e g. As a towel 407 00:21:44,840 --> 00:21:48,399 Speaker 1: hook in the bathroom to secretly record undressed individuals without 408 00:21:48,400 --> 00:21:51,520 Speaker 1: their consent is a federal felony and also a crime 409 00:21:51,600 --> 00:21:54,959 Speaker 1: under West Virginia law. So the judge didn't outright dismiss 410 00:21:55,000 --> 00:21:58,000 Speaker 1: the case against Amazon, because the judge said, quote Amazon 411 00:21:58,040 --> 00:22:01,879 Speaker 1: approved product description suggesting cannsumer use of the spycam to 412 00:22:01,960 --> 00:22:05,520 Speaker 1: record private moments in the bathroom. Amazon cannot claim shock 413 00:22:05,560 --> 00:22:08,800 Speaker 1: when a consumer does just that. So there are plenty 414 00:22:08,840 --> 00:22:12,320 Speaker 1: of like non creepy reasons that somebody might put a 415 00:22:12,359 --> 00:22:15,119 Speaker 1: camera in their home. I have a lot of issues 416 00:22:15,160 --> 00:22:17,640 Speaker 1: with it, but my mom has cameras all over her 417 00:22:17,680 --> 00:22:20,199 Speaker 1: house because my dad is disabled and might be in 418 00:22:20,200 --> 00:22:22,760 Speaker 1: another room and need help. But those cameras are not 419 00:22:22,840 --> 00:22:27,560 Speaker 1: disguised as bathroom hooks. Professor Eric Goldman, who runs the 420 00:22:27,600 --> 00:22:30,400 Speaker 1: Tech and Marketing Law blog, said that this ruling could 421 00:22:30,400 --> 00:22:32,960 Speaker 1: have potentially changed the way that spycams are sold through 422 00:22:32,960 --> 00:22:36,880 Speaker 1: online retailers like Amazon. He writes, the court's analysis could 423 00:22:36,880 --> 00:22:40,760 Speaker 1: indicate that all surreptitious hook cameras are categorically illegal to sell, 424 00:22:41,160 --> 00:22:44,240 Speaker 1: even when buyers plan to use them completely legally. That 425 00:22:44,400 --> 00:22:47,280 Speaker 1: makes this a dangerous ruling for the spycam industry and 426 00:22:47,359 --> 00:22:50,200 Speaker 1: for Amazon. At the same time, I'm curious to hear 427 00:22:50,240 --> 00:22:52,960 Speaker 1: more about what Amazon's product safety team thought when it 428 00:22:53,040 --> 00:22:55,920 Speaker 1: evaluated this item, because according to the suit, they evaluated 429 00:22:55,960 --> 00:22:58,520 Speaker 1: it three times and still allow it on their platform. 430 00:22:58,840 --> 00:23:00,959 Speaker 1: And I do think it kind of goes back to 431 00:23:01,280 --> 00:23:03,800 Speaker 1: what we were talking about when we talked about TikTok shop, 432 00:23:03,880 --> 00:23:06,840 Speaker 1: Like I don't think that platforms should just be off 433 00:23:06,880 --> 00:23:09,800 Speaker 1: the hook. Like I don't think that they should be 434 00:23:09,840 --> 00:23:13,800 Speaker 1: able to knowingly profit from the sale of items that 435 00:23:13,880 --> 00:23:16,639 Speaker 1: make clear that what their intended use is for is 436 00:23:16,880 --> 00:23:18,960 Speaker 1: something that is illegal and super harmful. 437 00:23:19,320 --> 00:23:20,000 Speaker 2: Yeah. 438 00:23:20,280 --> 00:23:23,679 Speaker 3: Yeah, And like, honestly, the whole idea of having a 439 00:23:23,760 --> 00:23:27,639 Speaker 3: camera that you can disguise as like a clothing hook, 440 00:23:27,760 --> 00:23:31,720 Speaker 3: Like I don't know, I can't think of any situation 441 00:23:31,880 --> 00:23:35,760 Speaker 3: where you would need that where it like it seems justifiable. 442 00:23:35,920 --> 00:23:37,359 Speaker 3: I don't know, like honestly, like I feel like that 443 00:23:37,440 --> 00:23:40,360 Speaker 3: is something that you only use to like get footage 444 00:23:40,359 --> 00:23:45,160 Speaker 3: of people without their permission, and like this is usually 445 00:23:45,359 --> 00:23:47,000 Speaker 3: the outcome of that. Like I don't know, I feel 446 00:23:47,040 --> 00:23:49,560 Speaker 3: like specifically just designing it as like a hook, like 447 00:23:49,600 --> 00:23:51,560 Speaker 3: I can't I don't see any argument for why that's 448 00:23:51,560 --> 00:23:54,359 Speaker 3: something that somebody would need. The whole idea of like 449 00:23:54,480 --> 00:23:58,560 Speaker 3: hidden camera which comes upon the show like surveillance and 450 00:23:58,560 --> 00:24:01,920 Speaker 3: like hidden cameras, Like we're all ways too comfortable. 451 00:24:01,480 --> 00:24:02,720 Speaker 2: With this idea of hitting cameras. 452 00:24:02,880 --> 00:24:05,959 Speaker 3: I like, I seriously, like it seems really strange to me, 453 00:24:06,040 --> 00:24:09,200 Speaker 3: and it seems like this is just we're gonna see 454 00:24:09,200 --> 00:24:11,720 Speaker 3: more and more of this happening and more like of 455 00:24:11,760 --> 00:24:12,800 Speaker 3: these kind of stories. 456 00:24:13,080 --> 00:24:16,360 Speaker 1: Joey, don't even get me started. You know, it's holiday season. 457 00:24:16,640 --> 00:24:19,399 Speaker 1: I'm sure people are gonna be buying all kinds of 458 00:24:19,520 --> 00:24:24,520 Speaker 1: cameras and devices with cameras and recording devices to be 459 00:24:24,520 --> 00:24:27,119 Speaker 1: given to their loved ones to like put in their home. 460 00:24:27,640 --> 00:24:32,400 Speaker 1: So that Amazon can misuse as as they have done. 461 00:24:32,400 --> 00:24:36,000 Speaker 1: They settled a lawsuit about their staff using cameras to 462 00:24:36,080 --> 00:24:40,080 Speaker 1: like peep at consumers or so that police can get 463 00:24:40,119 --> 00:24:43,199 Speaker 1: your information, like, don't do it, take it from me, 464 00:24:43,560 --> 00:24:47,160 Speaker 1: Like like you loved your loved ones more than giving 465 00:24:47,160 --> 00:24:49,000 Speaker 1: them the gift of surveillance, give them the gift of 466 00:24:49,040 --> 00:24:54,280 Speaker 1: privacy instead. So we talk a lot about the irl 467 00:24:54,400 --> 00:24:57,760 Speaker 1: consequences of misinformation online on the show, and that means 468 00:24:57,760 --> 00:24:59,080 Speaker 1: that we really have to take a look at what's 469 00:24:59,080 --> 00:25:02,760 Speaker 1: happening in Ireland. So last week in Dublin, social media 470 00:25:02,800 --> 00:25:07,480 Speaker 1: fueled disinformation turned into a situation with very real world consequences. 471 00:25:08,000 --> 00:25:10,520 Speaker 1: Three young children and a woman were injured in a 472 00:25:10,560 --> 00:25:13,600 Speaker 1: knife attack near a school in Dublin last week. The 473 00:25:13,640 --> 00:25:16,360 Speaker 1: suspect was apprehended. I think it was like a situation 474 00:25:16,440 --> 00:25:20,000 Speaker 1: where like people walking by, people nearby just all helped 475 00:25:20,000 --> 00:25:24,199 Speaker 1: to apprehend the suspect and help the victims. The motive was, 476 00:25:24,480 --> 00:25:28,320 Speaker 1: according to police, entirely unclear. However, according to The New 477 00:25:28,400 --> 00:25:31,000 Speaker 1: York Times, the police said that the attack was followed 478 00:25:31,000 --> 00:25:34,240 Speaker 1: by destructive riots that they blamed on the far right, 479 00:25:34,320 --> 00:25:38,600 Speaker 1: weaponizing misinformation about the knife attack. So the evening after 480 00:25:38,640 --> 00:25:41,720 Speaker 1: the attack, chaotic scenes broke out in the city after 481 00:25:41,760 --> 00:25:44,800 Speaker 1: a group of rioters attacked police vehicles and set fires. 482 00:25:45,160 --> 00:25:48,240 Speaker 1: Videos from that scene showed like stores being broken into, 483 00:25:48,359 --> 00:25:51,680 Speaker 1: police cars and buses being set on fire, people clashing 484 00:25:51,680 --> 00:25:53,960 Speaker 1: with police officers all of that. In the wake of 485 00:25:53,960 --> 00:25:58,000 Speaker 1: that attack, far right figures were using social media to 486 00:25:58,160 --> 00:26:01,679 Speaker 1: spread rumors about the nash finality of the attacker, and 487 00:26:01,760 --> 00:26:04,960 Speaker 1: one protester said that quote, Irish people are being attacked 488 00:26:04,960 --> 00:26:08,080 Speaker 1: by the scum. Drew Harris of the Dublin Police suggested 489 00:26:08,119 --> 00:26:11,200 Speaker 1: that they were being driven by misinformation online about the 490 00:26:11,280 --> 00:26:15,159 Speaker 1: knife attack being spread for what he called malevolent purposes. 491 00:26:15,640 --> 00:26:18,520 Speaker 1: Ireland's President Michaeld Higgins said that the idea that the 492 00:26:18,560 --> 00:26:20,840 Speaker 1: attack would be used or abused by groups with an 493 00:26:20,840 --> 00:26:24,800 Speaker 1: agenda attacks the principle of social inclusion and is reprehensible, 494 00:26:25,080 --> 00:26:28,000 Speaker 1: referring to these far right protesters, and that it deserves 495 00:26:28,080 --> 00:26:30,520 Speaker 1: condemnation by all who believe in the rule of law 496 00:26:30,600 --> 00:26:34,480 Speaker 1: and democracy. So that happened last week, now this week, 497 00:26:34,600 --> 00:26:37,440 Speaker 1: social media platforms were being taken to task for what happened. 498 00:26:37,960 --> 00:26:40,159 Speaker 1: The Independent, which is where I get most of my 499 00:26:40,280 --> 00:26:43,199 Speaker 1: news about what's happening in Ireland. Had a really comprehensive 500 00:26:43,240 --> 00:26:45,760 Speaker 1: breakdown of what happened, which will go on the show notes. 501 00:26:46,000 --> 00:26:50,080 Speaker 1: Irish politician Tanista Michael Martin said he was concerned about 502 00:26:50,080 --> 00:26:53,440 Speaker 1: the rapid mobilization of so many people via social media platforms. 503 00:26:53,680 --> 00:26:58,200 Speaker 1: So Facebook, TikTok, and Google, which all have Dublin headquarters, 504 00:26:58,480 --> 00:27:02,280 Speaker 1: appeared before the Test Media Company to discuss this information 505 00:27:02,520 --> 00:27:05,280 Speaker 1: media literacy and the response to the chaos in Dublin. 506 00:27:05,880 --> 00:27:10,320 Speaker 1: So guess who didn't even bother to show up to 507 00:27:10,560 --> 00:27:13,560 Speaker 1: that committee to talk about what happened? Can you guess? 508 00:27:13,920 --> 00:27:20,040 Speaker 3: Ooh, I am sneaking, sneaking, suspicion and might be. 509 00:27:21,640 --> 00:27:24,399 Speaker 2: One formerly known as Twitter. 510 00:27:25,040 --> 00:27:27,440 Speaker 1: All right, you are right. So that's what Elon Musk 511 00:27:27,480 --> 00:27:29,440 Speaker 1: has not done now, which is that he didn't even 512 00:27:29,520 --> 00:27:32,479 Speaker 1: show up to this committee and the politicians who were 513 00:27:32,560 --> 00:27:34,240 Speaker 1: running this thing were not having it. 514 00:27:34,560 --> 00:27:37,080 Speaker 3: Yeah, he doesn't have to ask a like answer to 515 00:27:37,080 --> 00:27:39,560 Speaker 3: the American government. Why would he have to answer the 516 00:27:39,640 --> 00:27:40,320 Speaker 3: Irish government? 517 00:27:40,400 --> 00:27:44,679 Speaker 1: You know? Find Gail td Kieran Cannon encouraged Twitter employees 518 00:27:44,720 --> 00:27:48,200 Speaker 1: to drop an email to the owner of Twitter, Elon Musk, 519 00:27:48,359 --> 00:27:52,160 Speaker 1: suggesting that he desist from commenting on affairs within Ireland, 520 00:27:52,320 --> 00:27:55,840 Speaker 1: which he patently knows nothing about. He also added that 521 00:27:56,359 --> 00:27:59,680 Speaker 1: Elon Musk personally served to stoke up hatred and conflict 522 00:27:59,680 --> 00:28:01,840 Speaker 1: and recent times here in Ireland, and that he should 523 00:28:01,840 --> 00:28:07,280 Speaker 1: be deeply ashamed of those actions. Let him know. Canon, Yeah, 524 00:28:07,400 --> 00:28:07,919 Speaker 1: I would not. 525 00:28:08,840 --> 00:28:13,320 Speaker 2: I would not fuk with the Irish like with this 526 00:28:13,359 --> 00:28:16,879 Speaker 2: sort of thing too, Like Elon Musk, I feel like 527 00:28:17,600 --> 00:28:20,320 Speaker 2: that would also be very poetic justice if this was 528 00:28:20,359 --> 00:28:21,920 Speaker 2: what led to the Elon Musk downfall. 529 00:28:21,960 --> 00:28:24,600 Speaker 3: And I think that would be really funny, but probably 530 00:28:24,600 --> 00:28:25,480 Speaker 3: won't unfortunately. 531 00:28:25,760 --> 00:28:27,760 Speaker 1: Yeah, you don't want this, don't You don't want that. 532 00:28:27,960 --> 00:28:30,760 Speaker 1: You don't want smoke with the Irish. I gotta tell 533 00:28:30,840 --> 00:28:33,400 Speaker 1: you right now, Elon Musk, this is not a fight 534 00:28:33,440 --> 00:28:36,520 Speaker 1: you want to pick. They've gone through so much so 535 00:28:36,640 --> 00:28:39,920 Speaker 1: the platforms who did actually show up kind of gave 536 00:28:40,000 --> 00:28:42,800 Speaker 1: the usual song and dance. I gotta say, like I 537 00:28:43,640 --> 00:28:47,160 Speaker 1: I was sort of heartened to hear what TikTok said 538 00:28:47,160 --> 00:28:49,640 Speaker 1: that they had done in light of this incident. So 539 00:28:49,760 --> 00:28:52,240 Speaker 1: Susan Moss they had a public policy for TikTok Ireland 540 00:28:52,560 --> 00:28:55,880 Speaker 1: said that TikTok had activated its crisis management protocols to 541 00:28:55,920 --> 00:28:59,160 Speaker 1: remove violating content and to prevent the spread of misinformation. 542 00:28:59,640 --> 00:29:02,680 Speaker 1: She said, we activated our emergency fact checking procedure in 543 00:29:02,720 --> 00:29:05,160 Speaker 1: collaboration with those fact checkers. We have a fact checking 544 00:29:05,200 --> 00:29:07,360 Speaker 1: organization here in Ireland. And what they did is that 545 00:29:07,360 --> 00:29:10,360 Speaker 1: they were flagging content, not this on TikTok, but horizon 546 00:29:10,400 --> 00:29:13,360 Speaker 1: scanning across the Internet and flagging to us these individual 547 00:29:13,400 --> 00:29:15,480 Speaker 1: claims that they were seeing that helped us to stop 548 00:29:15,480 --> 00:29:17,840 Speaker 1: the content spreading on TikTok. She said that there were 549 00:29:17,840 --> 00:29:21,760 Speaker 1: twenty five individual claims or type of stories that she 550 00:29:21,840 --> 00:29:24,920 Speaker 1: saw moving across the platform, and she did say that 551 00:29:24,960 --> 00:29:28,320 Speaker 1: she felt that TikTok's response had been very fast moving 552 00:29:28,360 --> 00:29:30,480 Speaker 1: and that she was very confident in it. But then 553 00:29:30,600 --> 00:29:34,560 Speaker 1: contrast that with Facebook. So Facebook said that they removed 554 00:29:34,600 --> 00:29:37,680 Speaker 1: a thousand pieces of misinformation from the platform in the 555 00:29:37,680 --> 00:29:40,280 Speaker 1: first half of this year, which I'm sorry that is 556 00:29:40,400 --> 00:29:43,040 Speaker 1: not a lot. Like a thousand pieces of misinformation is 557 00:29:43,080 --> 00:29:46,560 Speaker 1: just not a lot, and also it just doesn't work 558 00:29:46,640 --> 00:29:50,160 Speaker 1: like that, Like trust me, this was my flagging individual 559 00:29:50,240 --> 00:29:53,280 Speaker 1: pieces of misinformation for removal was my job at one 560 00:29:53,280 --> 00:29:55,160 Speaker 1: point in my life. And I can tell you that 561 00:29:55,280 --> 00:29:58,040 Speaker 1: job was the equivalent of playing whack a mole, like 562 00:29:58,160 --> 00:30:01,080 Speaker 1: even if it's removed, which is that than nothing, it 563 00:30:01,200 --> 00:30:03,680 Speaker 1: just pops back, It pops right back up. And so 564 00:30:04,200 --> 00:30:08,240 Speaker 1: Facebook saying like, oh, this piecemeal strategy that we already 565 00:30:08,280 --> 00:30:10,720 Speaker 1: know is not really that effective. That was our main 566 00:30:10,760 --> 00:30:13,560 Speaker 1: line of defense. That is shocking to me and like 567 00:30:13,920 --> 00:30:17,680 Speaker 1: almost laughable. They also said that they use debunking labels 568 00:30:17,720 --> 00:30:21,760 Speaker 1: on false contact, which we also know isn't always that effective. Like, again, 569 00:30:21,800 --> 00:30:25,000 Speaker 1: it's better than nothing, but they're like using these tactics 570 00:30:25,040 --> 00:30:27,400 Speaker 1: that we have known for a while are not necessarily 571 00:30:27,440 --> 00:30:31,320 Speaker 1: the most effective. Lawmakers also asked specifically about the issue 572 00:30:31,320 --> 00:30:33,720 Speaker 1: of a voice note that was being shared on the 573 00:30:33,840 --> 00:30:37,760 Speaker 1: Facebook owned messaging app WhatsApp calling for immigrants to be killed, 574 00:30:37,920 --> 00:30:39,760 Speaker 1: and asked if there was a failure to curb it 575 00:30:39,800 --> 00:30:43,520 Speaker 1: from spreading like wildfire. I will say that WhatsApp being 576 00:30:43,640 --> 00:30:47,320 Speaker 1: a closed platform where the messages are encrypted, means that 577 00:30:47,400 --> 00:30:52,480 Speaker 1: like tackling harmful or inaccurate information, there is like a specific, 578 00:30:52,800 --> 00:30:56,600 Speaker 1: unique challenge Like that is true, But I was really 579 00:30:56,720 --> 00:31:01,560 Speaker 1: surprised by just how not robust what Facebook was offering 580 00:31:01,760 --> 00:31:04,600 Speaker 1: was in comparison to what TikTok was saying that they 581 00:31:04,600 --> 00:31:07,080 Speaker 1: were doing, Like like they're very different platforms. But I 582 00:31:07,120 --> 00:31:09,560 Speaker 1: just can't believe that, like this is what Facebook brought 583 00:31:09,840 --> 00:31:15,240 Speaker 1: to these lawmakers in Ireland after such a such chaos 584 00:31:15,280 --> 00:31:18,840 Speaker 1: that that these folks say was was sparked by social 585 00:31:18,880 --> 00:31:19,880 Speaker 1: media platforms. 586 00:31:20,120 --> 00:31:24,400 Speaker 3: Well, you know, TikTok's too busy making everybody seventeen percent 587 00:31:24,760 --> 00:31:28,080 Speaker 3: anti Semitic for every thirty minutes you spend on TikTok. 588 00:31:29,360 --> 00:31:31,040 Speaker 3: Not to go back to that, but now I'm wondering, 589 00:31:31,120 --> 00:31:33,680 Speaker 3: is it you get progressively more anti semitic, like the 590 00:31:33,680 --> 00:31:36,680 Speaker 3: more like the more thirty minute chunks go by. Yeah, see, 591 00:31:36,720 --> 00:31:39,800 Speaker 3: their TikTok's too busy. They can't they can't be spreading 592 00:31:40,480 --> 00:31:41,920 Speaker 3: misinformation in Ireland. 593 00:31:43,200 --> 00:31:44,960 Speaker 1: So if you use an hour, if you use a 594 00:31:45,080 --> 00:31:48,280 Speaker 1: TikTok for an hour a day, are you like thirty 595 00:31:48,320 --> 00:31:51,320 Speaker 1: four percent more anti Sibmitic? How does that work? Is there? 596 00:31:51,360 --> 00:31:54,480 Speaker 3: Like I'm I want to graph, I want to chart, 597 00:31:54,680 --> 00:31:57,080 Speaker 3: I want the full again. 598 00:31:57,200 --> 00:31:59,680 Speaker 2: I took ape stats. I know how this is supposed 599 00:31:59,720 --> 00:32:00,560 Speaker 2: to work. 600 00:32:02,800 --> 00:32:05,760 Speaker 3: Yeah, no, but there definitely is something I mean like 601 00:32:06,080 --> 00:32:08,960 Speaker 3: and not not to give TikTok too much credit because 602 00:32:08,960 --> 00:32:13,920 Speaker 3: again they're a big social media company that you know 603 00:32:15,000 --> 00:32:18,360 Speaker 3: deserves much of the criticism it gets, but like it 604 00:32:18,440 --> 00:32:21,640 Speaker 3: really is says something. I feel like that TikTok is 605 00:32:21,720 --> 00:32:25,320 Speaker 3: under fire so much, like in the news cycle, and 606 00:32:25,360 --> 00:32:27,760 Speaker 3: it's like and yet they seem to be the only 607 00:32:27,800 --> 00:32:29,720 Speaker 3: platform that's like responding. 608 00:32:29,200 --> 00:32:30,160 Speaker 2: To these kind of things. 609 00:32:31,600 --> 00:32:33,880 Speaker 3: You know, I guess everybody makes fun of the fact 610 00:32:33,920 --> 00:32:35,680 Speaker 3: you can't say like there's certain words you can't say 611 00:32:35,720 --> 00:32:38,480 Speaker 3: on TikTok, Like people started saying like unlived instead of 612 00:32:38,560 --> 00:32:40,600 Speaker 3: killed and stuff like that. But like it's I don't know, 613 00:32:40,960 --> 00:32:42,360 Speaker 3: something to be said on the fact that I'm like 614 00:32:42,680 --> 00:32:44,920 Speaker 3: TikTok at least has that in place, but like no 615 00:32:45,000 --> 00:32:48,840 Speaker 3: other social media company apparently has any interest in doing 616 00:32:48,840 --> 00:32:50,360 Speaker 3: anything to moderate its content. 617 00:32:50,400 --> 00:32:53,320 Speaker 1: I guess I agree, And I think it really demonstrates 618 00:32:53,360 --> 00:32:57,280 Speaker 1: how it really isn't about one specific platform, and so 619 00:32:57,440 --> 00:33:01,240 Speaker 1: conversations like the one that happened on the GOP debate 620 00:33:01,280 --> 00:33:04,560 Speaker 1: stage this week that make it about one singular platform 621 00:33:04,680 --> 00:33:08,440 Speaker 1: or like are just deeply missing the point. Right. It 622 00:33:08,480 --> 00:33:14,120 Speaker 1: is about having a safer, robust digital landscape, and that 623 00:33:14,200 --> 00:33:17,680 Speaker 1: is that is a conversation that involves lots of platforms 624 00:33:17,760 --> 00:33:20,560 Speaker 1: and a lot more, but just making it about one 625 00:33:21,000 --> 00:33:24,200 Speaker 1: making one singular platform. The Boogeyman is just like not 626 00:33:24,280 --> 00:33:27,040 Speaker 1: how we get to a safer media landscape where the 627 00:33:27,120 --> 00:33:29,560 Speaker 1: kind of thing that happened in Dublin does not happen 628 00:33:29,600 --> 00:33:32,280 Speaker 1: anywhere else, which should be the goal exactly. 629 00:33:37,840 --> 00:33:38,000 Speaker 3: More. 630 00:33:38,000 --> 00:33:53,800 Speaker 1: After a quick break, let's get right back into it. So, 631 00:33:54,040 --> 00:33:57,240 Speaker 1: as you probably know, Time magazine released their Person of 632 00:33:57,240 --> 00:33:59,440 Speaker 1: the Year and it's Taylor Swift. I've seen a lot 633 00:33:59,440 --> 00:34:02,640 Speaker 1: of controverse about whether she deserved it or not. I 634 00:34:02,680 --> 00:34:05,280 Speaker 1: will say, people forget that it's it's I think it's 635 00:34:05,320 --> 00:34:07,440 Speaker 1: not supposed to be like the best person of the year. 636 00:34:07,800 --> 00:34:11,040 Speaker 1: It's like the most impactful or important or like meaningful 637 00:34:11,120 --> 00:34:13,560 Speaker 1: person of the year. A lot of folks have said, like, oh, 638 00:34:13,600 --> 00:34:17,160 Speaker 1: it should be like Palestinian journalists or activists. Someone else 639 00:34:17,200 --> 00:34:19,400 Speaker 1: I really respect said that it should be the concept 640 00:34:19,520 --> 00:34:23,400 Speaker 1: of like disruptors or like resistors. So people who are 641 00:34:23,760 --> 00:34:28,920 Speaker 1: you know, challenging power structures and technology, challenging state power structures, 642 00:34:29,000 --> 00:34:30,920 Speaker 1: which I thought was cool. Remember that year when it 643 00:34:30,960 --> 00:34:34,160 Speaker 1: was just like you, So it was you, Joe Way, 644 00:34:34,239 --> 00:34:36,719 Speaker 1: it was me Bridget, it was every listener listening, it 645 00:34:36,760 --> 00:34:37,600 Speaker 1: was all of us. 646 00:34:37,840 --> 00:34:39,359 Speaker 2: Was that that was like in the middle of like 647 00:34:39,520 --> 00:34:41,760 Speaker 2: lockdown right, Like, wasn't. 648 00:34:41,520 --> 00:34:46,799 Speaker 1: It was that? Oh, Mike just chimed in, did you 649 00:34:46,840 --> 00:34:47,120 Speaker 1: hear that? 650 00:34:50,200 --> 00:34:51,440 Speaker 2: It was two thousand and six? 651 00:34:51,680 --> 00:34:52,480 Speaker 1: Okay, but. 652 00:34:54,320 --> 00:34:58,920 Speaker 2: Five years old? But our six years old. I was 653 00:34:58,920 --> 00:34:59,680 Speaker 2: six years old. 654 00:35:00,560 --> 00:35:03,960 Speaker 1: Six years old, you were Time magazines personally successful? 655 00:35:04,040 --> 00:35:08,880 Speaker 3: Yeah, me and Taylor Swift. Yeah, No, there's always weird 656 00:35:09,880 --> 00:35:14,319 Speaker 3: discourse about it. Like I people love to bring up. 657 00:35:14,320 --> 00:35:16,480 Speaker 3: One of the people always bring up is that like 658 00:35:16,480 --> 00:35:19,520 Speaker 3: in the nineteen thirties they made like Hitler personally year 659 00:35:19,640 --> 00:35:21,759 Speaker 3: one time and they were like, see guys, Like people 660 00:35:21,760 --> 00:35:24,240 Speaker 3: always use that as like the see they're they're bad 661 00:35:24,280 --> 00:35:26,880 Speaker 3: and they keep picking and it's like no, like that 662 00:35:27,160 --> 00:35:29,799 Speaker 3: was the person in current events at that time that 663 00:35:29,880 --> 00:35:34,759 Speaker 3: was causing the most right weird shit going on. So like, yeah, 664 00:35:34,760 --> 00:35:36,759 Speaker 3: I guess in that sense, like it makes you know, 665 00:35:36,840 --> 00:35:39,120 Speaker 3: Taylor Swift has a lot of power, It has a 666 00:35:39,120 --> 00:35:41,640 Speaker 3: lot of cultural power, has done a lot. 667 00:35:43,400 --> 00:35:44,319 Speaker 1: But yeah, I. 668 00:35:44,320 --> 00:35:48,960 Speaker 3: Don't know, I definitely agree. I think, like I'm not 669 00:35:49,320 --> 00:35:52,440 Speaker 3: surprised that the Time is that magazine didn't do this, 670 00:35:52,560 --> 00:35:54,680 Speaker 3: but like I would have, like there definitely are like 671 00:35:54,680 --> 00:35:58,520 Speaker 3: Palestinian journalists that I think have really like changed just 672 00:35:58,560 --> 00:36:02,319 Speaker 3: so much of like how we've been talking about PLO sign. 673 00:36:02,840 --> 00:36:04,759 Speaker 2: I don't know, like it definitely, I think it would 674 00:36:04,800 --> 00:36:08,440 Speaker 2: have been much more deserved. 675 00:36:08,040 --> 00:36:10,600 Speaker 3: To maybe given to one of them. No disrespect to 676 00:36:10,640 --> 00:36:15,839 Speaker 3: Taylor Swift, but yeah, it definitely. It feels like there 677 00:36:15,880 --> 00:36:17,840 Speaker 3: have been a lot of big things that have happened 678 00:36:17,880 --> 00:36:21,200 Speaker 3: this year, maybe some other people should have been considered, 679 00:36:21,680 --> 00:36:23,040 Speaker 3: but yeah. 680 00:36:22,560 --> 00:36:25,880 Speaker 1: Yeah, that's kind of what people are saying. So because 681 00:36:25,920 --> 00:36:28,120 Speaker 1: she was named Time Magazine's Person of the Year, she 682 00:36:28,160 --> 00:36:31,120 Speaker 1: did this wide ranging interview in the magazine and pose 683 00:36:31,200 --> 00:36:32,840 Speaker 1: on a cover with her cat. So I have not 684 00:36:32,920 --> 00:36:35,360 Speaker 1: actually read this interview, but I do know that Taylor 685 00:36:35,360 --> 00:36:37,480 Speaker 1: has had, you know, a pretty big year with her 686 00:36:37,480 --> 00:36:39,960 Speaker 1: Era's tour, which I did not know. This the highest 687 00:36:39,960 --> 00:36:42,720 Speaker 1: grossing tour ever by a woman and the second highest 688 00:36:42,760 --> 00:36:47,040 Speaker 1: grossing overall tour. You know, she's got this high profile 689 00:36:47,200 --> 00:36:50,960 Speaker 1: romantic relationship with an NFL player. People obviously had a 690 00:36:51,000 --> 00:36:54,400 Speaker 1: lot of opinions about whether or not Taylor deserved or 691 00:36:54,400 --> 00:36:56,279 Speaker 1: should have been the Person of the Year, and if 692 00:36:56,280 --> 00:36:59,120 Speaker 1: folks listening have opinions, I want to hear them, please 693 00:36:59,160 --> 00:37:02,600 Speaker 1: let me know. However, it also seems like there is 694 00:37:02,640 --> 00:37:07,360 Speaker 1: a new conspiracy theory emerging from right wing pockets of 695 00:37:07,360 --> 00:37:09,680 Speaker 1: the Internet. Some of the biggest names on the far 696 00:37:09,760 --> 00:37:12,480 Speaker 1: right are suggesting that Taylor Swift is being promoted right 697 00:37:12,520 --> 00:37:16,040 Speaker 1: now as some kind of like sigh up campaign to 698 00:37:16,080 --> 00:37:20,160 Speaker 1: get people to vote for Democrats. Question Mark conspiracy theorist 699 00:37:20,239 --> 00:37:23,120 Speaker 1: Laura Lumer, who I was gonna say, like who she is? 700 00:37:23,200 --> 00:37:25,480 Speaker 1: And I was like, Oh, she's not like a very 701 00:37:25,480 --> 00:37:28,000 Speaker 1: important person. But I actually did not know this that 702 00:37:28,200 --> 00:37:31,799 Speaker 1: just last year, Laura Lumer narrowly lost a bid to 703 00:37:31,840 --> 00:37:36,720 Speaker 1: represent Florida's eleventh congressional district, While she tweeted, Taylor Swift 704 00:37:36,760 --> 00:37:39,560 Speaker 1: has been named Times twenty twenty three Person of the Year. 705 00:37:39,880 --> 00:37:42,560 Speaker 1: This isn't shocking since she's who the Democrats are counting 706 00:37:42,560 --> 00:37:45,120 Speaker 1: on to interfere in the twenty twenty four election. What 707 00:37:45,120 --> 00:37:47,799 Speaker 1: would Democrats do without their idol who runs through men 708 00:37:47,960 --> 00:37:51,279 Speaker 1: like water and spew's anti Trump drivel every chance she 709 00:37:51,360 --> 00:37:54,839 Speaker 1: gets her entire world tourists become a Democrat Party voter 710 00:37:55,160 --> 00:37:59,200 Speaker 1: registration drive. You might be asking why would Taylor Swift 711 00:37:59,400 --> 00:38:04,440 Speaker 1: architect global tour across Latin America, Australia, Asia, and Europe, 712 00:38:04,920 --> 00:38:09,000 Speaker 1: specifically to register voters in the United States presidential election. 713 00:38:09,280 --> 00:38:11,279 Speaker 1: Unclear how that would work, but that is what Laura 714 00:38:11,320 --> 00:38:12,359 Speaker 1: Lumer says is going on. 715 00:38:12,920 --> 00:38:18,400 Speaker 3: I think it is really funny that everybody, like, listen, 716 00:38:18,600 --> 00:38:20,640 Speaker 3: I'm a Taylor Swift fan. I will say, like I 717 00:38:20,680 --> 00:38:22,840 Speaker 3: love her music. I did go to the Airs tour. 718 00:38:24,239 --> 00:38:27,760 Speaker 3: I've been following her for a while. Like people really 719 00:38:28,400 --> 00:38:31,359 Speaker 3: from all sides like try to attach some sort of 720 00:38:31,400 --> 00:38:38,120 Speaker 3: like political booky man thing to her. I think, which 721 00:38:38,160 --> 00:38:42,520 Speaker 3: is weird because she's like when I say a political 722 00:38:42,600 --> 00:38:44,600 Speaker 3: not in the like, oh I don't want to like 723 00:38:44,600 --> 00:38:47,000 Speaker 3: like oh, she just like doesn't want to talk about 724 00:38:47,040 --> 00:38:49,839 Speaker 3: Like she just doesn't. I don't think I've ever seen 725 00:38:49,920 --> 00:38:54,319 Speaker 3: her say anything that's like remotely like political, like the 726 00:38:54,560 --> 00:38:57,640 Speaker 3: I don't know the most She's spoken up a little 727 00:38:57,640 --> 00:38:59,640 Speaker 3: bit about like feminism and stuff like that, but it's 728 00:38:59,640 --> 00:39:01,920 Speaker 3: never like super in depth, like I've never seen her 729 00:39:02,040 --> 00:39:04,880 Speaker 3: like comment on like a specific sort of issue that's happening, 730 00:39:04,880 --> 00:39:07,560 Speaker 3: which I think is interesting. I think it like mostly 731 00:39:07,640 --> 00:39:10,120 Speaker 3: is just the fact that she has such a big platform, 732 00:39:10,160 --> 00:39:10,680 Speaker 3: and she has. 733 00:39:10,520 --> 00:39:13,960 Speaker 2: Such a big platform that's like mostly young women. 734 00:39:15,600 --> 00:39:17,759 Speaker 5: But then yeah, the other side is crazy because if 735 00:39:17,800 --> 00:39:21,080 Speaker 5: she wanted, she has so much power, like she could 736 00:39:21,239 --> 00:39:24,040 Speaker 5: I get like literally I remember seeing something about it 737 00:39:24,080 --> 00:39:25,960 Speaker 5: was after the whole like Ticketmaster thing. 738 00:39:26,800 --> 00:39:29,359 Speaker 3: Uh, like somebody was like somebody like needs to get 739 00:39:29,400 --> 00:39:32,040 Speaker 3: her to just like say something off handedly about like 740 00:39:32,080 --> 00:39:35,399 Speaker 3: student loans or something so that people like start pit 741 00:39:35,560 --> 00:39:37,600 Speaker 3: or like that there needs to like like whatever they're 742 00:39:37,600 --> 00:39:41,279 Speaker 3: gains the sort of political momentum from the swifties that 743 00:39:41,320 --> 00:39:44,200 Speaker 3: we need to But yeah, no exactly. That is kind 744 00:39:44,200 --> 00:39:46,919 Speaker 3: of crazy that she would have this whole global tour 745 00:39:47,000 --> 00:39:48,759 Speaker 3: when like, all, yeah, again, all she would need to 746 00:39:48,800 --> 00:39:51,040 Speaker 3: do is make an Instagram post being like I think 747 00:39:51,040 --> 00:39:53,640 Speaker 3: you should vote for this person and people. I mean 748 00:39:53,719 --> 00:39:55,520 Speaker 3: I think she could do it, I think if she 749 00:39:55,640 --> 00:39:58,360 Speaker 3: wanted to, but like she's not going to and she's never. 750 00:40:00,640 --> 00:40:03,920 Speaker 1: Yeah, and like she she did post on Instagram for 751 00:40:04,040 --> 00:40:07,640 Speaker 1: National Voter Registration Day, and like that single Instagram post 752 00:40:07,640 --> 00:40:10,280 Speaker 1: did drive like I think it was thirty five thousand 753 00:40:10,560 --> 00:40:15,120 Speaker 1: new people to register to vote. But registering voters is 754 00:40:15,160 --> 00:40:17,560 Speaker 1: not the same thing as like. 755 00:40:17,920 --> 00:40:21,120 Speaker 2: Taking a stand, like making a stand or. 756 00:40:21,080 --> 00:40:23,480 Speaker 1: Like it's not like she said register and vote for 757 00:40:23,560 --> 00:40:26,120 Speaker 1: Biden or like vote for a specific person, So it's 758 00:40:26,160 --> 00:40:31,480 Speaker 1: like just just driving registrations is nonpartisan or it certainly 759 00:40:31,560 --> 00:40:33,799 Speaker 1: should be nonpartisan, right, And so like this idea that 760 00:40:33,880 --> 00:40:37,440 Speaker 1: like she's a shill for the Democratic Party is so 761 00:40:37,560 --> 00:40:40,960 Speaker 1: curious to me. It makes me think, like, if you 762 00:40:41,000 --> 00:40:44,480 Speaker 1: feel that Taylor Swift driving people to register to vote 763 00:40:44,520 --> 00:40:47,319 Speaker 1: in general is automatically her being in the can for 764 00:40:47,360 --> 00:40:49,840 Speaker 1: the Democratic Party, it really tells me a lot about 765 00:40:49,840 --> 00:40:52,640 Speaker 1: what you think that, you know, people who are not 766 00:40:52,680 --> 00:40:56,000 Speaker 1: Democrats have to offer young women voters, which is maybe 767 00:40:56,040 --> 00:40:57,000 Speaker 1: not maybe not a lot. 768 00:40:57,200 --> 00:40:57,279 Speaker 2: So. 769 00:40:57,520 --> 00:41:01,440 Speaker 1: Former Trump White House advisor Stephen Miller tweeted, what's happening 770 00:41:01,480 --> 00:41:04,920 Speaker 1: with Taylor Swift is not organic, which is like the 771 00:41:04,960 --> 00:41:07,440 Speaker 1: weirdest sentence I've ever seen someone tweet. 772 00:41:07,600 --> 00:41:10,319 Speaker 6: Like, Yeah, first of all, weird thing to say to 773 00:41:10,360 --> 00:41:14,600 Speaker 6: begin with, weird, real weird. Also, it's like Taylor Swift 774 00:41:14,600 --> 00:41:18,320 Speaker 6: has been a huge pop star for a while and like, yeah. 775 00:41:18,160 --> 00:41:22,200 Speaker 2: It's that is so I thought, it is so weird. 776 00:41:22,880 --> 00:41:26,279 Speaker 3: That is the Yeah, the kids these days, it's their 777 00:41:26,320 --> 00:41:28,480 Speaker 3: fault rock and roll and Taylor Swift. 778 00:41:28,680 --> 00:41:34,439 Speaker 1: That's exactly it. Jack Prosbyak tweeted, the Taylor Swift girl 779 00:41:34,480 --> 00:41:38,640 Speaker 1: Boss Sya has been fully activated from her hand selected vaccine. 780 00:41:38,719 --> 00:41:41,719 Speaker 1: She'll boyfriend to her dink lifestyle. If you don't know 781 00:41:41,760 --> 00:41:45,880 Speaker 1: what dink is, it's dual income, no kids to her 782 00:41:46,000 --> 00:41:49,960 Speaker 1: upcoming twenty twenty four voter operation for Democrats on abortion rights, 783 00:41:50,000 --> 00:41:53,040 Speaker 1: it's all coming. I do find it interesting that they're 784 00:41:53,080 --> 00:41:58,080 Speaker 1: calling her out for being like having a dink lifestyle, 785 00:41:58,520 --> 00:42:01,000 Speaker 1: as if just like not having children is some sort 786 00:42:01,000 --> 00:42:03,640 Speaker 1: of like morally deviant way to live. 787 00:42:04,080 --> 00:42:08,480 Speaker 3: Yeah, that's so weird, Like she's not, she's she's also 788 00:42:08,560 --> 00:42:12,160 Speaker 3: like what like thirty three, thirty four, Like she's not. 789 00:42:14,320 --> 00:42:14,719 Speaker 2: I don't know. 790 00:42:14,760 --> 00:42:17,080 Speaker 3: I guess that's like thirty three, yeah, which I guess. Yeah, 791 00:42:17,120 --> 00:42:19,640 Speaker 3: I guess we're like that's a normal age to whatever. 792 00:42:19,800 --> 00:42:22,040 Speaker 3: I'll like get married and have kids. But like, I 793 00:42:22,040 --> 00:42:23,640 Speaker 3: don't know, I know plenty of people that are like 794 00:42:23,680 --> 00:42:24,439 Speaker 3: in that age range. 795 00:42:24,440 --> 00:42:24,960 Speaker 1: You aren't. 796 00:42:25,239 --> 00:42:28,600 Speaker 3: And that's such a weird, like dank, I've never heard 797 00:42:28,600 --> 00:42:31,320 Speaker 3: that phrase before, but that is that's weird. 798 00:42:31,640 --> 00:42:33,840 Speaker 2: Her vaccine chill boyfriend too. 799 00:42:34,840 --> 00:42:37,239 Speaker 3: Honestly, I think Travis Kelsey, you know, this is my 800 00:42:37,360 --> 00:42:39,840 Speaker 3: controversial opinion, Travis Kelsey should have been Time Person in 801 00:42:39,920 --> 00:42:44,520 Speaker 3: the year. His tweets so funny, Like his tweets have 802 00:42:44,640 --> 00:42:50,640 Speaker 3: been like the light in my news feeds lately, So. 803 00:42:50,719 --> 00:42:52,279 Speaker 1: Joey coming in with the hata. 804 00:42:53,960 --> 00:42:56,960 Speaker 3: Not seeing chill boyfriend. That's so insane, though they really 805 00:42:57,040 --> 00:42:59,160 Speaker 3: like it is. Again, this is why I like, I 806 00:42:59,239 --> 00:43:01,160 Speaker 3: think it is so weird that they found her to 807 00:43:01,200 --> 00:43:03,480 Speaker 3: be a target of all of this, because again, she 808 00:43:03,640 --> 00:43:09,560 Speaker 3: is like the most mild like I'm like, doesn't really 809 00:43:09,719 --> 00:43:12,080 Speaker 3: has not said anything particularly controversial. 810 00:43:13,360 --> 00:43:14,960 Speaker 2: That is so weird. 811 00:43:15,880 --> 00:43:18,919 Speaker 1: Yeah, and I talked about this in an episode years ago, 812 00:43:18,960 --> 00:43:23,240 Speaker 1: but only recently has she been somebody who publicly identifies 813 00:43:23,239 --> 00:43:25,560 Speaker 1: as a feminist, Like she was on record in her 814 00:43:25,640 --> 00:43:27,320 Speaker 1: the early days of her fame being like, I'm not 815 00:43:27,440 --> 00:43:30,960 Speaker 1: a feminist, I don't believe in men versus women, YadA, YadA, YadA, 816 00:43:31,000 --> 00:43:33,160 Speaker 1: And it wasn't until kind of later in the game 817 00:43:33,160 --> 00:43:34,960 Speaker 1: that she was like, oh no, I'm a totally a feminist. 818 00:43:35,280 --> 00:43:38,880 Speaker 1: And so it's curious to me that these people are 819 00:43:39,120 --> 00:43:42,560 Speaker 1: are criticizing for her for it. And I also think, 820 00:43:42,600 --> 00:43:45,680 Speaker 1: like the whole thing is really like gendered and racialized, 821 00:43:45,800 --> 00:43:47,759 Speaker 1: Like I think I don't think any of these people 822 00:43:47,760 --> 00:43:50,880 Speaker 1: would ever say that part out loud, but like they're 823 00:43:51,239 --> 00:43:56,000 Speaker 1: giving her crap for not having children, being somebody who 824 00:43:56,080 --> 00:43:59,560 Speaker 1: is in a relationship with another person who earns income 825 00:43:59,600 --> 00:44:02,000 Speaker 1: outs how the home and not having kids a dink. 826 00:44:02,600 --> 00:44:05,799 Speaker 1: And you know, certainly they would not be clamoring for me, 827 00:44:06,040 --> 00:44:08,239 Speaker 1: a black woman, to have kids, or I'd be a 828 00:44:08,280 --> 00:44:10,520 Speaker 1: welfare queen, and they wouldn't be clamoring for people who 829 00:44:10,520 --> 00:44:12,680 Speaker 1: aren't Sis women to have kids because they might be 830 00:44:12,760 --> 00:44:15,239 Speaker 1: like threats to children, right, and so like, it's very 831 00:44:15,239 --> 00:44:20,920 Speaker 1: interesting who gets kind of smeared as doing something wrong 832 00:44:21,600 --> 00:44:24,680 Speaker 1: just by by virtue of them not having a child. 833 00:44:24,680 --> 00:44:26,279 Speaker 1: And I guess I feel like you can't really talk 834 00:44:26,280 --> 00:44:28,600 Speaker 1: about the kind of hate that she is receiving from 835 00:44:28,640 --> 00:44:32,319 Speaker 1: these people without talking about like race and gender and sexuality. 836 00:44:32,640 --> 00:44:34,800 Speaker 1: I did see this one kind of like men's rights 837 00:44:34,840 --> 00:44:38,640 Speaker 1: influence her tweet quote it's shameful and said that a 838 00:44:38,760 --> 00:44:42,680 Speaker 1: hyper promiscuous, childless woman, aging and alone with a cat 839 00:44:42,760 --> 00:44:45,439 Speaker 1: has become the heroine of a feminist age. And part 840 00:44:45,440 --> 00:44:47,560 Speaker 1: of me is like, side note, this person runs an 841 00:44:47,600 --> 00:44:52,239 Speaker 1: organization for what they're calling hard men, like masculine manly men. 842 00:44:52,360 --> 00:44:56,920 Speaker 1: I mean, because what is harder for a man to 843 00:44:56,960 --> 00:44:59,200 Speaker 1: do that obsess over the dating life of a pop 844 00:44:59,200 --> 00:45:01,640 Speaker 1: star on Twitter? Right, Like it's like they can't even 845 00:45:01,760 --> 00:45:07,000 Speaker 1: keep their own ridiculous gender binary nonsense straight, Like if 846 00:45:07,040 --> 00:45:10,319 Speaker 1: you were like I don't, I don't think like that 847 00:45:10,440 --> 00:45:13,480 Speaker 1: is a paragon of traditional masculinity, caring who tailor us 848 00:45:13,520 --> 00:45:15,600 Speaker 1: with this dating here you are making a big stink 849 00:45:15,640 --> 00:45:16,560 Speaker 1: about it on Twitter, you know. 850 00:45:16,719 --> 00:45:20,040 Speaker 2: I also, first of all, hard hard men. 851 00:45:20,280 --> 00:45:27,359 Speaker 3: If you're gonna call yourself hard men, the peak of heterosexuality, okay, 852 00:45:29,120 --> 00:45:30,120 Speaker 3: also okay. 853 00:45:30,000 --> 00:45:32,880 Speaker 2: Hyper promiscuous, Like I'm sorry. 854 00:45:32,960 --> 00:45:36,640 Speaker 3: I remember like when she brought up sex for like 855 00:45:36,680 --> 00:45:38,560 Speaker 3: the first time in one of her albums, and it 856 00:45:38,640 --> 00:45:40,640 Speaker 3: was like I think it was reputation, Like it was 857 00:45:40,719 --> 00:45:44,120 Speaker 3: like way down the line, like she was like again 858 00:45:44,360 --> 00:45:49,080 Speaker 3: very much the face of like respectable, nice white girl 859 00:45:49,520 --> 00:45:51,880 Speaker 3: with her guitar, you know, for the longest time. And 860 00:45:51,920 --> 00:45:55,560 Speaker 3: it's it's weird because again she really with the feminism thing, 861 00:45:55,600 --> 00:45:59,920 Speaker 3: she's done like the bare minimum to you know, stand 862 00:46:00,160 --> 00:46:02,480 Speaker 3: up for human rights or whatever, and that apparently is 863 00:46:02,520 --> 00:46:08,200 Speaker 3: too much. But yeah, no, I just that's just it's 864 00:46:08,239 --> 00:46:09,400 Speaker 3: just such a weird quote. 865 00:46:09,480 --> 00:46:12,840 Speaker 2: I it's such a weird quote, but like not smarts. 866 00:46:12,960 --> 00:46:15,239 Speaker 3: I also like remember it wasn't there because I was 867 00:46:15,239 --> 00:46:17,200 Speaker 3: going back to about like I feel like people turn 868 00:46:17,280 --> 00:46:18,960 Speaker 3: her in her image into like a lot like the 869 00:46:18,960 --> 00:46:20,799 Speaker 3: symbol of a lot of political stuff without it really 870 00:46:21,280 --> 00:46:22,040 Speaker 3: really knew anything. 871 00:46:22,320 --> 00:46:23,840 Speaker 2: And I don't think this one, I don't think was 872 00:46:23,880 --> 00:46:24,480 Speaker 2: necessarily wrong. 873 00:46:24,520 --> 00:46:26,000 Speaker 3: But I remember, like a couple of years ago, there 874 00:46:26,000 --> 00:46:27,239 Speaker 3: was a lot of stuff that was coming out about 875 00:46:27,239 --> 00:46:30,120 Speaker 3: people talking about her music and her short of persona 876 00:46:30,160 --> 00:46:32,719 Speaker 3: that she's created and how that relates to like white supremacy, 877 00:46:32,960 --> 00:46:36,440 Speaker 3: and how like she kind of inadvertently has become this 878 00:46:36,560 --> 00:46:41,520 Speaker 3: symbol of white supremacy, and like there I again, it's 879 00:46:41,520 --> 00:46:44,000 Speaker 3: been a minute since I but I like, it's really 880 00:46:44,040 --> 00:46:46,719 Speaker 3: interesting to see that that switch within a couple of 881 00:46:46,840 --> 00:46:49,520 Speaker 3: years because she's dating somebody who's vaccinated. 882 00:46:49,560 --> 00:46:50,239 Speaker 2: I guess, I don't know. 883 00:46:50,480 --> 00:46:53,839 Speaker 1: Yeah, I remember this very clearly. There was a time 884 00:46:53,880 --> 00:46:59,400 Speaker 1: where in white supremacist corners of the internet she was 885 00:46:59,440 --> 00:47:04,560 Speaker 1: sort of the poster girl for like Nazism essentially, and 886 00:47:05,320 --> 00:47:08,760 Speaker 1: at a time this was like going back like several 887 00:47:08,840 --> 00:47:13,200 Speaker 1: years now, at the time she I remember doing an 888 00:47:13,200 --> 00:47:15,680 Speaker 1: episode of stuff Mom never told you talk about, like 889 00:47:15,760 --> 00:47:17,719 Speaker 1: Blast in the Past, back when I was the host 890 00:47:17,760 --> 00:47:19,560 Speaker 1: of the cost of that show with my friend Emily, 891 00:47:20,000 --> 00:47:23,400 Speaker 1: and at the time I did not you could probably 892 00:47:23,440 --> 00:47:25,520 Speaker 1: go back to the old Seminty archives and find it. 893 00:47:25,560 --> 00:47:27,319 Speaker 1: I did not feel that she had done enough to 894 00:47:27,400 --> 00:47:31,960 Speaker 1: like denounce that and be like, no, sorry, Nazis, I'm 895 00:47:31,960 --> 00:47:34,280 Speaker 1: not your poster girl. But I do think it speaks 896 00:47:34,280 --> 00:47:36,560 Speaker 1: to what you're talking about that she is someone that 897 00:47:36,600 --> 00:47:39,640 Speaker 1: I think people project a lot of stuff onto and around, 898 00:47:39,960 --> 00:47:42,560 Speaker 1: and I think that all of this, like all of 899 00:47:42,600 --> 00:47:45,240 Speaker 1: these weird tweets about her being on the cover of Time, 900 00:47:45,560 --> 00:47:48,919 Speaker 1: I really think is an anxiety about the rise of 901 00:47:49,400 --> 00:47:52,960 Speaker 1: single women voters, because like a lot of her listenership 902 00:47:53,000 --> 00:47:55,920 Speaker 1: and fan base is younger women, and I think that 903 00:47:56,000 --> 00:48:01,640 Speaker 1: it's about anxiety around how government has failed single women 904 00:48:01,719 --> 00:48:04,560 Speaker 1: voters and what that means. For the twenty twenty four election, 905 00:48:05,239 --> 00:48:08,440 Speaker 1: so we know that a lot of white women voted 906 00:48:08,440 --> 00:48:10,719 Speaker 1: for Trump, but it's a little more complicated than that, 907 00:48:10,760 --> 00:48:14,840 Speaker 1: because it's really married white women. Specifically, married white women 908 00:48:14,960 --> 00:48:18,840 Speaker 1: are more likely to vote conservative than their single white counterparts. 909 00:48:18,960 --> 00:48:21,200 Speaker 1: So I think after the last few years, with like 910 00:48:21,320 --> 00:48:23,879 Speaker 1: big Supreme Court decisions on things like abortion, I think 911 00:48:23,880 --> 00:48:27,760 Speaker 1: that folks are rightfully starting to worry about single women 912 00:48:28,200 --> 00:48:30,799 Speaker 1: as a voting block and what this means. But here's 913 00:48:30,840 --> 00:48:32,759 Speaker 1: my thing, It really kind of goes back to the 914 00:48:32,800 --> 00:48:35,120 Speaker 1: episode that we did with Julia Maser, who was like 915 00:48:35,600 --> 00:48:39,480 Speaker 1: shamed by Tucker Carlson for just existing on the internet 916 00:48:39,560 --> 00:48:41,880 Speaker 1: as a woman who is not married. I think that 917 00:48:42,040 --> 00:48:46,280 Speaker 1: all that these people have to offer younger single women 918 00:48:46,719 --> 00:48:49,600 Speaker 1: is shame and making fun of us and like the 919 00:48:49,640 --> 00:48:53,239 Speaker 1: most tired jokes about cats and being alone and this 920 00:48:53,360 --> 00:48:57,200 Speaker 1: and that, and like trying to scare us into marriage 921 00:48:57,280 --> 00:49:00,040 Speaker 1: or invalidating our choices, like not everybody wants to be 922 00:49:00,080 --> 00:49:02,560 Speaker 1: married or partnered, and there are so many ways to 923 00:49:02,560 --> 00:49:05,040 Speaker 1: have a full life that doesn't include being married. And 924 00:49:05,080 --> 00:49:07,880 Speaker 1: I just think that they don't have anything to really 925 00:49:07,960 --> 00:49:11,319 Speaker 1: offer younger single women, So the best they can do 926 00:49:11,560 --> 00:49:14,719 Speaker 1: is try to scare them into heterosexual marriages and then 927 00:49:14,960 --> 00:49:19,200 Speaker 1: hope that that proximity to men will will make them 928 00:49:19,280 --> 00:49:21,799 Speaker 1: vote more conservatively. Like I don't they have anything real 929 00:49:21,880 --> 00:49:24,000 Speaker 1: to offer. It's just like all they can do is 930 00:49:24,040 --> 00:49:27,680 Speaker 1: shame you and hopefully effectively scare you into marriage, which 931 00:49:27,719 --> 00:49:30,239 Speaker 1: hopefully will make you vote more conservatively. And I think 932 00:49:30,280 --> 00:49:35,759 Speaker 1: Taylor Swift, particularly how popular she's been this year, I 933 00:49:35,800 --> 00:49:40,160 Speaker 1: think reiterates that anxiety that like, oh no, we really 934 00:49:40,160 --> 00:49:44,000 Speaker 1: don't have anything to offer a pretty big section of 935 00:49:44,440 --> 00:49:47,160 Speaker 1: the voting public. This might come back to bite us 936 00:49:47,160 --> 00:49:47,880 Speaker 1: in the ass. 937 00:49:48,239 --> 00:49:50,799 Speaker 2: Yeah, which is so funny because it's like. 938 00:49:52,760 --> 00:49:56,560 Speaker 3: You just sat like, clearly you don't understand how Swifties 939 00:49:56,640 --> 00:50:00,600 Speaker 3: work because you didn't even again she gave you she 940 00:50:00,680 --> 00:50:03,400 Speaker 3: were in the clear. She wasn't telling anybody to go 941 00:50:03,520 --> 00:50:06,880 Speaker 3: vote for Biden or do anything. Like she was doing 942 00:50:06,920 --> 00:50:09,000 Speaker 3: the bare minimum telling people to go vote. Maybe not 943 00:50:09,000 --> 00:50:10,160 Speaker 3: even doing that again this year. 944 00:50:10,200 --> 00:50:10,799 Speaker 2: I think she like. 945 00:50:10,760 --> 00:50:14,040 Speaker 3: Literally did it one time, but like she would have 946 00:50:14,040 --> 00:50:16,280 Speaker 3: been fine, but it's like, no, now you've attacked Taylor 947 00:50:16,320 --> 00:50:18,600 Speaker 3: Swift the Swifties like they don't care who you are. 948 00:50:19,080 --> 00:50:22,520 Speaker 2: They're gonna learn who you are now, and like they're 949 00:50:22,520 --> 00:50:23,520 Speaker 2: gonna start targeting you. 950 00:50:23,719 --> 00:50:26,840 Speaker 3: And the Swifties are fucking terrifying, So I would not, 951 00:50:27,520 --> 00:50:30,800 Speaker 3: like I I do think it would be really funny 952 00:50:30,920 --> 00:50:34,120 Speaker 3: if like this was what led to like the downfall 953 00:50:34,160 --> 00:50:36,839 Speaker 3: of like some one of at least one of these people, 954 00:50:36,880 --> 00:50:39,840 Speaker 3: If if the Swifties could do that, I think that 955 00:50:39,840 --> 00:50:43,200 Speaker 3: would be really beautiful and really uh poetic justice. 956 00:50:43,360 --> 00:50:47,840 Speaker 2: But yeah, no, that's that's so weird. Why are you 957 00:50:47,840 --> 00:50:50,440 Speaker 2: gotta go after the cats like she has? I know, 958 00:50:50,600 --> 00:50:52,280 Speaker 2: I have. I love cats. 959 00:50:52,480 --> 00:50:59,000 Speaker 1: They're great great. Who doesn't enjoy cat exactly? Joey, thank 960 00:50:59,040 --> 00:51:02,440 Speaker 1: you so much for wading through all of this ridiculous 961 00:51:02,520 --> 00:51:04,680 Speaker 1: tech news coorse. You know, at the top of the episode, 962 00:51:04,680 --> 00:51:06,560 Speaker 1: we got to know you a little bit better. What 963 00:51:06,680 --> 00:51:08,439 Speaker 1: else are you working on? What have you got going 964 00:51:08,480 --> 00:51:09,400 Speaker 1: on these days? 965 00:51:09,520 --> 00:51:10,880 Speaker 2: I'm so glad you asked. 966 00:51:11,280 --> 00:51:16,720 Speaker 3: I recently have a show that is in the middle 967 00:51:16,920 --> 00:51:21,440 Speaker 3: of coming out. We just had episode four released. It's 968 00:51:21,480 --> 00:51:26,480 Speaker 3: called Afterlives, the Lelien Planco Story. I worked with Raquel Willis, 969 00:51:26,560 --> 00:51:29,719 Speaker 3: who I think will be on the show soon. Yes, 970 00:51:29,880 --> 00:51:35,200 Speaker 3: little teaser there, but it was based on her incredible 971 00:51:35,239 --> 00:51:40,960 Speaker 3: reporting on transphobic violence and the epidemic of violence against 972 00:51:40,960 --> 00:51:42,600 Speaker 3: transwoman to color in particular. 973 00:51:42,920 --> 00:51:46,280 Speaker 2: In this season, we are talking about Lilien Planco. 974 00:51:45,960 --> 00:51:49,760 Speaker 3: Who was a woman who died at Rikers Island Prison 975 00:51:50,400 --> 00:51:52,720 Speaker 3: a couple of years back. But yeah, you should definitely 976 00:51:53,160 --> 00:51:57,600 Speaker 3: check out that show. I think it's a super important conversation. 977 00:51:57,239 --> 00:51:57,880 Speaker 2: To be having. 978 00:51:58,320 --> 00:52:01,040 Speaker 3: I'm biased, I worked on it, but also, yes, you 979 00:52:01,080 --> 00:52:02,239 Speaker 3: should definitely check that out. 980 00:52:03,640 --> 00:52:05,520 Speaker 2: Episode four you just came out. Episode five is coming 981 00:52:05,560 --> 00:52:06,160 Speaker 2: out next week. 982 00:52:06,200 --> 00:52:09,680 Speaker 3: Again, it's called Afterlives The Lelien Planco Story And of course, 983 00:52:09,680 --> 00:52:12,319 Speaker 3: as always, if you want to follow me on social media, you. 984 00:52:12,280 --> 00:52:14,560 Speaker 2: Can find me at pat not prat. That's p a 985 00:52:14,680 --> 00:52:16,440 Speaker 2: t t n O T p r. 986 00:52:16,480 --> 00:52:21,600 Speaker 3: A t T Twitter, Instagram. But yeah, you should definitely 987 00:52:21,680 --> 00:52:25,040 Speaker 3: check out the show, find me on social media. 988 00:52:24,440 --> 00:52:27,040 Speaker 1: And we'll throw a link to Afterlives in the show notes. 989 00:52:27,360 --> 00:52:30,640 Speaker 1: Folks should definitely listen it's it is such an important 990 00:52:30,640 --> 00:52:34,200 Speaker 1: conversation and thank you for bringing bringing it to us 991 00:52:34,239 --> 00:52:38,040 Speaker 1: because it's really special. And I actually have a little 992 00:52:38,040 --> 00:52:40,840 Speaker 1: bit of news, which is that the other podcast that 993 00:52:41,000 --> 00:52:44,160 Speaker 1: I host IRL Online Life is Real Life, which is 994 00:52:44,160 --> 00:52:47,440 Speaker 1: an original podcast from Mozilla, yep, the Mozilla that makes 995 00:52:47,719 --> 00:52:51,640 Speaker 1: the browser that we all love, Firefox. Well, we have 996 00:52:51,760 --> 00:52:55,879 Speaker 1: been nominated for an Anthem Award for Responsible Technology and 997 00:52:56,080 --> 00:52:58,879 Speaker 1: it would mean so much if folks could vote for us. 998 00:52:59,000 --> 00:53:01,680 Speaker 1: If you could help celeb this nomination by voting for 999 00:53:01,719 --> 00:53:04,680 Speaker 1: IRL to win, you can go to tangody dot com 1000 00:53:04,719 --> 00:53:07,520 Speaker 1: slash I r L. That's t A N G O 1001 00:53:07,680 --> 00:53:10,399 Speaker 1: t I dot com slash I r L. That link 1002 00:53:10,400 --> 00:53:12,600 Speaker 1: will be in the show notes. Please vote for us. 1003 00:53:13,719 --> 00:53:15,839 Speaker 1: I don't want to get into it, but it would 1004 00:53:15,840 --> 00:53:18,239 Speaker 1: be really cool to win and then maybe Mozilla will 1005 00:53:18,239 --> 00:53:20,200 Speaker 1: have me back to host it again. Who knows would 1006 00:53:20,200 --> 00:53:23,239 Speaker 1: love that? So please please please vote for us. It 1007 00:53:23,320 --> 00:53:25,400 Speaker 1: means the world to me to spotlight the voices and 1008 00:53:25,440 --> 00:53:27,920 Speaker 1: the stories of folks who are making technology like AI 1009 00:53:28,400 --> 00:53:31,320 Speaker 1: more trustworthy, and it's really also important to me to 1010 00:53:31,360 --> 00:53:33,920 Speaker 1: see those voices celebrated through winning an award like this, 1011 00:53:34,040 --> 00:53:38,480 Speaker 1: So please vote for us again. Tangody dot com, slash 1012 00:53:38,680 --> 00:53:41,600 Speaker 1: I r L. Thank you so much, and thank you 1013 00:53:41,719 --> 00:53:45,600 Speaker 1: Joy for being here. Thank you listeners for listening. Uh yeah, 1014 00:53:45,880 --> 00:53:53,400 Speaker 1: be well, and I will talk to you soon. If 1015 00:53:53,440 --> 00:53:55,560 Speaker 1: you're looking for ways to support the show, check out 1016 00:53:55,560 --> 00:53:59,399 Speaker 1: our mark store at tangoty dot com slash store Got 1017 00:53:59,400 --> 00:54:01,400 Speaker 1: a story about it, interesting thing in tech, or just 1018 00:54:01,440 --> 00:54:03,439 Speaker 1: want to say hi? You can reach us at Hello 1019 00:54:03,440 --> 00:54:05,960 Speaker 1: at tegody dot com. You can also find transcripts for 1020 00:54:06,000 --> 00:54:08,680 Speaker 1: today's episode at tengody dot com. There Are No Girls 1021 00:54:08,719 --> 00:54:11,080 Speaker 1: on the Internet was created by me Bridget Tod. It's 1022 00:54:11,080 --> 00:54:14,440 Speaker 1: a production of iHeartRadio and Unbossed Creative edited by Joey 1023 00:54:14,440 --> 00:54:18,319 Speaker 1: Pat Jonathan Strickland as our executive producer. Tari Harrison is 1024 00:54:18,320 --> 00:54:21,920 Speaker 1: our producer and sound engineer. Michael Almado is our contributing producer. 1025 00:54:22,200 --> 00:54:24,359 Speaker 1: I'm your host, bridget Toad. If you want to help 1026 00:54:24,400 --> 00:54:27,400 Speaker 1: us grow, rate and review us on Apple podcasts. For 1027 00:54:27,480 --> 00:54:30,440 Speaker 1: more podcasts from iHeartRadio, check out the iHeartRadio app, Apple 1028 00:54:30,480 --> 00:54:32,520 Speaker 1: podcast or wherever you get your podcasts.