1
00:00:04,400 --> 00:00:06,120
Speaker 1: There Are No Girls on the Internet, as a production

2
00:00:06,160 --> 00:00:13,680
Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Tot and this

3
00:00:13,760 --> 00:00:18,040
Speaker 1: is there Are No Girls on the Internet. This is

4
00:00:18,079 --> 00:00:20,040
Speaker 1: There No Girls on the Internet, where we explore the

5
00:00:20,040 --> 00:00:24,080
Speaker 1: intersection of technology, social media, and identity. Joey, we are

6
00:00:24,160 --> 00:00:26,040
Speaker 1: so thrilled to have you back on the show.

7
00:00:26,480 --> 00:00:30,600
Speaker 2: Hey, Bridget, it's going to be back. Happy Pride months.

8
00:00:30,720 --> 00:00:33,080
Speaker 1: Happy Pride. Are you doing anything fun for Pride?

9
00:00:34,840 --> 00:00:35,000
Speaker 3: Oh?

10
00:00:35,080 --> 00:00:36,640
Speaker 4: God? What am I not doing?

11
00:00:36,960 --> 00:00:37,200
Speaker 5: Yeah?

12
00:00:37,240 --> 00:00:40,560
Speaker 2: I have a couple events that like friends are doing.

13
00:00:40,760 --> 00:00:45,239
Speaker 2: I had my household through something last weekend, kind of

14
00:00:45,760 --> 00:00:47,520
Speaker 2: kick off at the beginning of the month.

15
00:00:47,680 --> 00:00:51,040
Speaker 4: So I'm busy. I'm out and about, you know.

16
00:00:51,960 --> 00:00:54,720
Speaker 1: I'm glad to hear it. I'm still in COVID recovery mode,

17
00:00:54,720 --> 00:00:57,240
Speaker 1: but I'm living vicariously through folks like you who are

18
00:00:57,320 --> 00:00:59,880
Speaker 1: out having a good time. I am very.

19
00:01:01,680 --> 00:01:01,920
Speaker 4: Well.

20
00:01:02,040 --> 00:01:04,759
Speaker 1: I have to tell you, I feel like a new

21
00:01:05,120 --> 00:01:09,080
Speaker 1: tech gripe of mine has just dropped. And that is

22
00:01:09,440 --> 00:01:14,880
Speaker 1: maybe you've seen them, these seemingly wholesome but very obviously

23
00:01:14,920 --> 00:01:18,800
Speaker 1: AI generated images. It'll be like the ones I've been

24
00:01:18,800 --> 00:01:23,120
Speaker 1: seeing lately. Are It'll be these AI generated images of

25
00:01:23,520 --> 00:01:27,800
Speaker 1: three older black people and the caption will be like, oh,

26
00:01:28,000 --> 00:01:31,760
Speaker 1: these three triplets just turned a hundred, leave them some

27
00:01:32,000 --> 00:01:35,640
Speaker 1: love for their birthday, and every single comment is like

28
00:01:36,280 --> 00:01:40,720
Speaker 1: how beautiful, yay, like happy birthday? Love it.

29
00:01:40,840 --> 00:01:41,640
Speaker 3: Have you seen this?

30
00:01:42,440 --> 00:01:44,959
Speaker 2: I haven't seen that one, but I did see something

31
00:01:45,040 --> 00:01:48,120
Speaker 2: yesterday where it was like it was like a food

32
00:01:48,280 --> 00:01:51,640
Speaker 2: influencer like or publication account, and they were they did

33
00:01:51,640 --> 00:01:54,560
Speaker 2: one of those things where it's like which picture are

34
00:01:54,600 --> 00:01:55,320
Speaker 2: you based.

35
00:01:55,080 --> 00:01:55,880
Speaker 4: On your birth month?

36
00:01:56,000 --> 00:01:59,120
Speaker 2: Except it was all AI generated food like that was

37
00:01:59,200 --> 00:02:00,880
Speaker 2: the prompt and it was kind of like wait, what,

38
00:02:01,240 --> 00:02:04,440
Speaker 2: like why why did this? Like you couldn't have taken

39
00:02:04,520 --> 00:02:07,920
Speaker 2: pictures of like actual cakes, like you're doing which AI

40
00:02:08,040 --> 00:02:09,160
Speaker 2: generated cake are you?

41
00:02:09,480 --> 00:02:09,600
Speaker 3: Like?

42
00:02:09,720 --> 00:02:11,440
Speaker 4: I don't know, but yeah, it's it's weird.

43
00:02:12,000 --> 00:02:14,920
Speaker 1: I am so sick of it, and honest, like the

44
00:02:15,000 --> 00:02:17,600
Speaker 1: cake thing part of me needs to take a step

45
00:02:17,600 --> 00:02:21,960
Speaker 1: back and be like, this is obviously, all things considered,

46
00:02:22,200 --> 00:02:25,400
Speaker 1: it's pretty benign because people are using AI to do

47
00:02:25,480 --> 00:02:27,760
Speaker 1: all kinds of things that are that are much more

48
00:02:27,919 --> 00:02:31,400
Speaker 1: problematic and worrisome than what AI generated cake or are

49
00:02:31,440 --> 00:02:36,760
Speaker 1: you or show these fake ninety year old elderly people

50
00:02:36,800 --> 00:02:40,160
Speaker 1: some birthday love? But it just it feels like one

51
00:02:40,480 --> 00:02:44,600
Speaker 1: additional small way that the Internet is becoming worse and

52
00:02:44,639 --> 00:02:46,560
Speaker 1: worse and worse. And I think the thing that bugs

53
00:02:46,600 --> 00:02:49,680
Speaker 1: me is seeing how much like obviously the people who

54
00:02:49,720 --> 00:02:52,600
Speaker 1: do this are just trying to get easy engagement, and

55
00:02:52,639 --> 00:02:54,040
Speaker 1: I guess part of me is like, well, they could

56
00:02:54,080 --> 00:02:56,880
Speaker 1: be doing in worse ways, but the fact that there

57
00:02:56,880 --> 00:03:00,680
Speaker 1: are people who seemingly like this and like they will

58
00:03:00,720 --> 00:03:04,280
Speaker 1: actually be like, oh, I'm cake one or happy birthday,

59
00:03:04,600 --> 00:03:05,720
Speaker 1: so they elder think it's.

60
00:03:05,880 --> 00:03:09,200
Speaker 2: Like it's the normalizing of it, Like it's the normalizing

61
00:03:09,200 --> 00:03:10,600
Speaker 2: that this is something that we're just going to be

62
00:03:10,680 --> 00:03:13,280
Speaker 2: seeing on our feeds all the time. And I think,

63
00:03:13,440 --> 00:03:15,280
Speaker 2: I mean, I do think, like I agree with you

64
00:03:15,280 --> 00:03:17,960
Speaker 2: where it's like this is so benign compared to some

65
00:03:18,040 --> 00:03:19,959
Speaker 2: of the other things that AI has been used for.

66
00:03:20,040 --> 00:03:25,760
Speaker 2: But it's like, okay, yeah, it's almost like like normalizing

67
00:03:25,919 --> 00:03:28,000
Speaker 2: the use of AI images, so then when it is

68
00:03:28,120 --> 00:03:31,560
Speaker 2: used for more nefarious purposes, it's it kind of slides

69
00:03:31,639 --> 00:03:33,520
Speaker 2: under the radar a little bit more easily cause we're

70
00:03:33,560 --> 00:03:35,120
Speaker 2: so used to seeing these images all the time.

71
00:03:35,160 --> 00:03:36,360
Speaker 4: Anyways, I think.

72
00:03:36,240 --> 00:03:39,800
Speaker 2: That's exactly why that's my issue too. I mean, on

73
00:03:39,840 --> 00:03:42,200
Speaker 2: top of the whole like copyright thing. I think that's

74
00:03:42,240 --> 00:03:44,400
Speaker 2: my issue too with a lot of the like movie

75
00:03:44,440 --> 00:03:50,960
Speaker 2: studios that are using like AI generated images or stuff

76
00:03:50,960 --> 00:03:53,320
Speaker 2: for like there, Like there was just some like Marvel

77
00:03:53,360 --> 00:03:55,800
Speaker 2: Show from I think last summer that came out that

78
00:03:55,840 --> 00:03:57,440
Speaker 2: there was a whole scandal about how they used like

79
00:03:57,480 --> 00:04:00,400
Speaker 2: AI for for like the opening credits, and it's like,

80
00:04:00,440 --> 00:04:03,200
Speaker 2: that's part of the bigger issue is it's just normalizing this,

81
00:04:03,760 --> 00:04:06,760
Speaker 2: the use of this technology to make these big things

82
00:04:06,840 --> 00:04:08,960
Speaker 2: that you know could be used.

83
00:04:08,720 --> 00:04:11,040
Speaker 4: In a bunch more nefarious way.

84
00:04:11,560 --> 00:04:15,320
Speaker 1: Yes, there was a recent controversy over a Netflix true

85
00:04:15,320 --> 00:04:18,880
Speaker 1: crime documentary called What Jennifer Did, about a young woman

86
00:04:18,920 --> 00:04:21,200
Speaker 1: who spoiler alert if you can't tell from the title

87
00:04:21,520 --> 00:04:25,240
Speaker 1: did something awful to her family, and in part of it,

88
00:04:25,360 --> 00:04:25,880
Speaker 1: I assumed it.

89
00:04:25,839 --> 00:04:28,920
Speaker 2: Was gonna be like Jennifer's Body.

90
00:04:28,400 --> 00:04:34,240
Speaker 1: I wish, I wish the reading two, Oh my God,

91
00:04:34,279 --> 00:04:36,800
Speaker 1: I am a justice for Jennifer's Body.

92
00:04:36,960 --> 00:04:37,320
Speaker 3: I am.

93
00:04:37,839 --> 00:04:39,719
Speaker 1: I was on the right side of history on that one.

94
00:04:40,080 --> 00:04:44,760
Speaker 1: But yeah, the movie used AI to generate what is

95
00:04:44,880 --> 00:04:48,440
Speaker 1: what purport to be historical images of her or like

96
00:04:48,880 --> 00:04:51,080
Speaker 1: archival images of her, and so in one of the

97
00:04:51,080 --> 00:04:54,360
Speaker 1: images it's like, well, that's clearly AI generated, and especially

98
00:04:54,360 --> 00:04:58,160
Speaker 1: for a documentary that is supposedly trying to show us

99
00:04:58,200 --> 00:05:01,600
Speaker 1: something true, something real, something that happen to be using

100
00:05:01,640 --> 00:05:04,719
Speaker 1: AI generated images without any kind of warning letting you

101
00:05:04,760 --> 00:05:08,000
Speaker 1: know that this is that we've recreated this. Really, I

102
00:05:08,040 --> 00:05:10,360
Speaker 1: think it brings up some ethical questions and I think

103
00:05:10,400 --> 00:05:12,120
Speaker 1: it really comes back to what you just said, that

104
00:05:12,920 --> 00:05:17,719
Speaker 1: these things might not be nefarious now, but because they

105
00:05:17,800 --> 00:05:21,200
Speaker 1: are benign there, it helps it sort of normalize it. Right,

106
00:05:21,240 --> 00:05:23,200
Speaker 1: So it helps it so like, oh, we shouldn't be

107
00:05:23,200 --> 00:05:25,880
Speaker 1: asking questions when we see AI used for this, and

108
00:05:25,880 --> 00:05:28,720
Speaker 1: so what happens when that use case is not so benign? Right,

109
00:05:28,920 --> 00:05:31,240
Speaker 1: what happens when it's a more important use case? I

110
00:05:31,320 --> 00:05:35,440
Speaker 1: think you really you've helped me understand why when I

111
00:05:35,440 --> 00:05:40,120
Speaker 1: see malicious use of AI, I'm angry or whatever. But

112
00:05:40,240 --> 00:05:43,120
Speaker 1: something about these like wholesome uses of it makes me

113
00:05:43,200 --> 00:05:44,960
Speaker 1: even more angry. I don't know why. I'm like, I

114
00:05:44,960 --> 00:05:47,520
Speaker 1: hate this even more. I think I think you really

115
00:05:47,600 --> 00:05:48,040
Speaker 1: nailed it.

116
00:05:48,920 --> 00:05:51,080
Speaker 2: I think it's the it's also the kind of collective

117
00:05:51,160 --> 00:05:53,200
Speaker 2: like wait, wait, I thought we just like I thought

118
00:05:53,200 --> 00:05:55,920
Speaker 2: we were at a point where we were like Hey, guys,

119
00:05:56,000 --> 00:05:58,000
Speaker 2: let's think a little bit more critically about AI and

120
00:05:58,040 --> 00:06:00,880
Speaker 2: whether we should be using it so casually. I thought

121
00:06:00,880 --> 00:06:04,880
Speaker 2: we reached that point as a society, but I guess not. Yeah,

122
00:06:04,960 --> 00:06:08,240
Speaker 2: I will say, I know this is awful. Another like

123
00:06:08,279 --> 00:06:11,520
Speaker 2: this is one where there's a lot of bad things

124
00:06:11,560 --> 00:06:13,599
Speaker 2: that can come from it. With those videos of I

125
00:06:13,640 --> 00:06:18,480
Speaker 2: mean not the videos, the AI pictures of Trump getting arrested.

126
00:06:18,760 --> 00:06:20,600
Speaker 1: I remember that. I'm going around.

127
00:06:20,400 --> 00:06:22,880
Speaker 2: Recently, there's some new ones that were going around because

128
00:06:22,880 --> 00:06:29,000
Speaker 2: of the ruling that we're kind of funny. Okay, objectively, yeah,

129
00:06:29,040 --> 00:06:31,400
Speaker 2: I know. Objectively, I'm like, this is bad. This is

130
00:06:31,400 --> 00:06:34,560
Speaker 2: bad that these inventures are out there, but they were

131
00:06:34,640 --> 00:06:35,200
Speaker 2: kind of funny.

132
00:06:35,320 --> 00:06:37,520
Speaker 1: I felt the same way about the first crap where

133
00:06:37,520 --> 00:06:38,960
Speaker 1: it's like, ooh, I wouldn't I would have. I would

134
00:06:39,000 --> 00:06:40,279
Speaker 1: have if only.

135
00:06:41,640 --> 00:06:43,440
Speaker 4: Like it's just the idea of him like running down

136
00:06:43,440 --> 00:06:44,240
Speaker 4: the street.

137
00:06:44,160 --> 00:06:49,479
Speaker 1: Like god, yeah, can I only wish. So, speaking of

138
00:06:49,480 --> 00:06:52,760
Speaker 1: stuff online not always being what it seems, we've got

139
00:06:52,760 --> 00:06:56,600
Speaker 1: to talk about this huge news about the disinformation campaign

140
00:06:56,720 --> 00:07:01,600
Speaker 1: out of Israel. So Israel organized a large scale disinformation

141
00:07:01,720 --> 00:07:06,000
Speaker 1: campaign which used fake accounts and disinformation to target progressive

142
00:07:06,000 --> 00:07:08,960
Speaker 1: activists and black members of Congress in the United States

143
00:07:09,040 --> 00:07:11,920
Speaker 1: and Canada. Herrit's first reported on the existence of this

144
00:07:12,000 --> 00:07:14,560
Speaker 1: operation way back in March, but now we have more

145
00:07:14,600 --> 00:07:17,440
Speaker 1: details about how it worked and exactly who was behind it.

146
00:07:17,880 --> 00:07:20,400
Speaker 1: They reported that it was launched after the start of

147
00:07:20,440 --> 00:07:22,920
Speaker 1: the war in Gaza and it was intended to sway

148
00:07:23,160 --> 00:07:27,520
Speaker 1: certain segments of the public on Israel's conduct. It really

149
00:07:27,560 --> 00:07:29,040
Speaker 1: sounds like the point of this whole thing was to

150
00:07:29,120 --> 00:07:33,080
Speaker 1: use social media to quote promote content that it's pro Israel,

151
00:07:33,160 --> 00:07:36,680
Speaker 1: anti Palestinian, and anti Muslim, as well as disinformation about

152
00:07:36,680 --> 00:07:40,240
Speaker 1: anti Semitism on American campuses. According to an investigation by

153
00:07:40,280 --> 00:07:43,280
Speaker 1: the Fake Reporter Organization published this week.

154
00:07:43,560 --> 00:07:47,440
Speaker 2: I will say the Fake Reporter Organization maybe not the

155
00:07:47,480 --> 00:07:48,360
Speaker 2: best name.

156
00:07:49,280 --> 00:07:51,080
Speaker 1: I know, but I was reading this, I was like

157
00:07:51,120 --> 00:07:53,480
Speaker 1: a little confused. I was like, are they the fake reporters?

158
00:07:54,280 --> 00:07:56,680
Speaker 2: Hello, I'm here from the Fake Reporter or like that

159
00:07:56,720 --> 00:07:59,480
Speaker 2: sounds like it would feel like a bit but you

160
00:07:59,560 --> 00:08:00,600
Speaker 2: know what, glad they're doing.

161
00:08:00,600 --> 00:08:02,440
Speaker 4: The work that they do was that it's importing totally.

162
00:08:04,240 --> 00:08:08,200
Speaker 1: So according to this reporting, this disinformation campaign came straight

163
00:08:08,240 --> 00:08:12,080
Speaker 1: from the Israeli government. Parrot's reports that it was established

164
00:08:12,080 --> 00:08:15,200
Speaker 1: by Israel's Diaspora Affairs Ministry, but it was run by

165
00:08:15,240 --> 00:08:18,240
Speaker 1: a private organization for fear of it being exposed and

166
00:08:18,320 --> 00:08:21,080
Speaker 1: like traced back to Israel. So how this whole thing

167
00:08:21,120 --> 00:08:23,680
Speaker 1: worked is if they set up fake news websites with

168
00:08:23,800 --> 00:08:27,080
Speaker 1: names like no Agenda or Unfold News, and then they

169
00:08:27,120 --> 00:08:30,160
Speaker 1: make fake social media accounts on Twitter and Facebook that

170
00:08:30,800 --> 00:08:33,199
Speaker 1: are kind of trying to seem like real people. It

171
00:08:33,240 --> 00:08:34,840
Speaker 1: seems like maybe they didn't do a great job. We'll

172
00:08:34,840 --> 00:08:36,760
Speaker 1: get to that in a moment. They would then use

173
00:08:36,800 --> 00:08:39,920
Speaker 1: these accounts to amass followers and then push out these

174
00:08:40,000 --> 00:08:43,120
Speaker 1: news stories. The news stories themselves on the websites that

175
00:08:43,120 --> 00:08:45,960
Speaker 1: they were setting up, they would just steal and repurpose

176
00:08:46,000 --> 00:08:48,640
Speaker 1: content from places like CNN and The New York Times

177
00:08:48,640 --> 00:08:51,680
Speaker 1: reports that they were using chat gpt to generate many

178
00:08:51,679 --> 00:08:55,640
Speaker 1: of the posts. The campaign also created three fake English

179
00:08:55,679 --> 00:09:00,520
Speaker 1: language news sites featuring pro Israel articles. So something I

180
00:09:00,600 --> 00:09:03,120
Speaker 1: found really interesting about this whole thing is that it

181
00:09:03,120 --> 00:09:06,560
Speaker 1: sounds like why they were doing this, according to the reporting,

182
00:09:06,840 --> 00:09:10,600
Speaker 1: was this understanding that perhaps Israel is like losing the

183
00:09:10,640 --> 00:09:15,240
Speaker 1: social media war of public opinion. Herot's reports. Various Israeli

184
00:09:15,280 --> 00:09:17,959
Speaker 1: officials say that the war in Gaza has exposed quote

185
00:09:18,000 --> 00:09:22,559
Speaker 1: a great failure in Israel's hesebara, or public diplomacy. Despite

186
00:09:22,600 --> 00:09:25,640
Speaker 1: the massive investment in different pr enterprises over the years,

187
00:09:26,040 --> 00:09:28,240
Speaker 1: Israel has not been able to effectively cope with the

188
00:09:28,240 --> 00:09:31,719
Speaker 1: flood of pro Palestinian messages on social media. Israel lacked

189
00:09:31,760 --> 00:09:34,280
Speaker 1: the necessary digital assets to contend with what it called

190
00:09:34,480 --> 00:09:38,520
Speaker 1: quote the pro Palestinian poison propaganda machine and to adequately

191
00:09:38,520 --> 00:09:43,040
Speaker 1: publicize hamostrocities to defend the war in Gaza. So it

192
00:09:43,120 --> 00:09:46,160
Speaker 1: just sounds like they were like people on social media

193
00:09:46,240 --> 00:09:49,240
Speaker 1: are too pro Palestinian, so we have to make some

194
00:09:49,280 --> 00:09:51,400
Speaker 1: fake accounts to see if that turns the tides.

195
00:09:52,320 --> 00:09:57,240
Speaker 2: Yeah, And I mean I think in this situation too,

196
00:09:57,360 --> 00:10:00,440
Speaker 2: like we've really seen some of the ways that social

197
00:10:00,480 --> 00:10:01,280
Speaker 2: media can be.

198
00:10:01,240 --> 00:10:03,439
Speaker 4: A really powerful tool for.

199
00:10:03,240 --> 00:10:07,320
Speaker 2: Uh spreading misinformation and also like getting important information out there.

200
00:10:07,320 --> 00:10:08,400
Speaker 4: Where I think a.

201
00:10:08,360 --> 00:10:10,360
Speaker 2: Lot of this backlash and a lot of this kind

202
00:10:10,400 --> 00:10:13,960
Speaker 2: of uh, A lot of the backlash to what Israel

203
00:10:14,080 --> 00:10:16,240
Speaker 2: is doing is because we're getting these images that are

204
00:10:16,280 --> 00:10:18,120
Speaker 2: out of Gaza and out of Rafa that are.

205
00:10:18,120 --> 00:10:20,559
Speaker 4: You know, real awful images.

206
00:10:20,600 --> 00:10:22,719
Speaker 2: A lot of us it's like the first time we're

207
00:10:22,760 --> 00:10:26,880
Speaker 2: really witnessing this, like not firsthand, but I guess like

208
00:10:26,960 --> 00:10:30,079
Speaker 2: through these actual images of what's happening, and they're like seriously,

209
00:10:30,120 --> 00:10:34,520
Speaker 2: I just I'm sure people listening feel the same, but

210
00:10:34,559 --> 00:10:37,160
Speaker 2: like I've seen some of literally the worst things I've

211
00:10:37,160 --> 00:10:39,280
Speaker 2: ever seen in my life over the past year, uh,

212
00:10:39,760 --> 00:10:44,120
Speaker 2: coming out of coming out of Rafa, and and that

213
00:10:44,280 --> 00:10:47,120
Speaker 2: has power, Like those images have power. And I think

214
00:10:47,280 --> 00:10:53,560
Speaker 2: the Israeli government and the Israeli Hasbara kind of machine

215
00:10:53,679 --> 00:10:56,240
Speaker 2: has relied a lot on these images, like not getting

216
00:10:56,280 --> 00:10:59,080
Speaker 2: out there and relied on kind of them being the

217
00:10:59,400 --> 00:11:03,079
Speaker 2: primary drivers of what the narrative is. And we're seeing

218
00:11:03,080 --> 00:11:07,000
Speaker 2: that start to fall apart. And you know, it makes

219
00:11:07,040 --> 00:11:09,880
Speaker 2: sense if the reaction is gonna be to go to

220
00:11:09,920 --> 00:11:13,280
Speaker 2: these extreme measures like creating fake support.

221
00:11:13,640 --> 00:11:15,360
Speaker 4: I mean, this is and this has been happened.

222
00:11:15,400 --> 00:11:17,480
Speaker 2: Like there was an image early on when there were

223
00:11:17,520 --> 00:11:20,400
Speaker 2: a lot of these big protests that were happening in

224
00:11:20,440 --> 00:11:24,280
Speaker 2: support of Palestinians. There was literally, I think image I

225
00:11:24,320 --> 00:11:26,760
Speaker 2: remember it was going around of like a pro Israel

226
00:11:27,320 --> 00:11:30,439
Speaker 2: protest that was entirely AI generated, Like it was a

227
00:11:30,760 --> 00:11:33,120
Speaker 2: it was like a very obviously AI generated photo. And

228
00:11:33,160 --> 00:11:36,040
Speaker 2: this was like early on, So yeah, I'm not surprised

229
00:11:36,080 --> 00:11:36,760
Speaker 2: to hear this.

230
00:11:38,720 --> 00:11:39,000
Speaker 4: Again.

231
00:11:39,040 --> 00:11:43,319
Speaker 2: I think, like, as somebody who is a Jewish American

232
00:11:43,640 --> 00:11:46,680
Speaker 2: who you know, a lot of my Judaism is rooted

233
00:11:46,720 --> 00:11:48,520
Speaker 2: in a lot of like anti Zionists.

234
00:11:49,720 --> 00:11:51,400
Speaker 4: Diaspora values.

235
00:11:52,160 --> 00:11:55,160
Speaker 2: It's not surprising just see this happen, because this has

236
00:11:55,240 --> 00:11:57,680
Speaker 2: been happening, and this, this kind of has been the

237
00:11:57,720 --> 00:12:03,199
Speaker 2: direction that the Israeli state has been going, especially especially recently.

238
00:12:03,760 --> 00:12:05,840
Speaker 1: And I think it goes back to a conversation you

239
00:12:05,880 --> 00:12:10,640
Speaker 1: and I had about campus protests. How there's just I

240
00:12:10,679 --> 00:12:15,480
Speaker 1: do feel like there's this underlying attitude that people could

241
00:12:15,480 --> 00:12:20,280
Speaker 1: not possibly be watching what's going on and be basing

242
00:12:20,320 --> 00:12:23,760
Speaker 1: their stances on what they're seeing, right, and so like,

243
00:12:24,400 --> 00:12:28,680
Speaker 1: if young people on campuses are protesting in supportive Palestinians,

244
00:12:28,720 --> 00:12:32,320
Speaker 1: it has to be TikTok influencing influencing them in some way.

245
00:12:32,800 --> 00:12:36,120
Speaker 1: If people on social media are are not supportive of

246
00:12:36,160 --> 00:12:39,320
Speaker 1: what Israel has been doing to Palestinians, it must like

247
00:12:39,360 --> 00:12:41,960
Speaker 1: we need to get in there and like correct the record, right.

248
00:12:42,400 --> 00:12:45,600
Speaker 2: I think a big kind of point that the Israeli

249
00:12:45,640 --> 00:12:48,400
Speaker 2: government has relied on for a while is drawing the

250
00:12:48,400 --> 00:12:51,320
Speaker 2: focus away from what is happening to the Palestinian and people,

251
00:12:51,320 --> 00:12:52,880
Speaker 2: and instead of turning it into an issue of like,

252
00:12:52,960 --> 00:12:55,720
Speaker 2: quote unquote Jewish safety, which it isn't. It is not

253
00:12:55,800 --> 00:13:00,680
Speaker 2: an issue of Jewish safety to be murdering of billions

254
00:13:00,720 --> 00:13:02,400
Speaker 2: and murdering children like obvious.

255
00:13:02,480 --> 00:13:05,240
Speaker 4: That is so obviously not an equivalency.

256
00:13:05,679 --> 00:13:08,120
Speaker 2: And again, I think going back to the campus protests

257
00:13:08,200 --> 00:13:10,000
Speaker 2: very much like turning it into an issue of like, oh, well,

258
00:13:10,080 --> 00:13:13,319
Speaker 2: Jewish students don't feel safe, that is not the case.

259
00:13:13,360 --> 00:13:14,640
Speaker 4: That is a huge generalization.

260
00:13:14,760 --> 00:13:16,600
Speaker 2: I'm sure there are some students out there that don't

261
00:13:16,600 --> 00:13:20,080
Speaker 2: feel safe, but that is an individual issue. I personally

262
00:13:20,520 --> 00:13:22,360
Speaker 2: have a lot of friends that are Jewish that have

263
00:13:22,440 --> 00:13:25,080
Speaker 2: been involved with the protests. I know, like Jewish Voice

264
00:13:25,120 --> 00:13:27,960
Speaker 2: or Peace has been a big organization that has a

265
00:13:27,960 --> 00:13:30,720
Speaker 2: lot of chapters on college campuses that have been really

266
00:13:30,720 --> 00:13:33,480
Speaker 2: involved with these protests. I think like it has been

267
00:13:33,520 --> 00:13:36,200
Speaker 2: a very kind of important propaganda point for them to

268
00:13:37,120 --> 00:13:39,840
Speaker 2: draw attention away from the real issue, which is, yes,

269
00:13:39,920 --> 00:13:44,480
Speaker 2: these are students that are protesting human rights atrocities that

270
00:13:44,480 --> 00:13:49,080
Speaker 2: they're witnessing every day through social media, through you know,

271
00:13:49,640 --> 00:13:53,600
Speaker 2: family or friends that were on the ground there. Like

272
00:13:53,760 --> 00:13:56,120
Speaker 2: just just the main thing is just driving that focus

273
00:13:56,120 --> 00:13:57,760
Speaker 2: away when that yeah, that is the issue.

274
00:13:57,760 --> 00:13:59,079
Speaker 4: It has nothing to do with.

275
00:14:01,080 --> 00:14:04,520
Speaker 2: Like, honestly, it has nothing to do with me as

276
00:14:04,520 --> 00:14:06,319
Speaker 2: a Jewish American. It has nothing to do with my

277
00:14:06,440 --> 00:14:08,360
Speaker 2: other follow Jewish Americans. It has to do with what

278
00:14:08,480 --> 00:14:11,400
Speaker 2: is clearly a human rights atrocity that is happening right now.

279
00:14:12,400 --> 00:14:15,959
Speaker 1: So it's funny that you say this because the fake

280
00:14:16,040 --> 00:14:18,719
Speaker 1: accounts that it's when you look at what they were

281
00:14:18,760 --> 00:14:20,800
Speaker 1: saying and who they were targeting, the kind of messages

282
00:14:20,800 --> 00:14:22,920
Speaker 1: they were putting out, is it is so clear that

283
00:14:22,960 --> 00:14:27,480
Speaker 1: it's like, oh, we are trying to get the perspective,

284
00:14:27,480 --> 00:14:31,120
Speaker 1: ship the perspective away from what is actually happening to Palestinians.

285
00:14:31,160 --> 00:14:32,840
Speaker 1: It's like so clear. So when you look at the

286
00:14:32,880 --> 00:14:35,280
Speaker 1: messages they were putting out, they had these different messages

287
00:14:35,280 --> 00:14:37,160
Speaker 1: meant to appeal to different groups they were trying to target.

288
00:14:37,400 --> 00:14:40,200
Speaker 1: They had the United Citizens for Canada site, which had

289
00:14:40,280 --> 00:14:44,840
Speaker 1: multiple social media accounts and disseminated heavily Islamophobic material, including

290
00:14:44,840 --> 00:14:47,600
Speaker 1: claims that Muslim immigrants were a threat to Canada and

291
00:14:47,640 --> 00:14:51,840
Speaker 1: demanding a Shari estate. Another was the Arab Slave Trade site,

292
00:14:51,920 --> 00:14:55,080
Speaker 1: which was copied almost entirely from Wikipedia and aimed at

293
00:14:55,080 --> 00:14:57,640
Speaker 1: black Americans, trying to repeat the message that the Arabs

294
00:14:57,640 --> 00:15:00,680
Speaker 1: had been slave traders in Africa. I actually really saw

295
00:15:00,760 --> 00:15:05,240
Speaker 1: this messaging my like organically via social media and also

296
00:15:05,920 --> 00:15:09,360
Speaker 1: repeated by people that I know like this. This was

297
00:15:09,400 --> 00:15:13,120
Speaker 1: messaging that I will say, like anecdotally personally I saw

298
00:15:13,200 --> 00:15:15,800
Speaker 1: sort of like breakthrough on social media a little bit.

299
00:15:16,200 --> 00:15:18,280
Speaker 4: Yeah, that's crazy. And I will say.

300
00:15:18,080 --> 00:15:21,600
Speaker 6: With that one too, that's wild because that is also

301
00:15:22,440 --> 00:15:26,120
Speaker 6: an anti Semitic talking point been used by you know,

302
00:15:26,240 --> 00:15:29,120
Speaker 6: other people on the right that if wanted to spread misinformation,

303
00:15:29,160 --> 00:15:32,240
Speaker 6: like anti Semitic misinformation, has been saying that it was.

304
00:15:32,200 --> 00:15:34,000
Speaker 2: Like Jewish people that were involved with the Strength that

305
00:15:34,040 --> 00:15:35,560
Speaker 2: we're leading the Strength slave train and all that. So

306
00:15:35,560 --> 00:15:37,600
Speaker 2: it's like, wow, okay, we can't even come up with

307
00:15:37,640 --> 00:15:40,680
Speaker 2: our with another with another narrative. We're just gonna copy

308
00:15:40,680 --> 00:15:43,320
Speaker 2: and paste it, but like switch it from Jewish people

309
00:15:43,360 --> 00:15:46,880
Speaker 2: to Arab people, like we can't the Israeli state can't

310
00:15:46,880 --> 00:15:50,520
Speaker 2: even come up with their own hate narratives now, Like yes, insane.

311
00:15:50,840 --> 00:15:53,080
Speaker 1: So much of this stuff is so sloppy. That is

312
00:15:53,120 --> 00:15:55,400
Speaker 1: something that I really like when I was reading about this,

313
00:15:55,520 --> 00:15:58,680
Speaker 1: influenced campaign. I was like, y'all couldn't even like get that,

314
00:15:59,040 --> 00:16:02,040
Speaker 1: get this, cause have there be consistency in this. So

315
00:16:02,160 --> 00:16:05,720
Speaker 1: this one's my favorite. Yet another site was called Serenity Now,

316
00:16:06,040 --> 00:16:09,680
Speaker 1: which branded itself as an anarchist and anti establishment group.

317
00:16:09,960 --> 00:16:12,600
Speaker 1: It sought to convince young Americans to oppose the creation

318
00:16:12,680 --> 00:16:16,360
Speaker 1: of a Palestinian state because states are man made structures

319
00:16:16,560 --> 00:16:19,360
Speaker 1: and a Palestinian state would hurt the goals of the

320
00:16:19,400 --> 00:16:20,560
Speaker 1: progressive movement.

321
00:16:21,240 --> 00:16:25,040
Speaker 2: Oh my god, Serenity Now I really like brought me

322
00:16:25,160 --> 00:16:28,400
Speaker 2: back to like my college organizing days, because I swear

323
00:16:28,440 --> 00:16:30,760
Speaker 2: to god, there was like one dude who like this

324
00:16:30,840 --> 00:16:31,880
Speaker 2: would be his talking.

325
00:16:32,120 --> 00:16:36,680
Speaker 1: Oh I know that dude. Oh god, Oh my gosh.

326
00:16:36,760 --> 00:16:40,360
Speaker 1: It's like you can be so left that you've got

327
00:16:40,360 --> 00:16:42,440
Speaker 1: to swing back around to the other side, and now

328
00:16:42,480 --> 00:16:44,080
Speaker 1: you're likeying a right wing point.

329
00:16:44,280 --> 00:16:46,360
Speaker 2: Yeah, or you're like just too too deep in like

330
00:16:46,400 --> 00:16:49,480
Speaker 2: the theory spiral that it's like you have not talked

331
00:16:49,480 --> 00:16:52,320
Speaker 2: to a single other human being about like the realities

332
00:16:52,360 --> 00:16:53,840
Speaker 2: of their Like oh my god.

333
00:16:54,000 --> 00:16:55,960
Speaker 4: But yeah, that's I mean. But yeah.

334
00:16:56,000 --> 00:16:59,160
Speaker 2: I on the other hand, the fact that they're going

335
00:16:59,160 --> 00:17:01,240
Speaker 2: in that direction, I can read that and be like

336
00:17:01,240 --> 00:17:03,760
Speaker 2: Oh my god, I recognized this, like line of thought.

337
00:17:04,480 --> 00:17:07,800
Speaker 2: That just shows that this is like a decent propaganda technique.

338
00:17:08,200 --> 00:17:10,800
Speaker 4: Yeah, yeah, and.

339
00:17:10,720 --> 00:17:14,439
Speaker 1: I think that. I think the technique of driving wedges

340
00:17:14,560 --> 00:17:17,760
Speaker 1: and pitting marginalized groups against each other and pitting like

341
00:17:18,720 --> 00:17:21,560
Speaker 1: anarchists progressive activist against like like like it's a it's

342
00:17:21,600 --> 00:17:24,880
Speaker 1: a it's a time tested strategy. They also were targeting

343
00:17:24,960 --> 00:17:28,560
Speaker 1: specific black elected officials. The operation focused on more than

344
00:17:28,560 --> 00:17:31,200
Speaker 1: a dozen members of Congress who are black and Democrats.

345
00:17:31,280 --> 00:17:35,760
Speaker 1: According to fake reporter, representative Richie Torres, a Democrat out

346
00:17:35,760 --> 00:17:37,960
Speaker 1: of New York who's pretty outspoken about its pro as

347
00:17:38,040 --> 00:17:41,680
Speaker 1: real views, was targeted in addition to Jeffreys and Warnock.

348
00:17:42,000 --> 00:17:44,560
Speaker 1: So Some of the fake accounts would respond to Richie

349
00:17:44,560 --> 00:17:48,000
Speaker 1: Torres's posts on Twitter commenting on anti semitism on college

350
00:17:48,000 --> 00:17:51,639
Speaker 1: campuses and in major US cities. In response to a

351
00:17:51,680 --> 00:17:54,680
Speaker 1: December eighth post on Twitter by Torres about fire safety,

352
00:17:55,000 --> 00:17:58,439
Speaker 1: one fake account replied harmas is perpetrating the conflict. The

353
00:17:58,480 --> 00:18:01,359
Speaker 1: posts included a hashtag that said Jews are being persecuted.

354
00:18:01,600 --> 00:18:04,639
Speaker 1: Over On Facebook, the fake accounts posted on Jeffrey's public

355
00:18:04,640 --> 00:18:06,760
Speaker 1: page asking if you had seen a report about the

356
00:18:06,840 --> 00:18:11,199
Speaker 1: United Nations allegedly employing members of Hamas in Gaza. So

357
00:18:11,440 --> 00:18:14,160
Speaker 1: it is really fascinating to me to see how they

358
00:18:14,160 --> 00:18:17,480
Speaker 1: were targeting black folks in specific. Like it reminds me

359
00:18:17,600 --> 00:18:21,719
Speaker 1: that we often see like foreign influence campaigns that target

360
00:18:21,800 --> 00:18:25,200
Speaker 1: black people specifically, and part of me wonders if they

361
00:18:25,240 --> 00:18:28,520
Speaker 1: just see our community as like less critical or like

362
00:18:28,680 --> 00:18:33,320
Speaker 1: more easily swayed. That is complete like bs, but it

363
00:18:33,359 --> 00:18:36,040
Speaker 1: makes me wonder if like that's how they see us

364
00:18:36,160 --> 00:18:41,720
Speaker 1: as easily swayed, easily cajoled, easily influenced, and so like

365
00:18:41,840 --> 00:18:44,600
Speaker 1: that's why they target us, whether or not that's true.

366
00:18:44,840 --> 00:18:48,240
Speaker 2: Yeah, I mean I wouldn't. I mean I wouldn't be surprised.

367
00:18:48,280 --> 00:18:53,879
Speaker 2: And like we're talking about like Israel is legally in

368
00:18:53,960 --> 00:18:58,600
Speaker 2: apartheized state, like it is the Israeli government like runs

369
00:18:58,680 --> 00:19:00,520
Speaker 2: on a racist sideology.

370
00:19:00,600 --> 00:19:02,680
Speaker 4: I would not be surprised if that were the case.

371
00:19:03,040 --> 00:19:04,879
Speaker 1: Yeah, Like when I was reading this, I was like,

372
00:19:05,000 --> 00:19:08,240
Speaker 1: this feels anti black, even though I can't exactly name

373
00:19:08,400 --> 00:19:11,480
Speaker 1: why that is, it feels anti black to me. I'll

374
00:19:11,520 --> 00:19:13,720
Speaker 1: get back to you on on the why this I

375
00:19:13,800 --> 00:19:17,120
Speaker 1: feel it. So I should say it sounds like they

376
00:19:17,119 --> 00:19:20,520
Speaker 1: did not do a great job of this influence campaign.

377
00:19:20,960 --> 00:19:23,919
Speaker 1: In at least two instances, accounts with profile photos of

378
00:19:23,960 --> 00:19:27,600
Speaker 1: black men would then post about being middle aged Jewish women.

379
00:19:27,840 --> 00:19:30,160
Speaker 1: On one hundred and eighteen posts in which the fake

380
00:19:30,160 --> 00:19:34,320
Speaker 1: account shared pro Israel articles, the same sentence appeared, quote

381
00:19:34,640 --> 00:19:37,480
Speaker 1: I gotta reevaluate my opinions due to this new information,

382
00:19:39,080 --> 00:19:42,239
Speaker 1: which like, that's so someone trying to sound like a

383
00:19:42,240 --> 00:19:44,480
Speaker 1: black person but like getting it wrong. And this is

384
00:19:44,480 --> 00:19:47,040
Speaker 1: why I always say, if somebody is on the Internet

385
00:19:47,440 --> 00:19:49,320
Speaker 1: purporting to be a black person, but the way that

386
00:19:49,359 --> 00:19:53,520
Speaker 1: they're speaking or however, it just sounds off your sobidy

387
00:19:53,640 --> 00:19:54,800
Speaker 1: senses should be tingling.

388
00:19:55,080 --> 00:19:57,400
Speaker 4: Oh man, I can't even do their racism right.

389
00:19:58,040 --> 00:19:59,040
Speaker 1: Exactly exactly.

390
00:20:04,760 --> 00:20:17,120
Speaker 3: Let's take a quick break. Ed are back.

391
00:20:20,800 --> 00:20:24,800
Speaker 1: So Facebook has now removed these fake profiles. Fake Reporter's

392
00:20:24,840 --> 00:20:28,840
Speaker 1: executive director Achia Shots told Herod's that quote running the

393
00:20:28,880 --> 00:20:33,080
Speaker 1: foreign influence campaign against American lawmakers is amateurish, irresponsible an

394
00:20:33,119 --> 00:20:36,320
Speaker 1: anti democratic. Fake Reporter has called on the Israeli government

395
00:20:36,400 --> 00:20:39,920
Speaker 1: to take steps against foreign influence campaigns. He added, the

396
00:20:39,960 --> 00:20:43,520
Speaker 1: Israeli government is expected to refrain from taking similar actions,

397
00:20:43,640 --> 00:20:47,600
Speaker 1: especially ones at target israel Significant democratic partners. The money

398
00:20:47,640 --> 00:20:49,919
Speaker 1: spent on this campaign would have been better spent in

399
00:20:49,960 --> 00:20:53,800
Speaker 1: protecting the Israeli public from foreign intervention. And that is

400
00:20:53,840 --> 00:21:00,159
Speaker 1: a great point that I don't understand how I want

401
00:21:00,200 --> 00:21:02,680
Speaker 1: to paying somebody, paying a firm to run a bunch

402
00:21:02,720 --> 00:21:05,800
Speaker 1: of fake accounts and make a bunch of fake news sites.

403
00:21:06,200 --> 00:21:09,320
Speaker 1: Is making people in Israel more safe?

404
00:21:09,440 --> 00:21:11,639
Speaker 4: Yeah, definitely.

405
00:21:11,880 --> 00:21:15,159
Speaker 2: I mean, you know, it's like the same It's the

406
00:21:15,160 --> 00:21:19,240
Speaker 2: same thing the US does where it's like, God, I

407
00:21:19,320 --> 00:21:22,159
Speaker 2: remember there was some tweet a while ago about like,

408
00:21:22,200 --> 00:21:25,280
Speaker 2: you know, the brave soldiers like keeping our countries or

409
00:21:25,400 --> 00:21:26,679
Speaker 2: like protecting our freedom and.

410
00:21:26,680 --> 00:21:28,680
Speaker 4: Somebody who's like, what what's my freedom doing in the

411
00:21:28,720 --> 00:21:33,399
Speaker 4: Middle East? Like they're protecting our freedom like on the

412
00:21:33,440 --> 00:21:34,200
Speaker 4: other side of the.

413
00:21:34,119 --> 00:21:36,840
Speaker 2: World, and like, I yeah, it's like the same thing

414
00:21:36,880 --> 00:21:39,439
Speaker 2: where it's like oh wow, Like once you start digging deeper,

415
00:21:39,480 --> 00:21:44,160
Speaker 2: I think into a lot of these like very militant

416
00:21:44,520 --> 00:21:49,000
Speaker 2: kind of campaigns, it's very clear that what they say

417
00:21:49,000 --> 00:21:51,040
Speaker 2: they're doing is not what they're actually doing.

418
00:21:52,160 --> 00:21:55,720
Speaker 1: Right, Well, that's exactly the point. And I should add

419
00:21:55,720 --> 00:21:57,880
Speaker 1: that when we talk about these kinds of influence campaigns,

420
00:21:57,920 --> 00:21:59,760
Speaker 1: and it's always important to note that they are not

421
00:22:00,480 --> 00:22:04,040
Speaker 1: generally very successful in their intended impacts. It sounds like

422
00:22:04,040 --> 00:22:06,600
Speaker 1: this one did not have the biggest impact. Both Facebook

423
00:22:06,640 --> 00:22:09,920
Speaker 1: and Open ais that the campaign did not have widespread impact.

424
00:22:10,160 --> 00:22:13,160
Speaker 1: The fake accounts did accumulate more than forty thousand followers

425
00:22:13,160 --> 00:22:17,320
Speaker 1: across Twitter, Facebook, and Instagram. However, many of those followers

426
00:22:17,359 --> 00:22:19,560
Speaker 1: may have been bought, and it did not generate a

427
00:22:19,680 --> 00:22:22,879
Speaker 1: meaningfully large audience. But I think it is still worth

428
00:22:22,920 --> 00:22:28,280
Speaker 1: talking about that, you know, even if it wasn't particularly

429
00:22:28,320 --> 00:22:32,040
Speaker 1: effective in swaying the communities it was targeting, it's still

430
00:22:32,080 --> 00:22:33,800
Speaker 1: important to note that, like this day kind of thing

431
00:22:33,840 --> 00:22:36,520
Speaker 1: is going on, and so when you are using social media,

432
00:22:36,960 --> 00:22:38,640
Speaker 1: it's just kind of like what we were talking about

433
00:22:38,680 --> 00:22:41,520
Speaker 1: with the AI images. Like I think that this kind

434
00:22:41,560 --> 00:22:44,200
Speaker 1: of thing is becoming more normalized, and that it creates

435
00:22:44,200 --> 00:22:47,359
Speaker 1: an Internet landscape where we can't just count on getting

436
00:22:47,480 --> 00:22:51,280
Speaker 1: authentic information or content from real humans. You never know

437
00:22:51,280 --> 00:22:52,760
Speaker 1: what's going on, and I just think it makes us

438
00:22:52,800 --> 00:22:55,840
Speaker 1: all less safe and less secure when our Internet landscape

439
00:22:56,080 --> 00:23:00,600
Speaker 1: is just a wash in these kinds of media manipulation

440
00:23:00,720 --> 00:23:02,119
Speaker 1: campaigns exactly.

441
00:23:02,480 --> 00:23:05,359
Speaker 4: And I mean it goes back to the whole conversation.

442
00:23:04,880 --> 00:23:08,840
Speaker 2: About X, right, now and how it hurts me to

443
00:23:08,880 --> 00:23:11,520
Speaker 2: call it X, but you know, I'm talking negatively about it,

444
00:23:11,560 --> 00:23:12,879
Speaker 2: so I will infer to its X.

445
00:23:14,960 --> 00:23:18,520
Speaker 4: It's like your child I don't know or whatever.

446
00:23:18,720 --> 00:23:21,040
Speaker 1: Like I was just thinking that, like when I when

447
00:23:21,119 --> 00:23:24,080
Speaker 1: when I'm purporting on something that X that they've done bad,

448
00:23:24,119 --> 00:23:26,640
Speaker 1: I'm like, it's X. When it's something that's like kind

449
00:23:26,640 --> 00:23:29,200
Speaker 1: of okay, I'm like on Twitter. Yeah, it's like when

450
00:23:29,200 --> 00:23:32,520
Speaker 1: I when I mentioned behave, I'm like, oh, your child

451
00:23:32,720 --> 00:23:36,120
Speaker 1: has skipped school. My mom would always say my dad right.

452
00:23:37,680 --> 00:23:40,840
Speaker 2: But yeah, but it's it's the whole bigger conversation about

453
00:23:41,520 --> 00:23:44,480
Speaker 2: X right now and how it uses to be, you know,

454
00:23:45,000 --> 00:23:47,199
Speaker 2: with still having lots of flaws, but it used to

455
00:23:47,200 --> 00:23:49,840
Speaker 2: be a place where you could get information, you could

456
00:23:49,840 --> 00:23:52,200
Speaker 2: get first hand accounts, you could get all these different

457
00:23:52,359 --> 00:23:56,720
Speaker 2: kind of journalists and experts like weighing on things, and

458
00:23:56,720 --> 00:23:59,600
Speaker 2: now it's just kind of turned into like bots following

459
00:23:59,680 --> 00:24:04,160
Speaker 2: thoughts and like sharing misinformation and.

460
00:24:05,560 --> 00:24:07,320
Speaker 4: Like bots hyping each other up.

461
00:24:07,400 --> 00:24:10,840
Speaker 5: Like it's like not even I don't, like I don't

462
00:24:10,840 --> 00:24:13,240
Speaker 5: even feel like I'm like communicating with real people when

463
00:24:13,280 --> 00:24:15,320
Speaker 5: I open like Twitter now, like I'm just like, oh,

464
00:24:15,320 --> 00:24:21,520
Speaker 5: I'm just seeing it's like content that is either super

465
00:24:21,520 --> 00:24:23,960
Speaker 5: not relevant to what I care about, or yeah, or

466
00:24:24,000 --> 00:24:26,600
Speaker 5: it's just this kind of like nightmare of like bots

467
00:24:26,640 --> 00:24:30,280
Speaker 5: and artificial like AI generated stuff and whatever.

468
00:24:31,520 --> 00:24:34,200
Speaker 1: Yeah, I feel the same way. And I should say,

469
00:24:34,240 --> 00:24:36,840
Speaker 1: like in our life, we were just talking about disinformation

470
00:24:36,920 --> 00:24:39,160
Speaker 1: campaigns being run by other countries, which we talk about

471
00:24:39,160 --> 00:24:41,520
Speaker 1: a lot when we talk about medium manipulation online, but

472
00:24:42,040 --> 00:24:45,440
Speaker 1: we should also keep in mind that it's not always

473
00:24:45,560 --> 00:24:49,080
Speaker 1: about another country like Israel or Russia trying to manipulate us.

474
00:24:49,440 --> 00:24:52,600
Speaker 1: In addition to all the bots, there are just still

475
00:24:53,200 --> 00:24:56,840
Speaker 1: fashion and blood humans out there on Twitter spreading misinformation.

476
00:24:57,600 --> 00:24:59,640
Speaker 1: And a new study is actually shedding light on who

477
00:24:59,720 --> 00:25:03,679
Speaker 1: is that that is on Twitter, and I am sorry

478
00:25:03,680 --> 00:25:05,520
Speaker 1: to say that it is a lot of women in

479
00:25:05,560 --> 00:25:10,000
Speaker 1: their late fifties, specifically, so Ours Technica reports that there's

480
00:25:10,040 --> 00:25:14,640
Speaker 1: this new study by researchers Shahar Barbie Bartov, Brianni Swier Thompson,

481
00:25:14,680 --> 00:25:17,200
Speaker 1: and Near Grinberg that was released this week that looks

482
00:25:17,200 --> 00:25:19,679
Speaker 1: at a large panel of Twitter accounts that are associated

483
00:25:19,680 --> 00:25:23,160
Speaker 1: with US based voters. So it identifies that a kind

484
00:25:23,160 --> 00:25:28,280
Speaker 1: of shockingly small handful of misinformation super spreaders is what

485
00:25:28,280 --> 00:25:31,679
Speaker 1: they're calling them, which represents just zero point three percent

486
00:25:31,760 --> 00:25:35,480
Speaker 1: of the accounts, but are responsible for sharing eighty percent

487
00:25:35,560 --> 00:25:39,280
Speaker 1: of Twitter's links to fake news or unreliable news sources.

488
00:25:39,640 --> 00:25:42,680
Speaker 1: So that means a lot less people than you might

489
00:25:42,720 --> 00:25:44,760
Speaker 1: think are responsible for the spread of the bulk of

490
00:25:44,800 --> 00:25:49,640
Speaker 1: misinformation from unreliable sources on Twitter. It really just does

491
00:25:49,760 --> 00:25:53,080
Speaker 1: sound like this is people who are like power sharers

492
00:25:53,160 --> 00:25:56,400
Speaker 1: on Twitter. According to the study, on an average day

493
00:25:56,400 --> 00:25:59,199
Speaker 1: on Twitter at this time, only seven percent of the

494
00:25:59,240 --> 00:26:02,880
Speaker 1: news story shareed linked to sites prone to publishing misinformation,

495
00:26:03,280 --> 00:26:06,040
Speaker 1: but super spreaders ended up accounting for most of these

496
00:26:06,080 --> 00:26:09,160
Speaker 1: for two reasons. One is if they shared more newslengths

497
00:26:09,160 --> 00:26:12,199
Speaker 1: than anybody else on the platform an average of sixteen

498
00:26:12,280 --> 00:26:15,920
Speaker 1: a day compared to less than one from the random sampling.

499
00:26:16,640 --> 00:26:18,320
Speaker 1: And most of the time these people are just like

500
00:26:18,600 --> 00:26:22,640
Speaker 1: smashing that retweet button like nobody's business. The study found

501
00:26:22,680 --> 00:26:25,719
Speaker 1: that three quarters of these super spreaders their contact was

502
00:26:25,800 --> 00:26:28,719
Speaker 1: just retweets. So you might be wondering, like, who are

503
00:26:28,800 --> 00:26:32,600
Speaker 1: these people? Well, Ours Technica says they are kind of

504
00:26:32,680 --> 00:26:35,480
Speaker 1: like our aunties and our moms. There are women in

505
00:26:35,520 --> 00:26:39,560
Speaker 1: their late fifties. Ours Technical reports that the super spreaders

506
00:26:39,720 --> 00:26:42,680
Speaker 1: are sixty percent more likely to be female. They're also

507
00:26:42,800 --> 00:26:45,200
Speaker 1: a little bit older, on average fifty eight years old,

508
00:26:45,520 --> 00:26:48,160
Speaker 1: nearly twenty years older than the sample group as a whole.

509
00:26:48,760 --> 00:26:51,560
Speaker 1: And while much of the misinformation about the election largely

510
00:26:51,600 --> 00:26:55,000
Speaker 1: circulated within Republican circles, only sixty four percent of the

511
00:26:55,040 --> 00:26:58,719
Speaker 1: super spreaders were registered Republicans. Nearly twenty percent were registered

512
00:26:58,720 --> 00:27:04,320
Speaker 1: as Democrats. Well, it is like ladies in their late fifties.

513
00:27:04,600 --> 00:27:07,000
Speaker 1: But I actually have a theory on what's going on.

514
00:27:07,119 --> 00:27:09,479
Speaker 1: This is just my theory. This is not in the study.

515
00:27:10,080 --> 00:27:14,200
Speaker 1: I think it's this sounds like peak auntie behavior to me,

516
00:27:14,840 --> 00:27:16,520
Speaker 1: like spending, you know what I mean?

517
00:27:16,760 --> 00:27:22,320
Speaker 4: Yeah, like that, it's like like just like expend.

518
00:27:22,760 --> 00:27:26,280
Speaker 1: Yes, Like when I think about how some of my

519
00:27:26,560 --> 00:27:28,440
Speaker 1: the women in my life who are in their late fifties,

520
00:27:28,480 --> 00:27:31,280
Speaker 1: how they roll online. The fact that it's a lot

521
00:27:31,320 --> 00:27:33,280
Speaker 1: of retweets really speaks to me. I'm like, Oh, that's

522
00:27:33,920 --> 00:27:37,000
Speaker 1: my aunties. They love they they love us share people

523
00:27:37,040 --> 00:27:40,120
Speaker 1: of our of my generation and maybe your generation. Joey,

524
00:27:40,560 --> 00:27:43,840
Speaker 1: you've had some sense of like if you went to

525
00:27:43,920 --> 00:27:47,000
Speaker 1: your Facebook page and you ha had shared a hundred

526
00:27:47,119 --> 00:27:49,560
Speaker 1: things in one day, your friends might be like, are

527
00:27:49,600 --> 00:27:52,480
Speaker 1: they okay, oh yeah, but I don't think that aunties

528
00:27:52,520 --> 00:27:53,440
Speaker 1: have the same vibe.

529
00:27:53,640 --> 00:27:56,879
Speaker 2: Yeah, I mean, I like fairly open Facebook anymore for

530
00:27:57,400 --> 00:28:00,680
Speaker 2: this reason because it is mostly like, yeah, a lot

531
00:28:00,680 --> 00:28:04,200
Speaker 2: of the older people in my family sharing a lot

532
00:28:04,240 --> 00:28:06,760
Speaker 2: of these articles that I'm like, I don't even have

533
00:28:06,880 --> 00:28:10,560
Speaker 2: the energy to keep getting annoyed at like every single.

534
00:28:10,359 --> 00:28:11,440
Speaker 4: Piece of misinformation.

535
00:28:11,720 --> 00:28:15,240
Speaker 2: But yeah, it is like, I mean, Facebook has kind

536
00:28:15,280 --> 00:28:16,880
Speaker 2: of just become this like.

537
00:28:18,840 --> 00:28:24,280
Speaker 4: Fear of yeah, we're people. Okay, So this might snick

538
00:28:24,280 --> 00:28:25,920
Speaker 4: wing be here, this might sound a little bit of astrautge,

539
00:28:25,960 --> 00:28:27,120
Speaker 4: but I this is why.

540
00:28:27,520 --> 00:28:30,520
Speaker 2: And on one day, Pridget that I feel like I

541
00:28:30,560 --> 00:28:32,320
Speaker 2: come on to do a bigger episode about this. But

542
00:28:32,440 --> 00:28:37,560
Speaker 2: I I am a little bit critical of the sort

543
00:28:37,600 --> 00:28:40,920
Speaker 2: of the through lack of a better word, kind of

544
00:28:41,000 --> 00:28:43,200
Speaker 2: structure that we have right now on Instagram or a

545
00:28:43,280 --> 00:28:46,800
Speaker 2: lot of people are their activism or what they're doing

546
00:28:46,840 --> 00:28:48,760
Speaker 2: to raise awareness is just like sharing a lot of

547
00:28:48,760 --> 00:28:50,480
Speaker 2: stuff on their story. And I do this too, and

548
00:28:50,520 --> 00:28:52,040
Speaker 2: like we've talked about that, like, I mean, we all

549
00:28:52,120 --> 00:28:53,280
Speaker 2: kind of do it, and I think there are a

550
00:28:53,320 --> 00:28:56,040
Speaker 2: lot of benefits and there is something about especially if

551
00:28:56,080 --> 00:28:58,120
Speaker 2: you're sharing like kind of going back to the other story,

552
00:28:58,160 --> 00:29:01,760
Speaker 2: like sharing stuff about you know, fund raising or miss

553
00:29:02,040 --> 00:29:04,760
Speaker 2: or or debunking misinformation or like there are there are

554
00:29:04,840 --> 00:29:06,000
Speaker 2: important stuff to be sharing.

555
00:29:06,080 --> 00:29:08,400
Speaker 4: I do that too. But that being said, I think

556
00:29:08,440 --> 00:29:10,120
Speaker 4: i've kind of I'm.

557
00:29:09,840 --> 00:29:12,880
Speaker 2: Realizing like I think this is like our our two

558
00:29:13,000 --> 00:29:16,120
Speaker 2: generations kind of version of this is like the spiral

559
00:29:16,160 --> 00:29:18,280
Speaker 2: of people just sharing a lot of stuff in their

560
00:29:18,320 --> 00:29:21,680
Speaker 2: story because it speaks to that and obviously it's something

561
00:29:21,680 --> 00:29:23,480
Speaker 2: that like speaks to them. But this is what makes

562
00:29:23,520 --> 00:29:25,800
Speaker 2: me nervous, is I feel like we are now seeing

563
00:29:25,880 --> 00:29:30,080
Speaker 2: with Facebook and kind of the like Boomer gen X generation,

564
00:29:30,360 --> 00:29:34,160
Speaker 2: like how that goes in a negative direction. I'm a

565
00:29:34,200 --> 00:29:36,560
Speaker 2: little bit worried that we're like setting us helps up

566
00:29:36,560 --> 00:29:38,240
Speaker 2: for a similar thing to happen.

567
00:29:39,880 --> 00:29:41,240
Speaker 4: But yeah, I agree, I do.

568
00:29:41,480 --> 00:29:44,720
Speaker 2: I think this is like it's just it's the gossip

569
00:29:45,040 --> 00:29:47,400
Speaker 2: like expanded on a on a world stage.

570
00:29:48,760 --> 00:29:54,640
Speaker 1: Yeah, we have to do an episode about the dynamics

571
00:29:54,640 --> 00:29:58,040
Speaker 1: of Instagram share Instagram being used as a place to

572
00:29:58,040 --> 00:30:02,680
Speaker 1: share information. What I always say is just remember anybody

573
00:30:02,720 --> 00:30:06,920
Speaker 1: can get a Cannavas subscription, like just because yeah, just

574
00:30:06,960 --> 00:30:09,520
Speaker 1: because I have a Canvas subscription, I have put together

575
00:30:09,560 --> 00:30:13,000
Speaker 1: a few Canada infographics in my day. I am I am,

576
00:30:13,160 --> 00:30:14,920
Speaker 1: I am, I am a cog in this system. I'm

577
00:30:14,920 --> 00:30:17,600
Speaker 1: calling myself out. But just remember, just because it's a

578
00:30:17,680 --> 00:30:21,560
Speaker 1: slick looking graphic with good colors and it's a carousel

579
00:30:21,600 --> 00:30:24,560
Speaker 1: on Instagram, anybody can get a canvas subscription. They they

580
00:30:24,600 --> 00:30:26,720
Speaker 1: don't like have to pass a test or anything. So

581
00:30:26,800 --> 00:30:28,880
Speaker 1: we should always remember that when we're when we're using

582
00:30:28,880 --> 00:30:31,440
Speaker 1: social media in general and deciding what to share.

583
00:30:31,520 --> 00:30:33,360
Speaker 4: Huh, yeah, definitely.

584
00:30:33,440 --> 00:30:35,960
Speaker 2: And I mean last week you did the story about

585
00:30:36,040 --> 00:30:38,360
Speaker 2: the the all eyes on rough image that there was

586
00:30:38,440 --> 00:30:41,280
Speaker 2: sort of so backlash that for that same reason, and

587
00:30:41,320 --> 00:30:42,600
Speaker 2: I kind of was in the same boat with you

588
00:30:42,640 --> 00:30:44,680
Speaker 2: where I had shared it originally and I kind of

589
00:30:45,040 --> 00:30:46,600
Speaker 2: was like, Okay, maybe not like that.

590
00:30:46,800 --> 00:30:49,040
Speaker 4: Actually, like I understand a lot of the reasons our people.

591
00:30:48,840 --> 00:30:51,520
Speaker 2: Are upset about this, but I think that was like

592
00:30:51,560 --> 00:30:53,560
Speaker 2: a perfect example where it was just like there was

593
00:30:53,560 --> 00:30:56,600
Speaker 2: so much like back and forth about like what we

594
00:30:56,600 --> 00:30:57,240
Speaker 2: should be doing.

595
00:30:57,120 --> 00:30:58,400
Speaker 4: And then it like to get out to the point

596
00:30:58,440 --> 00:30:59,520
Speaker 4: where I was kind of like, what even is the

597
00:30:59,560 --> 00:31:02,800
Speaker 4: point of this? It's like what even like I don't know.

598
00:31:02,920 --> 00:31:06,400
Speaker 2: I yeah, I one day I'll do a figure episode

599
00:31:06,400 --> 00:31:07,040
Speaker 2: about that.

600
00:31:07,360 --> 00:31:09,280
Speaker 1: Yeah, I kind of got it. Sounds like we had

601
00:31:09,320 --> 00:31:11,400
Speaker 1: a very similar trajectory with the all eyes on a

602
00:31:11,480 --> 00:31:14,280
Speaker 1: Ratha Ai generated an image we talked about in last

603
00:31:14,280 --> 00:31:16,640
Speaker 1: week's round up. But yeah, we're to the end of it.

604
00:31:16,680 --> 00:31:19,880
Speaker 1: I was like, what, like, like I had overthought it

605
00:31:19,920 --> 00:31:21,800
Speaker 1: to a point where I'm like, what even, Like why

606
00:31:21,840 --> 00:31:23,320
Speaker 1: did I even feel like I should share it? Like

607
00:31:23,360 --> 00:31:26,080
Speaker 1: it just like you, when you really pull apart the

608
00:31:26,160 --> 00:31:29,240
Speaker 1: reasonings on social media, you can get so granular where

609
00:31:29,280 --> 00:31:33,240
Speaker 1: the focus gets so lost, and you know, initially I'm like, oh, yeah,

610
00:31:33,240 --> 00:31:36,840
Speaker 1: if something comes across my stories about Palestine that speaks

611
00:31:36,840 --> 00:31:38,880
Speaker 1: to me and I feel like might speak to somebody else,

612
00:31:38,960 --> 00:31:41,440
Speaker 1: I'll share it. And then it's like but but with

613
00:31:41,480 --> 00:31:44,200
Speaker 1: that image, is like, well why why why did that?

614
00:31:44,240 --> 00:31:46,320
Speaker 1: What am I hoping to accomplish? What's the end goal?

615
00:31:46,440 --> 00:31:46,680
Speaker 4: Right?

616
00:31:47,040 --> 00:31:48,400
Speaker 2: I'm in the same boat where I kind of have

617
00:31:48,520 --> 00:31:50,240
Speaker 2: like stopped sharing a lot of stuff for it. Like

618
00:31:50,280 --> 00:31:51,640
Speaker 2: I think early on, I was kind of like I

619
00:31:51,680 --> 00:31:53,360
Speaker 2: felt like I should be sharing this stuff constantly because

620
00:31:53,400 --> 00:31:54,760
Speaker 2: I had a regional point where I kind of need

621
00:31:54,800 --> 00:31:57,280
Speaker 2: to step back and be like is this productive? Like

622
00:31:57,480 --> 00:32:01,120
Speaker 2: this is what I'm doing actually productive or because and again,

623
00:32:01,200 --> 00:32:03,440
Speaker 2: like this is a different situation obviously, like this this

624
00:32:03,520 --> 00:32:06,920
Speaker 2: story is about misinformation specifically, but like going back to

625
00:32:06,960 --> 00:32:11,360
Speaker 2: this story, a big point of misinformation and these kind

626
00:32:11,400 --> 00:32:14,200
Speaker 2: of campaigns is just kind of creating chaos and putting

627
00:32:14,200 --> 00:32:16,080
Speaker 2: a lot of information out there, so like you don't

628
00:32:16,120 --> 00:32:16,960
Speaker 2: even really know.

629
00:32:16,920 --> 00:32:18,000
Speaker 4: What's true anymore.

630
00:32:18,120 --> 00:32:21,160
Speaker 2: So even if it's something where I'm like, I'm sharing

631
00:32:21,240 --> 00:32:23,520
Speaker 2: images that are coming from trusted news sources or whatever

632
00:32:23,600 --> 00:32:29,280
Speaker 2: that I know are real, is there any benefit of me,

633
00:32:29,560 --> 00:32:31,720
Speaker 2: Like what is actually going to be beneficial is that

634
00:32:31,840 --> 00:32:33,800
Speaker 2: I'm not just posting stuff on my story to show

635
00:32:33,800 --> 00:32:37,760
Speaker 2: that I'm paying attention to show that I'm like staying

636
00:32:37,760 --> 00:32:40,120
Speaker 2: with it, or like is this actually meant to like

637
00:32:41,120 --> 00:32:43,760
Speaker 2: share resources or share something that I think is important

638
00:32:43,800 --> 00:32:46,360
Speaker 2: that people might not know otherwise, or is it just

639
00:32:46,440 --> 00:32:47,480
Speaker 2: kind of creating more noise.

640
00:32:48,000 --> 00:32:51,160
Speaker 1: Yeah, we we have to do a longer conversation about Yeah,

641
00:32:51,440 --> 00:32:52,480
Speaker 1: these are these are things that.

642
00:32:52,400 --> 00:32:53,880
Speaker 4: These are I have a lot of thoughts.

643
00:32:54,000 --> 00:32:57,920
Speaker 1: Yeah, I will say We're definitely gonna do.

644
00:33:03,240 --> 00:33:03,400
Speaker 3: More.

645
00:33:03,400 --> 00:33:17,320
Speaker 7: After a quick break, let's get right back into it.

646
00:33:19,240 --> 00:33:23,440
Speaker 1: So we have to talk about this. I guess I

647
00:33:23,480 --> 00:33:26,960
Speaker 1: guess I'll call it transphobic dating app la app. Have

648
00:33:27,080 --> 00:33:27,720
Speaker 1: you heard of this?

649
00:33:28,440 --> 00:33:31,920
Speaker 2: I have not, but I just see the lesbian dating app,

650
00:33:32,640 --> 00:33:34,600
Speaker 2: which I feel like we've tried.

651
00:33:34,360 --> 00:33:35,960
Speaker 4: To do this so many times.

652
00:33:36,800 --> 00:33:41,960
Speaker 1: Its yeah, strap in for this one. So app is

653
00:33:42,000 --> 00:33:45,320
Speaker 1: an app that bills itself as being for lesbians to

654
00:33:45,360 --> 00:33:49,360
Speaker 1: meet other lesbians, but with a twist. They're using facial

655
00:33:49,400 --> 00:33:53,080
Speaker 1: recognition technology in an attempt to keep trans women off

656
00:33:53,120 --> 00:33:57,000
Speaker 1: of the platform. Pink News reports that app has been

657
00:33:57,040 --> 00:34:00,560
Speaker 1: created by a gender the gender critical campaigner Jenny and

658
00:34:00,680 --> 00:34:04,280
Speaker 1: says it will scan a prospective users face via their smartphone,

659
00:34:04,560 --> 00:34:07,840
Speaker 1: allegedly being able to text if that person is CYS

660
00:34:07,960 --> 00:34:11,399
Speaker 1: or trans with ninety nine percent accuracy. First of all,

661
00:34:11,560 --> 00:34:13,640
Speaker 1: I have you. I'll say that's over and over and

662
00:34:13,640 --> 00:34:17,719
Speaker 1: over again. Complete bs. That's a lie, No way, no how,

663
00:34:18,520 --> 00:34:20,240
Speaker 1: I'll pause there. What are your thoughts?

664
00:34:21,400 --> 00:34:23,640
Speaker 4: Yeah?

665
00:34:23,920 --> 00:34:29,319
Speaker 2: Also, okay, I'm turfs are so fucking goofy. I'd like,

666
00:34:29,440 --> 00:34:32,640
Speaker 2: especially like lesbian and by women that are terfed because

667
00:34:33,120 --> 00:34:36,200
Speaker 2: I'm sorry, like this is my issue. All of the

668
00:34:36,360 --> 00:34:41,400
Speaker 2: obvious like things aside, like the lesbian community and like

669
00:34:41,440 --> 00:34:46,640
Speaker 2: the queer women in general, like even if you're not trans,

670
00:34:46,680 --> 00:34:49,280
Speaker 2: there's a lot of like different There's a whole history

671
00:34:49,320 --> 00:34:54,240
Speaker 2: of like different varying like gender expressions. I I'm somebody

672
00:34:54,239 --> 00:34:58,200
Speaker 2: who's like transvasculine, I present like I I kind of

673
00:34:58,200 --> 00:34:59,439
Speaker 2: look like a twelve year old boy.

674
00:34:59,600 --> 00:35:01,800
Speaker 4: I don't know, I've I've talked about.

675
00:35:01,600 --> 00:35:03,200
Speaker 2: This before, but it's always like the thing too where

676
00:35:03,239 --> 00:35:05,760
Speaker 2: I think like TERFs when whenever it's like the bathroom issue,

677
00:35:05,760 --> 00:35:09,359
Speaker 2: when they like like hyper fixing the bathroom issue. I

678
00:35:09,400 --> 00:35:13,240
Speaker 2: have gotten weird looks in public bathrooms before because people

679
00:35:13,280 --> 00:35:15,120
Speaker 2: see me, and especially if I'm wearing a mask, like

680
00:35:15,160 --> 00:35:17,680
Speaker 2: I have short hair, and I'm like, you know, i'm

681
00:35:17,680 --> 00:35:21,520
Speaker 2: our masculine presenting. I'm not like I'm pretty like introgynous looking,

682
00:35:21,560 --> 00:35:23,560
Speaker 2: where like people will give me weird looks and I'm like,

683
00:35:23,600 --> 00:35:26,239
Speaker 2: I know you think that I am trans in the

684
00:35:26,280 --> 00:35:28,880
Speaker 2: other direction, and that's why you're being weird to me.

685
00:35:29,200 --> 00:35:31,520
Speaker 6: But this just proves that your whole point is stupid

686
00:35:31,920 --> 00:35:32,840
Speaker 6: because under.

687
00:35:32,760 --> 00:35:36,080
Speaker 2: That logic, like I should be the like quote unquote

688
00:35:36,160 --> 00:35:40,279
Speaker 2: like women that you're trying to protect you right, and like,

689
00:35:41,239 --> 00:35:43,759
Speaker 2: oh my god, it's that.

690
00:35:43,840 --> 00:35:46,520
Speaker 4: Is so insane. I'm also yeah, the idea of being

691
00:35:46,520 --> 00:35:49,720
Speaker 4: able to like detect if somebody's trans based on like.

692
00:35:49,880 --> 00:35:54,239
Speaker 1: Bullshit not true, that's such bullshit, Like oh my gosh,

693
00:35:54,320 --> 00:35:57,400
Speaker 1: oh my god. It like I mean, I was when

694
00:35:57,400 --> 00:35:59,680
Speaker 1: I was thinking about this earlier, I was like, dang

695
00:36:00,000 --> 00:36:05,760
Speaker 1: transphobes and turfs, in service of their transphobia, they will

696
00:36:05,840 --> 00:36:10,040
Speaker 1: get taken by charlatan's. And now I'm like, oh, and

697
00:36:10,080 --> 00:36:13,719
Speaker 1: it's like tech charlatans who are purporting that technology can

698
00:36:13,760 --> 00:36:15,080
Speaker 1: be used in a way that it can't be and

699
00:36:15,120 --> 00:36:18,879
Speaker 1: like to your point about how like this kind of

700
00:36:18,920 --> 00:36:20,960
Speaker 1: being like, oh well, our technology can tell who's this

701
00:36:21,040 --> 00:36:23,759
Speaker 1: or who's that. The scholar Alejandra Carrabello made a great

702
00:36:23,760 --> 00:36:27,040
Speaker 1: point on thread saying that basically, you are trusting technology

703
00:36:27,040 --> 00:36:29,480
Speaker 1: that we already know is falty to say that Kim

704
00:36:29,600 --> 00:36:32,560
Speaker 1: Petras isn't a woman, or that Late Ashley isn't a

705
00:36:32,600 --> 00:36:35,440
Speaker 1: man based on a photo. There is no machine learning

706
00:36:35,480 --> 00:36:39,920
Speaker 1: model in existence that could do this consistently and accurately

707
00:36:39,960 --> 00:36:44,480
Speaker 1: based on a photo. Like people like, it doesn't work

708
00:36:44,480 --> 00:36:47,080
Speaker 1: that way. People are complex, and it's wild to me

709
00:36:47,120 --> 00:36:49,600
Speaker 1: that people would be making the claim that facial recognition

710
00:36:49,640 --> 00:36:52,720
Speaker 1: technology can do this. It absolutely cannot. I cannot stress

711
00:36:52,760 --> 00:36:55,600
Speaker 1: this enough. And again there are so many reasons why

712
00:36:55,600 --> 00:36:59,319
Speaker 1: this is a stupid, terrible idea transphobia aside, Like we know,

713
00:36:59,560 --> 00:37:03,880
Speaker 1: fatial cognition technology is really faulty. It routinely misidentifies black

714
00:37:03,920 --> 00:37:07,600
Speaker 1: CIS women as men. Shout out to doctor Joe Bolomwuini

715
00:37:07,680 --> 00:37:10,880
Speaker 1: for her gender shades work about this, and yeah, it's

716
00:37:10,920 --> 00:37:16,040
Speaker 1: like so if you actually use this technology, you would

717
00:37:16,080 --> 00:37:22,160
Speaker 1: definitely be casting out the very people that you say

718
00:37:22,160 --> 00:37:24,120
Speaker 1: are the demographic that you want. Like it doesn't make

719
00:37:24,160 --> 00:37:24,800
Speaker 1: any sense.

720
00:37:25,120 --> 00:37:28,399
Speaker 2: Yeah, and like you said with like there is a

721
00:37:28,440 --> 00:37:32,360
Speaker 2: whole history of like how this is tied to racism

722
00:37:32,440 --> 00:37:35,120
Speaker 2: and how this is tied to a lot of like

723
00:37:35,120 --> 00:37:38,320
Speaker 2: like this feels like we're trying to do eugenics again,

724
00:37:38,600 --> 00:37:40,360
Speaker 2: like just in a different form. But like it is

725
00:37:40,440 --> 00:37:45,960
Speaker 2: so weird, and yeah, like gender, you know when people

726
00:37:46,040 --> 00:37:49,000
Speaker 2: say gender is the performance, gender is like you know,

727
00:37:49,080 --> 00:37:51,200
Speaker 2: all the fucking Jude Butler's.

728
00:37:50,760 --> 00:37:53,200
Speaker 4: Work is all about this, Like it very much is

729
00:37:53,239 --> 00:37:58,520
Speaker 4: like basically yeah, like you're able to. This is the

730
00:37:58,560 --> 00:37:59,880
Speaker 4: whole point of drags.

731
00:38:00,120 --> 00:38:02,080
Speaker 2: You can you can mess with gender, you can you

732
00:38:02,120 --> 00:38:06,160
Speaker 2: can present different ways and it, I god, this is.

733
00:38:06,080 --> 00:38:07,120
Speaker 1: Just such a weird.

734
00:38:07,880 --> 00:38:10,480
Speaker 4: This is just such a shit show, like what the hell.

735
00:38:10,560 --> 00:38:14,319
Speaker 1: It really is, and like, yeah, I mean there are

736
00:38:14,360 --> 00:38:17,279
Speaker 1: days like my on my own gender journey. There are

737
00:38:17,400 --> 00:38:22,080
Speaker 1: days where I am like rocking straight androgyny, and there

738
00:38:22,120 --> 00:38:23,959
Speaker 1: are days where I'm like, oh, I'm gonna like them

739
00:38:24,040 --> 00:38:26,120
Speaker 1: up because I want to or need to for some reason,

740
00:38:26,239 --> 00:38:29,200
Speaker 1: Like the idea that a machine learning app would be

741
00:38:29,200 --> 00:38:32,120
Speaker 1: able to zoom in on the particulars of that is.

742
00:38:32,560 --> 00:38:35,320
Speaker 1: It's just it's just not how it works. And I

743
00:38:35,320 --> 00:38:37,439
Speaker 1: think your point is a good one about how it's

744
00:38:37,480 --> 00:38:40,280
Speaker 1: like it's a good reminder that all this stuff is connected,

745
00:38:40,320 --> 00:38:43,239
Speaker 1: that their transphobia is also anti blackness. It's like it's

746
00:38:43,239 --> 00:38:45,440
Speaker 1: all it's like one big bag of awful.

747
00:38:46,160 --> 00:38:48,279
Speaker 4: Yeah yeah, yeah, And I the same.

748
00:38:48,320 --> 00:38:51,399
Speaker 2: I mean, like I I've done a total of one

749
00:38:51,520 --> 00:38:52,680
Speaker 2: drag performance in my life.

750
00:38:52,719 --> 00:38:53,839
Speaker 4: I would love to do more, but.

751
00:38:53,840 --> 00:39:00,200
Speaker 2: I but I did, like you know, I'm drag was

752
00:39:00,239 --> 00:39:01,680
Speaker 2: like wearing a wing and I did all my makeup

753
00:39:01,719 --> 00:39:04,440
Speaker 2: and stuff, and I like my friends did not recognize me, like.

754
00:39:04,440 --> 00:39:07,160
Speaker 4: I was a completely different person. And then also like

755
00:39:07,160 --> 00:39:08,719
Speaker 4: when I went out and was like walking.

756
00:39:08,400 --> 00:39:10,279
Speaker 2: Down the street, like people just assumed I was like

757
00:39:10,320 --> 00:39:12,480
Speaker 2: a super fam like sis.

758
00:39:12,200 --> 00:39:16,600
Speaker 4: Woman, Like it's all, yeah, it's all very superficial.

759
00:39:17,480 --> 00:39:19,719
Speaker 1: Yeah, And I did want to keep it really real

760
00:39:19,760 --> 00:39:22,840
Speaker 1: for a second, which is that I believe just having

761
00:39:23,600 --> 00:39:26,080
Speaker 1: just being a person, a woman, a SIS woman, who

762
00:39:26,120 --> 00:39:29,600
Speaker 1: is out there on who's been on a few apps, whatever, whatever,

763
00:39:30,280 --> 00:39:33,320
Speaker 1: the real threat to women on a lesbian dating app

764
00:39:33,640 --> 00:39:37,800
Speaker 1: is not from trans people. It is SIS men looking

765
00:39:37,800 --> 00:39:39,600
Speaker 1: to set up threesomes. I think, like, if you were

766
00:39:39,600 --> 00:39:42,400
Speaker 1: gonna fall out one section of like, well, that's the

767
00:39:42,480 --> 00:39:44,160
Speaker 1: real problem. It's gotta be them.

768
00:39:44,239 --> 00:39:45,680
Speaker 4: Nope, Oh god, Okay.

769
00:39:45,800 --> 00:39:48,560
Speaker 2: So I know in the past I have been defending

770
00:39:48,960 --> 00:39:51,440
Speaker 2: dating apps in general on this podcast.

771
00:39:51,480 --> 00:39:52,400
Speaker 4: I will say I've.

772
00:39:52,280 --> 00:39:56,680
Speaker 2: Had some really weird hinge messages over the last couple

773
00:39:56,719 --> 00:39:59,160
Speaker 2: of months. But I need to do an episode where

774
00:39:59,160 --> 00:40:01,480
Speaker 2: I just read through all the weird shit that oh

775
00:40:01,800 --> 00:40:04,440
Speaker 2: yes please because OsO Okay, So I get like, as

776
00:40:04,480 --> 00:40:07,279
Speaker 2: I mentioned, I'm trans, like I I do not date

777
00:40:07,320 --> 00:40:07,879
Speaker 2: straight men.

778
00:40:08,600 --> 00:40:09,160
Speaker 4: I date men.

779
00:40:09,239 --> 00:40:11,560
Speaker 2: I'm bisexual like I date people of all genders, but

780
00:40:11,560 --> 00:40:13,080
Speaker 2: I don't date straight people period.

781
00:40:13,200 --> 00:40:15,560
Speaker 4: I have that in my hinge bio. Some of the

782
00:40:15,600 --> 00:40:16,520
Speaker 4: weirdest like I.

783
00:40:16,800 --> 00:40:18,560
Speaker 2: Had a man I'm both the most recent one that

784
00:40:18,600 --> 00:40:20,880
Speaker 2: I cannot stop thinking about when somebody messaged me. This

785
00:40:20,920 --> 00:40:23,560
Speaker 2: man messaged me and was like, well, am I am

786
00:40:23,600 --> 00:40:25,719
Speaker 2: I straight? If I like being the little spoon And.

787
00:40:25,640 --> 00:40:26,279
Speaker 1: I was like what.

788
00:40:30,160 --> 00:40:33,720
Speaker 2: Oh, and that's like one of the milder ones.

789
00:40:33,880 --> 00:40:34,480
Speaker 4: I know.

790
00:40:34,680 --> 00:40:37,080
Speaker 2: One day I like, literally, I've just started saving them

791
00:40:37,120 --> 00:40:38,799
Speaker 2: because I like, I was like, I had a moment

792
00:40:38,800 --> 00:40:40,359
Speaker 2: where I was like, maybe this is unethical, but then

793
00:40:40,360 --> 00:40:41,200
Speaker 2: I was like, these men.

794
00:40:41,080 --> 00:40:46,439
Speaker 4: Are sending me these send this prompted like what is up?

795
00:40:46,560 --> 00:40:48,680
Speaker 4: Or like I'll get a lot of the like well

796
00:40:48,719 --> 00:40:50,680
Speaker 4: maybe I'm not straight because I think you're hot.

797
00:40:50,800 --> 00:40:53,759
Speaker 1: I'm like, yeah, let's.

798
00:40:53,680 --> 00:40:57,920
Speaker 4: Unpack that, like what the hell? Yeah, anyways, I agree

799
00:40:57,960 --> 00:41:01,960
Speaker 4: with that sentiment. The the the problem is sis straight men.

800
00:41:02,000 --> 00:41:03,080
Speaker 4: The problem is sis.

801
00:41:02,800 --> 00:41:07,280
Speaker 2: Men looking for the suns, looking to be creepy, looking

802
00:41:07,280 --> 00:41:10,560
Speaker 2: to be weird.

803
00:41:11,200 --> 00:41:12,480
Speaker 4: This should be common sense.

804
00:41:12,560 --> 00:41:17,239
Speaker 1: But you know it's them, them, they're the problem. There's

805
00:41:17,360 --> 00:41:19,040
Speaker 1: them straight men.

806
00:41:20,320 --> 00:41:22,520
Speaker 4: Yeah, oh my god, speaking.

807
00:41:22,160 --> 00:41:24,759
Speaker 1: Of straight men being problematic, I guess I don't know

808
00:41:24,800 --> 00:41:27,600
Speaker 1: that this next story only involves straight men, but you know,

809
00:41:27,640 --> 00:41:29,720
Speaker 1: I'm gonna take a leap and say that there's probably

810
00:41:29,719 --> 00:41:34,439
Speaker 1: a lot of them all. Yeah, historically and historically right,

811
00:41:34,680 --> 00:41:40,080
Speaker 1: So that is this horrible landlord scheme. So we got

812
00:41:40,080 --> 00:41:43,400
Speaker 1: to talk about Real Page, which is the quote property

813
00:41:43,560 --> 00:41:47,200
Speaker 1: management software company that has been accused of helping landlords

814
00:41:47,280 --> 00:41:51,520
Speaker 1: orchestrate a price fixing scam among large corporate landlords by

815
00:41:51,560 --> 00:41:55,839
Speaker 1: algorithmically predicting the highest possible rents that landlords can get

816
00:41:55,840 --> 00:41:59,880
Speaker 1: away with charging tenants. So these people are like scum

817
00:42:00,040 --> 00:42:02,960
Speaker 1: bags of the highest order. Even reading this story, I

818
00:42:03,040 --> 00:42:07,040
Speaker 1: was like, these people are not good people. Listen to

819
00:42:07,080 --> 00:42:09,879
Speaker 1: how pro Publica reported on how they talked about their

820
00:42:09,920 --> 00:42:13,879
Speaker 1: rent hiking technology back in October during a convention. Never

821
00:42:13,960 --> 00:42:17,000
Speaker 1: before have we seen these numbers, said Jay Parsons, a

822
00:42:17,080 --> 00:42:20,040
Speaker 1: vice president of Real Page, as convention goers wandered by

823
00:42:20,640 --> 00:42:23,200
Speaker 1: apartment rents had recently shot up by as much as

824
00:42:23,280 --> 00:42:26,600
Speaker 1: fourteen point five percent, he said in a video touting

825
00:42:26,600 --> 00:42:30,400
Speaker 1: the company's services. Turning to his colleague, Parsons asked, what

826
00:42:30,600 --> 00:42:33,360
Speaker 1: role has a software played. I think it's driving it,

827
00:42:33,480 --> 00:42:37,359
Speaker 1: quite honestly, answered Andrew Bowen, another Real Page executive. As

828
00:42:37,360 --> 00:42:40,080
Speaker 1: a property manager, very few of us would be willing

829
00:42:40,120 --> 00:42:43,160
Speaker 1: to actually raise rents double digits within a single month

830
00:42:43,200 --> 00:42:46,319
Speaker 1: by doing it manually. So they're basically just like bragging, like,

831
00:42:46,360 --> 00:42:49,520
Speaker 1: oh yeah, we raised this. This technology helps you raise

832
00:42:49,560 --> 00:42:52,000
Speaker 1: rents and ways you never thought. You didn't think that

833
00:42:52,000 --> 00:42:56,439
Speaker 1: that family with small children and a single mom would

834
00:42:56,480 --> 00:42:59,000
Speaker 1: pay this much. You might be wrong. You should raise

835
00:42:59,000 --> 00:43:00,080
Speaker 1: their rent and see.

836
00:43:00,200 --> 00:43:06,880
Speaker 4: Oh my god, just what the hell it's bad.

837
00:43:07,520 --> 00:43:10,560
Speaker 1: So, according to biz Now, Real Pages System, which provides

838
00:43:10,600 --> 00:43:13,719
Speaker 1: rental price recommendations based on real time data from landlords,

839
00:43:13,960 --> 00:43:16,279
Speaker 1: is alleged to be a key tool in manipulating the

840
00:43:16,320 --> 00:43:20,319
Speaker 1: rental market. The firm's influence covers seventy percent of multifamily

841
00:43:20,360 --> 00:43:24,839
Speaker 1: apartment buildings and unpacks sixteen million units nationwide. So this

842
00:43:24,920 --> 00:43:28,680
Speaker 1: is very much like a coordinated scheme. Landlords are encouraged

843
00:43:28,680 --> 00:43:31,480
Speaker 1: to adopt Real Pages price and recommendations, a practice that

844
00:43:31,520 --> 00:43:34,719
Speaker 1: they follow eighty to ninety percent of the time. This

845
00:43:34,840 --> 00:43:38,880
Speaker 1: coordinated approach reduces the availability of rental units, driving up prices.

846
00:43:39,239 --> 00:43:41,960
Speaker 1: One of the architects of Real Pages System reportedly stated

847
00:43:42,000 --> 00:43:46,040
Speaker 1: that the aim is to prevent landlords from undervaluing their properties,

848
00:43:46,320 --> 00:43:48,760
Speaker 1: ensuring consistently higher rates across the board.

849
00:43:49,120 --> 00:43:52,319
Speaker 2: Yeah, that's the real problem here, it's landlords under yeah,

850
00:43:52,360 --> 00:43:53,520
Speaker 2: doing their property.

851
00:43:53,680 --> 00:43:57,759
Speaker 1: Oh my god, the poor landlords. Won't someone think about

852
00:43:57,800 --> 00:44:02,239
Speaker 1: the land properties that have been under So if the

853
00:44:02,280 --> 00:44:05,400
Speaker 1: majority of landlords in a single area are all using

854
00:44:05,520 --> 00:44:08,520
Speaker 1: this this pricing tool, it kind of feels like a

855
00:44:08,560 --> 00:44:12,000
Speaker 1: coordinated price fixing scam, right, which you would think should

856
00:44:12,000 --> 00:44:15,640
Speaker 1: be illegal. Right. Well, last week, the FBI did a

857
00:44:15,680 --> 00:44:19,840
Speaker 1: surprise raid on the Atlanta headquarters of Courtland Management, marking

858
00:44:19,840 --> 00:44:22,960
Speaker 1: a significant escalation and a criminal anti trust investigation of

859
00:44:23,040 --> 00:44:26,680
Speaker 1: an Apartment of Justice into allegations of a nationwide conspiracy

860
00:44:26,880 --> 00:44:30,960
Speaker 1: to artificially inflate apartment rents. The implications of this probe

861
00:44:30,960 --> 00:44:35,359
Speaker 1: are far reaching, potentially affecting millions of renters across the US.

862
00:44:35,800 --> 00:44:40,920
Speaker 2: Yeah, surprising, Like you know, moment where I'm supporting the

863
00:44:41,000 --> 00:44:45,680
Speaker 2: FBI here because this is a real I live in

864
00:44:45,680 --> 00:44:50,080
Speaker 2: New York. Like I sometimes if I'm feeling particularly you know,

865
00:44:51,360 --> 00:44:53,880
Speaker 2: like getting in a depressive spiral about the state of

866
00:44:53,920 --> 00:44:55,600
Speaker 2: the world, I'll just think about the fact that it's

867
00:44:55,640 --> 00:44:59,160
Speaker 2: like it is so weird, how it's.

868
00:44:59,040 --> 00:45:00,560
Speaker 4: Just completely NORMALI is that.

869
00:45:02,920 --> 00:45:05,680
Speaker 2: I, Like, I don't know anybody who isn't constantly in

870
00:45:05,719 --> 00:45:09,759
Speaker 2: like a state of just like not stable, like not

871
00:45:09,800 --> 00:45:13,160
Speaker 2: having super stable housing because you don't know when your

872
00:45:13,160 --> 00:45:16,359
Speaker 2: rent's gonna go up, or if like it's already so

873
00:45:16,400 --> 00:45:19,520
Speaker 2: insanely expensive to live here, and then they're constantly raising

874
00:45:19,600 --> 00:45:22,320
Speaker 2: prices and all of the buildings are kind.

875
00:45:22,160 --> 00:45:23,920
Speaker 4: Of also like awful too.

876
00:45:24,080 --> 00:45:26,640
Speaker 2: Like the building that I live in has like a

877
00:45:26,640 --> 00:45:29,600
Speaker 2: billion fucking problems that they like never respond to, and

878
00:45:29,640 --> 00:45:32,400
Speaker 2: they're still like trying to raise rent for the next

879
00:45:32,640 --> 00:45:35,560
Speaker 2: like you know cycle of our our lease and all

880
00:45:35,560 --> 00:45:37,799
Speaker 2: of that, and I you know, it's it is a

881
00:45:37,880 --> 00:45:40,400
Speaker 2: huge problem and it's not something that should be normalized,

882
00:45:40,400 --> 00:45:42,320
Speaker 2: and it's not something that should just be like accepted

883
00:45:42,320 --> 00:45:44,160
Speaker 2: as like, oh it is the way it is like that,

884
00:45:44,320 --> 00:45:48,279
Speaker 2: like people should be able to live and exist and

885
00:45:49,000 --> 00:45:52,279
Speaker 2: you know, have the shelter because that's an important part

886
00:45:52,320 --> 00:45:53,200
Speaker 2: of life.

887
00:45:53,800 --> 00:45:57,040
Speaker 1: But yeah, yeah, and you're right, like it really is

888
00:45:57,239 --> 00:46:01,000
Speaker 1: deeply impacting renters. In Atlanta, for instance, this raid happened.

889
00:46:01,440 --> 00:46:05,760
Speaker 1: Software driven pricing affects eighty one percent of multifamily rental units.

890
00:46:06,000 --> 00:46:09,640
Speaker 1: Biznow reports that since twenty sixteen, rencident Atlanta have surged

891
00:46:09,680 --> 00:46:13,320
Speaker 1: by eighty percent, despite rising vacancy rates that would typically

892
00:46:13,320 --> 00:46:16,640
Speaker 1: result in lower rents. The widespread adoption of real pages

893
00:46:16,680 --> 00:46:20,480
Speaker 1: pricing recommendations by several landlords between twenty fifteen and twenty seventeen,

894
00:46:20,719 --> 00:46:23,799
Speaker 1: followed by Real Pages acquisition of its main rival lease

895
00:46:23,880 --> 00:46:27,480
Speaker 1: rent Option in twenty seventeen, has given the company unprecedented

896
00:46:27,480 --> 00:46:31,000
Speaker 1: control over rental pricing. So I'm with you, like, this

897
00:46:31,040 --> 00:46:32,440
Speaker 1: is a bit of a weird stance for me, but

898
00:46:32,520 --> 00:46:35,000
Speaker 1: I was like, I'm glad they got raided. I'm glad

899
00:46:35,040 --> 00:46:37,400
Speaker 1: it was a surprise raid. I hope it was, Like

900
00:46:38,440 --> 00:46:40,480
Speaker 1: you know, I would have loved to have been doing

901
00:46:40,480 --> 00:46:42,480
Speaker 1: a ride along and like wearing one of the FBI

902
00:46:42,600 --> 00:46:46,360
Speaker 1: jackets and like aviator glasses, kicking indoors and like bust

903
00:46:46,360 --> 00:46:50,080
Speaker 1: in the heads of these guys. Like I just think

904
00:46:50,120 --> 00:46:53,200
Speaker 1: these people are scumbags. And I hate that when we

905
00:46:53,239 --> 00:46:56,080
Speaker 1: think about technology that this is the use case that

906
00:46:56,200 --> 00:46:57,960
Speaker 1: a lot of people will go for, Like how can

907
00:46:58,000 --> 00:47:01,240
Speaker 1: we use it to extract and squeeze and exploit people?

908
00:47:01,400 --> 00:47:04,560
Speaker 1: And I hate it so like the algorithmic pricing is

909
00:47:04,600 --> 00:47:07,600
Speaker 1: a racket that prays on us renters. And yeah, I

910
00:47:07,680 --> 00:47:11,120
Speaker 1: just think that when we think about crime and the

911
00:47:11,160 --> 00:47:15,200
Speaker 1: way that crime and technology intersect, I don't like the

912
00:47:15,280 --> 00:47:20,680
Speaker 1: idea of bad facial recognition being being used to misidentify shoplifters.

913
00:47:21,239 --> 00:47:24,160
Speaker 1: This is the kind of tech enabled crime that is

914
00:47:24,200 --> 00:47:27,799
Speaker 1: impacting more people, right, Like we should be talking about this,

915
00:47:28,280 --> 00:47:32,560
Speaker 1: This should be big news. Yeah, I'm sure somebody listening

916
00:47:32,719 --> 00:47:36,720
Speaker 1: is like, wow, bridget Todd show for the FBI. Who knew?

917
00:47:38,840 --> 00:47:39,560
Speaker 4: I got it.

918
00:47:39,480 --> 00:47:41,560
Speaker 2: It's like one of the the It's the like one

919
00:47:41,600 --> 00:47:43,040
Speaker 2: in a million times where it's like, oh my god,

920
00:47:43,080 --> 00:47:46,680
Speaker 2: the justice system working the way it's supposed to. We'll see,

921
00:47:46,760 --> 00:47:50,520
Speaker 2: We'll feel like, we'll see, we'll see. I guess it's

922
00:47:50,560 --> 00:47:52,640
Speaker 2: like the like the TV show version of the FBI

923
00:47:52,719 --> 00:47:56,680
Speaker 2: where you're like, wow, yeah, they're I don't know, I

924
00:47:56,719 --> 00:48:01,359
Speaker 2: feel the same like cautious cautious is to the f Yeah,

925
00:48:01,360 --> 00:48:04,279
Speaker 2: I guess in this case. But yeah, once again, the

926
00:48:04,320 --> 00:48:11,759
Speaker 2: problem is capitalism. How how this technology is used, How

927
00:48:11,800 --> 00:48:15,560
Speaker 2: this technology is used not to make our lives easier

928
00:48:15,600 --> 00:48:20,000
Speaker 2: but just to help, you know, select few people.

929
00:48:19,719 --> 00:48:22,879
Speaker 4: Make a little bit more money from from the rest

930
00:48:22,920 --> 00:48:23,920
Speaker 4: of our misery.

931
00:48:24,760 --> 00:48:28,160
Speaker 1: Exactly. Okay, so my last question for you is are

932
00:48:28,239 --> 00:48:30,840
Speaker 1: you familiar with the K pop group New Genes.

933
00:48:31,760 --> 00:48:33,600
Speaker 4: I think I've just heard of that.

934
00:48:33,640 --> 00:48:36,080
Speaker 2: I'm not a big K pop listener, but like I feel, like,

935
00:48:36,160 --> 00:48:40,360
Speaker 2: you know, being on the internet, it comes into my

936
00:48:40,440 --> 00:48:44,239
Speaker 2: feed often, so I think I like, just heard of

937
00:48:44,239 --> 00:48:47,520
Speaker 2: this group from like some posts somewhere, but I'm not

938
00:48:47,560 --> 00:48:48,280
Speaker 2: super familiar.

939
00:48:48,440 --> 00:48:50,080
Speaker 1: Okay, I had never heard of them, and I was

940
00:48:50,120 --> 00:48:52,279
Speaker 1: asking my friend who does like K pop, like, oh,

941
00:48:52,320 --> 00:48:53,680
Speaker 1: do you know about New Genes? And she was like,

942
00:48:53,960 --> 00:48:57,040
Speaker 1: they are literally one of the biggest groups in the world.

943
00:48:57,120 --> 00:48:58,680
Speaker 1: That's like being like, oh, have you heard of this group?

944
00:48:58,719 --> 00:49:03,480
Speaker 1: The Beatles? Like It's like, oh, I had no idea.

945
00:49:03,560 --> 00:49:07,640
Speaker 1: So Earlier this year, the Korean pop apparently supergroup New

946
00:49:07,719 --> 00:49:11,360
Speaker 1: Genes was asking for help and identifying an anonymous YouTuber

947
00:49:11,440 --> 00:49:13,840
Speaker 1: to sue them for defamation, and this week for or

948
00:49:13,920 --> 00:49:17,480
Speaker 1: For Media reported that a US federal court actually allowed

949
00:49:17,560 --> 00:49:20,440
Speaker 1: the group to subpoenaed that information from Google about a

950
00:49:20,480 --> 00:49:23,680
Speaker 1: YouTube account so they can sue that person for defamation

951
00:49:23,920 --> 00:49:27,120
Speaker 1: under South Korean law. The group alleges that this account,

952
00:49:27,200 --> 00:49:30,399
Speaker 1: which according to court documents, posts under the handle at

953
00:49:30,480 --> 00:49:33,879
Speaker 1: middle seven, has posted as many as thirty three defamatory

954
00:49:33,960 --> 00:49:36,640
Speaker 1: videos that have been viewed nearly fourteen million times as

955
00:49:36,640 --> 00:49:39,880
Speaker 1: of their filing. Because the account is anonymous, the lawsuit

956
00:49:39,920 --> 00:49:42,960
Speaker 1: cannot continue until that user has been identified. Some of

957
00:49:42,960 --> 00:49:45,960
Speaker 1: the statements that New Genes are saying are defamatory include

958
00:49:46,480 --> 00:49:49,200
Speaker 1: calling one of the members of the group the eldest

959
00:49:49,280 --> 00:49:52,880
Speaker 1: daughter of a Vietnamese farmer, and a video titled reasons

960
00:49:52,920 --> 00:49:55,360
Speaker 1: why New Genes is a crap group.

961
00:49:56,040 --> 00:50:00,120
Speaker 4: I mean, it is that defavatory or is that just?

962
00:50:01,200 --> 00:50:04,920
Speaker 2: I mean, I mean, if that's the bavatory, Harry Styles

963
00:50:05,000 --> 00:50:07,759
Speaker 2: might be sending me a cease and desist sometime soon.

964
00:50:09,480 --> 00:50:10,600
Speaker 4: That's why I wanted.

965
00:50:10,320 --> 00:50:12,520
Speaker 1: To ask about this. So, like, I'm no lawyer. What

966
00:50:12,560 --> 00:50:14,000
Speaker 1: do I know if you're If you're a lawyer and

967
00:50:14,000 --> 00:50:16,759
Speaker 1: you have insight into this, uh, let me know. But

968
00:50:18,160 --> 00:50:20,799
Speaker 1: I was so curious, Like so this is not the

969
00:50:20,800 --> 00:50:22,720
Speaker 1: first case of this nature, and that is like why

970
00:50:22,960 --> 00:50:26,799
Speaker 1: Google was ordered to actually provide the name and birth

971
00:50:26,880 --> 00:50:30,160
Speaker 1: date and address and email address of the person on

972
00:50:30,200 --> 00:50:32,120
Speaker 1: YouTube who was doing this, because it's like a there's

973
00:50:32,120 --> 00:50:36,080
Speaker 1: already established legal precedent. However, I can't help but wonder,

974
00:50:36,160 --> 00:50:40,680
Speaker 1: like what does this mean for haters like me, like

975
00:50:40,680 --> 00:50:42,200
Speaker 1: people who just like I want to make a long

976
00:50:42,239 --> 00:50:45,080
Speaker 1: YouTube video about how much I hate Harry Styles or whatever,

977
00:50:45,239 --> 00:50:47,719
Speaker 1: like like what does that? What does that mean for

978
00:50:47,760 --> 00:50:48,839
Speaker 1: those kind of people? You know?

979
00:50:49,760 --> 00:50:53,640
Speaker 4: Yeah, yeah, I And I mean I don't know.

980
00:50:53,680 --> 00:50:55,160
Speaker 2: I guess I would need to look a little bit

981
00:50:55,200 --> 00:50:57,640
Speaker 2: more into this case and like what specific claims are

982
00:50:57,719 --> 00:51:00,520
Speaker 2: being made. But if like like those two up front,

983
00:51:00,680 --> 00:51:06,800
Speaker 2: a like the eldest daughter of a Vietnamese father, I

984
00:51:06,840 --> 00:51:08,879
Speaker 2: don't know, maybe that's not true, but also that doesn't

985
00:51:08,920 --> 00:51:11,799
Speaker 2: really seem like it's like defamatory, like I don't know.

986
00:51:12,520 --> 00:51:15,920
Speaker 2: And then also yeah, the whole like why it's like

987
00:51:16,160 --> 00:51:19,680
Speaker 2: if it's an opinion. I am also a bridge and

988
00:51:19,719 --> 00:51:20,520
Speaker 2: I'm in the same court.

989
00:51:20,640 --> 00:51:22,239
Speaker 4: I love being a hater.

990
00:51:22,480 --> 00:51:27,680
Speaker 2: I like I if they make being a hater illegal

991
00:51:27,719 --> 00:51:28,560
Speaker 2: on the internet, like.

992
00:51:28,520 --> 00:51:29,480
Speaker 4: I don't know what I'm gonna do.

993
00:51:29,719 --> 00:51:37,760
Speaker 1: I know, I'm so curious how it shakes out. M Well, Joey,

994
00:51:37,880 --> 00:51:39,799
Speaker 1: thank you so much for being here. Where can folks

995
00:51:39,840 --> 00:51:40,799
Speaker 1: keep in touch with what you're up to?

996
00:51:41,040 --> 00:51:47,880
Speaker 2: Of course you can find me on Twitter and Instagram

997
00:51:47,920 --> 00:51:50,879
Speaker 2: at Pat not Pratt p A T T n OT

998
00:51:51,200 --> 00:51:54,239
Speaker 2: p r A T T. Also, same day that this

999
00:51:54,280 --> 00:51:59,200
Speaker 2: is coming out, I have an episode of stuff Mom

1000
00:51:59,320 --> 00:52:02,680
Speaker 2: never told you, an Honor of Pride month, talking about

1001
00:52:02,680 --> 00:52:08,319
Speaker 2: the movie Bottoms. So you want to hear me talk

1002
00:52:08,360 --> 00:52:12,520
Speaker 2: about Honestly, a lot of it is me talking about

1003
00:52:12,520 --> 00:52:15,640
Speaker 2: other queer high school movies that I don't like but

1004
00:52:15,719 --> 00:52:18,680
Speaker 2: if you want to hear me talk about bottoms.

1005
00:52:18,200 --> 00:52:22,480
Speaker 4: And all of that. Yeah, the movie.

1006
00:52:24,160 --> 00:52:26,759
Speaker 1: Check it out, not the embody part, the body part.

1007
00:52:26,960 --> 00:52:30,359
Speaker 4: You know. You can check them out on stuff Mom

1008
00:52:30,480 --> 00:52:31,080
Speaker 4: Never told you.

1009
00:52:31,239 --> 00:52:35,520
Speaker 2: Also, also for Pride Month, there's a new show called

1010
00:52:36,160 --> 00:52:38,720
Speaker 2: but We Loved that's from the Outspoken Network.

1011
00:52:38,760 --> 00:52:40,640
Speaker 4: I would it's all about.

1012
00:52:40,440 --> 00:52:43,400
Speaker 2: Queer history told through kind of a kind of personal

1013
00:52:43,640 --> 00:52:44,680
Speaker 2: personal storytelling.

1014
00:52:44,960 --> 00:52:47,200
Speaker 4: I've been working on that as well. To go ahead

1015
00:52:47,200 --> 00:52:49,359
Speaker 4: and check that out. If that is of interest to.

1016
00:52:49,320 --> 00:52:52,600
Speaker 1: You, definitely check that out. We'll put the links in

1017
00:52:52,640 --> 00:52:54,759
Speaker 1: the show notes to all of this. Thanks so much

1018
00:52:54,800 --> 00:52:56,480
Speaker 1: for being here, Joey, and thanks to all of you

1019
00:52:56,560 --> 00:53:04,000
Speaker 1: for listening. I will see you on the Internet. If

1020
00:53:04,000 --> 00:53:06,120
Speaker 1: you're looking for ways to support the show, check out

1021
00:53:06,120 --> 00:53:10,000
Speaker 1: our merch store at tangody dot com slash store. Got

1022
00:53:10,000 --> 00:53:12,000
Speaker 1: a story about an interesting thing in tech, or just

1023
00:53:12,000 --> 00:53:14,000
Speaker 1: want to say hi, You can read us at Hello

1024
00:53:14,040 --> 00:53:16,560
Speaker 1: at tangody dot com. You can also find transcripts for

1025
00:53:16,600 --> 00:53:17,560
Speaker 1: today's episode.

1026
00:53:17,239 --> 00:53:18,560
Speaker 3: At tengody dot com.

1027
00:53:18,680 --> 00:53:20,319
Speaker 1: There Are No Girls on the Internet was created by

1028
00:53:20,360 --> 00:53:23,720
Speaker 1: me bridget Toid. It's a production of iHeartRadio and Unbossed Creative,

1029
00:53:24,200 --> 00:53:27,840
Speaker 1: edited by Joey Pat Jonathan Strickland as our executive producer.

1030
00:53:28,120 --> 00:53:31,400
Speaker 1: Tari Harrison is our producer and sound engineer. Michael Almada

1031
00:53:31,440 --> 00:53:34,440
Speaker 1: is our contributing producer. I'm your host, Bridget Todd. If

1032
00:53:34,480 --> 00:53:36,320
Speaker 1: you want to help us grow, rate and review.

1033
00:53:36,080 --> 00:53:37,120
Speaker 3: Us on Apple Podcasts.

1034
00:53:37,880 --> 00:53:40,680
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1035
00:53:40,760 --> 00:53:47,080
Speaker 1: Apple Podcasts, or wherever you get your podcasts