1 00:00:00,480 --> 00:00:03,880 Speaker 1: Just a quick heads up. Today's episode talks about pregnancy termination. 2 00:00:08,760 --> 00:00:10,479 Speaker 1: There Are No Girls on the Internet as a production 3 00:00:10,520 --> 00:00:18,040 Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Todd, and this 4 00:00:18,120 --> 00:00:22,479 Speaker 1: is there Are No Girls on the Internet. When it 5 00:00:22,520 --> 00:00:24,800 Speaker 1: comes to the health of our online landscape, I think 6 00:00:24,840 --> 00:00:28,240 Speaker 1: it's really important to look at how and honestly if 7 00:00:28,360 --> 00:00:32,160 Speaker 1: we're able to use our digital communications platforms to communicate 8 00:00:32,159 --> 00:00:33,960 Speaker 1: with each other about what's going on in the world. 9 00:00:34,600 --> 00:00:36,680 Speaker 1: So in this episode, I want to look at a 10 00:00:36,720 --> 00:00:40,920 Speaker 1: few recent cultural happenings and how bots did or didn't 11 00:00:41,000 --> 00:00:44,479 Speaker 1: sway those conversations. We'll be covering a lot of ground 12 00:00:44,479 --> 00:00:46,519 Speaker 1: and digging into a lot of research, and we're going 13 00:00:46,600 --> 00:00:50,040 Speaker 1: to start with Britney Spears. So we have talked about 14 00:00:50,040 --> 00:00:52,960 Speaker 1: Britney Spears on the podcast before, and I just finished 15 00:00:52,960 --> 00:00:56,640 Speaker 1: the audiobook of Britney Spears's memoir The Woman in Me. Now, 16 00:00:56,800 --> 00:00:59,120 Speaker 1: even if you're someone who does not read a lot 17 00:00:59,120 --> 00:01:02,640 Speaker 1: of celebrity memoirs, I think this book is still worth 18 00:01:02,640 --> 00:01:05,440 Speaker 1: a read, or you could listen to the audiobook narrated 19 00:01:05,480 --> 00:01:08,520 Speaker 1: by Michelle Williams. At first, I was like, Oh, I 20 00:01:08,520 --> 00:01:11,039 Speaker 1: don't need to read this whole thing. I'll just listen 21 00:01:11,080 --> 00:01:13,800 Speaker 1: to a recap on a podcast. So I listened to 22 00:01:13,840 --> 00:01:16,760 Speaker 1: the podcast Celebrity Memoir book Club about the memoir, and 23 00:01:16,959 --> 00:01:20,160 Speaker 1: they ended the episode saying, y'all really need to read 24 00:01:20,160 --> 00:01:23,240 Speaker 1: this book for yourselves. So I did, and they were right. 25 00:01:23,600 --> 00:01:26,840 Speaker 1: Listening to Brittany in her own words was really a 26 00:01:26,880 --> 00:01:30,200 Speaker 1: different experience. The book is a hit. It sold one 27 00:01:30,280 --> 00:01:33,480 Speaker 1: point one million copies in its first week alone. It's 28 00:01:33,560 --> 00:01:35,559 Speaker 1: at the top of the New York Times bestseller list, 29 00:01:35,880 --> 00:01:38,840 Speaker 1: and it is the number two fastest debut of the year, 30 00:01:39,000 --> 00:01:42,600 Speaker 1: second to Prince Harry's Spare, So it is clear people 31 00:01:42,640 --> 00:01:44,920 Speaker 1: have been waiting to hear what Britney has to say. 32 00:01:45,600 --> 00:01:47,720 Speaker 1: In the memoir, Brittany opens up about some of what 33 00:01:47,800 --> 00:01:50,560 Speaker 1: we already knew and some that we didn't about her upbringing, 34 00:01:50,680 --> 00:01:54,080 Speaker 1: her rise to fame, and the horrifying details of her 35 00:01:54,160 --> 00:01:58,200 Speaker 1: thirteen year long conservatorship. It's one of those memoirs where 36 00:01:58,200 --> 00:02:00,960 Speaker 1: Britney is pretty gracious and how she reflects on the 37 00:02:00,960 --> 00:02:03,080 Speaker 1: people in her life who did not treat her well. 38 00:02:03,560 --> 00:02:06,320 Speaker 1: She has a lot of grace and compassion for people 39 00:02:06,320 --> 00:02:11,040 Speaker 1: who treated her like absolute garbage, and unfortunately it sounds 40 00:02:11,080 --> 00:02:13,160 Speaker 1: like there are a lot of people like that in 41 00:02:13,200 --> 00:02:17,720 Speaker 1: her life, her own family who financially benefited from her conservatorship, 42 00:02:18,200 --> 00:02:21,520 Speaker 1: media figures who mocked and belittled her, and of course, 43 00:02:21,880 --> 00:02:26,040 Speaker 1: her ex, Justin Timberlake. In Britney's memoir, she talks about 44 00:02:26,040 --> 00:02:29,360 Speaker 1: some of the details of their relationship, including her pregnancy, 45 00:02:29,440 --> 00:02:32,320 Speaker 1: which she says she terminated after Justin said he was 46 00:02:32,360 --> 00:02:34,880 Speaker 1: not ready to be a parent. Brittany said in her 47 00:02:34,919 --> 00:02:38,000 Speaker 1: memoir that she was actually happy about the unplanned pregnancy, 48 00:02:38,280 --> 00:02:40,800 Speaker 1: but Justin Timberlake was not ready to be a father. 49 00:02:41,400 --> 00:02:44,200 Speaker 1: She says, terminating the pregnancy was quote one of the 50 00:02:44,240 --> 00:02:47,359 Speaker 1: most agonizing things I have ever experienced in my life. 51 00:02:47,639 --> 00:02:50,120 Speaker 1: On realizing that she was pregnant, she said, it was 52 00:02:50,120 --> 00:02:52,320 Speaker 1: such a surprise for me, but it wasn't a tragedy. 53 00:02:52,560 --> 00:02:55,359 Speaker 1: I loved Justin so much. I always expected us to 54 00:02:55,400 --> 00:02:57,600 Speaker 1: have a family together one day. This would just be 55 00:02:57,720 --> 00:03:01,080 Speaker 1: much earlier than I anticipated. But Justine and definitely was 56 00:03:01,120 --> 00:03:04,120 Speaker 1: not happy about the pregnancy. He said we weren't ready 57 00:03:04,160 --> 00:03:05,880 Speaker 1: to have a baby in our lives, that we were 58 00:03:05,919 --> 00:03:08,560 Speaker 1: way too young. I agreed not to have the baby. 59 00:03:08,680 --> 00:03:10,680 Speaker 1: I don't know if that was the right decision. If 60 00:03:10,680 --> 00:03:12,600 Speaker 1: it had been left up to me alone, I never 61 00:03:12,600 --> 00:03:15,200 Speaker 1: would have done it, And yet Justin was so sure 62 00:03:15,240 --> 00:03:17,480 Speaker 1: that he did not want to be a father. Now, 63 00:03:17,520 --> 00:03:20,560 Speaker 1: to be clear, this is only one detail from a 64 00:03:20,600 --> 00:03:23,800 Speaker 1: life story filled with so much color, pain and insight. 65 00:03:24,200 --> 00:03:26,200 Speaker 1: But it's the detail that I feel like a lot 66 00:03:26,240 --> 00:03:30,080 Speaker 1: of media outlets have really latched onto, probably because it 67 00:03:30,160 --> 00:03:33,440 Speaker 1: involves Justin Timberlake. Like, if you've not read the memoir, 68 00:03:33,520 --> 00:03:36,200 Speaker 1: you probably have seen this one detail about the abortion 69 00:03:36,680 --> 00:03:39,560 Speaker 1: in headlines about the book, as opposed to all the 70 00:03:39,640 --> 00:03:42,440 Speaker 1: other details that she shares in the book, like how 71 00:03:42,560 --> 00:03:45,320 Speaker 1: she recorded and produced her album Blackout during one of 72 00:03:45,320 --> 00:03:47,880 Speaker 1: the worst years of her life, and she did it 73 00:03:47,920 --> 00:03:51,000 Speaker 1: in basically one take, or about how she was almost 74 00:03:51,040 --> 00:03:54,720 Speaker 1: in the movie adaptation of the Broadway musical Chicago Boy. 75 00:03:54,840 --> 00:03:57,200 Speaker 1: Did we all miss out on that? Or how she 76 00:03:57,240 --> 00:03:59,640 Speaker 1: describes her use of Instagram it's just this place to 77 00:03:59,640 --> 00:04:00,560 Speaker 1: have fun and play. 78 00:04:00,920 --> 00:04:01,000 Speaker 2: Like. 79 00:04:01,040 --> 00:04:03,840 Speaker 1: The book is full of so much rich texture about 80 00:04:03,880 --> 00:04:06,600 Speaker 1: Brittany that it kind of makes me sad that we're 81 00:04:06,640 --> 00:04:10,360 Speaker 1: really seeing the conversation unfold along the lines of the 82 00:04:10,440 --> 00:04:12,840 Speaker 1: man in her life. And listen, if you listen to 83 00:04:12,840 --> 00:04:15,640 Speaker 1: the episode that we did on Janet Jackson or any 84 00:04:15,680 --> 00:04:17,760 Speaker 1: of the other times where it comes up on the show. 85 00:04:18,000 --> 00:04:21,440 Speaker 1: You probably already know that I will happily take any 86 00:04:21,600 --> 00:04:24,400 Speaker 1: opportunity to talk about how much I do not care 87 00:04:24,480 --> 00:04:27,479 Speaker 1: for Justin Timberlake. But there's so much in this book. 88 00:04:27,560 --> 00:04:30,240 Speaker 1: I just hate that the online conversation has been dominated 89 00:04:30,279 --> 00:04:33,800 Speaker 1: by what does Justin think? What is Justin's reaction? What 90 00:04:33,800 --> 00:04:36,960 Speaker 1: did Justin do? But that has been a big part 91 00:04:37,000 --> 00:04:40,479 Speaker 1: of the reaction. So after Britney's memoir was published, it 92 00:04:40,560 --> 00:04:44,520 Speaker 1: renewed criticism of Justin Timberlake's behavior in their romantic relationship 93 00:04:44,839 --> 00:04:48,360 Speaker 1: and especially during that relationship's aftermass people have been weighing 94 00:04:48,480 --> 00:04:51,840 Speaker 1: on social media, and Justin Timberlake actually turned off the 95 00:04:51,880 --> 00:04:55,240 Speaker 1: comments to his Instagram, so did his wife, Jessica Biel. 96 00:04:55,839 --> 00:05:00,120 Speaker 1: And people on social media notice something else unusual online too. 97 00:05:00,760 --> 00:05:05,720 Speaker 1: On Twitter, several social media users suspiciously used identical language 98 00:05:05,760 --> 00:05:09,240 Speaker 1: to downplay Britney's revelations about Timberlake and the termination of 99 00:05:09,279 --> 00:05:13,800 Speaker 1: the pregnancy, all repeating the same line, verbatim quote, two 100 00:05:13,880 --> 00:05:16,960 Speaker 1: consenting adults made a joint decision what was best for 101 00:05:17,000 --> 00:05:20,320 Speaker 1: them in the period of their lives. I see no issue. 102 00:05:20,360 --> 00:05:22,560 Speaker 1: This line was shown to have been posted from a 103 00:05:22,560 --> 00:05:27,400 Speaker 1: few different social media accounts verbatim, So different social media 104 00:05:27,480 --> 00:05:31,720 Speaker 1: accounts all defending the behavior of an unpopular, fading celebrity 105 00:05:32,040 --> 00:05:37,120 Speaker 1: using the exact same language. That's sus suspicious. So this 106 00:05:37,240 --> 00:05:41,280 Speaker 1: leads to the obvious question, is someone using inauthentic accounts 107 00:05:41,600 --> 00:05:45,080 Speaker 1: sometimes more commonly referred to as social media bots to 108 00:05:45,200 --> 00:05:49,200 Speaker 1: manipulate public sentiment around Britney Spears and Justin Timberlake. Oh So, 109 00:05:49,240 --> 00:05:51,960 Speaker 1: in case you're curious why I'm switching up using the 110 00:05:52,080 --> 00:05:55,720 Speaker 1: term bots and inauthentic accounts in this conversation, Well, it's 111 00:05:55,760 --> 00:05:59,880 Speaker 1: because social media researchers sometimes use the phrase inauthentic account 112 00:06:00,160 --> 00:06:03,760 Speaker 1: rather than bots, because bots brings to mind like automated 113 00:06:03,760 --> 00:06:07,239 Speaker 1: accounts not being run by humans. But sometimes these accounts 114 00:06:07,360 --> 00:06:11,640 Speaker 1: are actually real people trying to inauthentically coordinate to manipulate 115 00:06:11,680 --> 00:06:16,000 Speaker 1: public sentiment, hence inauthentic accounts. Okay, but back to Justin 116 00:06:16,000 --> 00:06:19,120 Speaker 1: and Brittany. So the obvious answer here is that Justin 117 00:06:19,200 --> 00:06:23,000 Speaker 1: Timberlake's PR team has organized inauthentic accounts to sway the 118 00:06:23,000 --> 00:06:25,919 Speaker 1: public to be on his side. Right, Well, maybe not 119 00:06:26,800 --> 00:06:29,000 Speaker 1: the daily Dot looked into it and found that of 120 00:06:29,040 --> 00:06:32,520 Speaker 1: the nine accounts on Twitter that repeated the two consenting 121 00:06:32,600 --> 00:06:36,000 Speaker 1: adults line since October seventeenth, at least one of them 122 00:06:36,000 --> 00:06:38,800 Speaker 1: appeared to have made the comment as a joke, like 123 00:06:38,920 --> 00:06:42,080 Speaker 1: a joking use of what is called copy pasta content 124 00:06:42,120 --> 00:06:44,880 Speaker 1: that is copied, pasted and repeated online as a joke 125 00:06:45,000 --> 00:06:47,520 Speaker 1: or a troll. The Daily Dot actually talked to people 126 00:06:47,520 --> 00:06:50,279 Speaker 1: behind two of the accounts that posted the line. One 127 00:06:50,320 --> 00:06:53,560 Speaker 1: of them seems to be a clear self identified troll 128 00:06:53,600 --> 00:06:57,320 Speaker 1: who said, I copy pasted it it seemed funny. Another 129 00:06:57,440 --> 00:07:00,919 Speaker 1: person they spoke to who repeated the two consenting adults 130 00:07:00,960 --> 00:07:05,080 Speaker 1: line on Twitter is a Justin Timberlake fan account, but 131 00:07:05,160 --> 00:07:07,880 Speaker 1: they claim to be genuinely copying and pasting the line 132 00:07:07,920 --> 00:07:10,840 Speaker 1: because they'd seen other Timberlake fan accounts post the same thing, 133 00:07:11,200 --> 00:07:13,760 Speaker 1: telling the Daily Dot there is no bot campaign, I 134 00:07:13,840 --> 00:07:16,920 Speaker 1: assure you. The issue is Britney's fan base is ten 135 00:07:17,040 --> 00:07:19,960 Speaker 1: times the size of Justin's on social media. They're twisting 136 00:07:20,040 --> 00:07:22,640 Speaker 1: narratives to make things worse for Justin. So to be 137 00:07:22,880 --> 00:07:26,000 Speaker 1: super clear, there is no evidence that Justin Timberlake or 138 00:07:26,000 --> 00:07:29,240 Speaker 1: anyone connected to him is directing inauthentic accounts to post 139 00:07:29,280 --> 00:07:31,960 Speaker 1: and support of him. Also, just a side note, did 140 00:07:32,000 --> 00:07:35,920 Speaker 1: you know that last summer Justin Timberlake danced so badly 141 00:07:36,040 --> 00:07:38,320 Speaker 1: during a performance at a music festival here in DC 142 00:07:38,760 --> 00:07:41,520 Speaker 1: that he had to issue an apology to the entire city. 143 00:07:41,960 --> 00:07:45,920 Speaker 1: But I digress. So maybe Justin's team is not behind 144 00:07:46,000 --> 00:07:49,680 Speaker 1: this social media behavior. But it is curious, you know, 145 00:07:49,840 --> 00:07:52,760 Speaker 1: other than having a laugh, why would a handful of 146 00:07:52,840 --> 00:07:56,160 Speaker 1: disparate people on social media all be using the exact 147 00:07:56,240 --> 00:07:59,360 Speaker 1: same language to talk about Britney Spears and Justin Timberlake. 148 00:08:00,040 --> 00:08:03,560 Speaker 1: Social media users noticed something similar earlier this summer, when 149 00:08:03,600 --> 00:08:07,040 Speaker 1: actor Jonah Hill was being scrutinized after his ex surfer 150 00:08:07,160 --> 00:08:10,080 Speaker 1: Sarah Brady, published text messages that he had set during 151 00:08:10,080 --> 00:08:14,080 Speaker 1: their relationship. The texts, to many, including myself, seemed to 152 00:08:14,080 --> 00:08:17,360 Speaker 1: be using therapy language to manipulate her behavior in some 153 00:08:17,480 --> 00:08:22,040 Speaker 1: pretty controlling ways that Brady herself categorized as emotionally abusive. 154 00:08:22,400 --> 00:08:26,000 Speaker 1: The conversation exploded online, with people talking about how their 155 00:08:26,080 --> 00:08:29,400 Speaker 1: exes tried to manipulate and control them. We even made 156 00:08:29,400 --> 00:08:31,840 Speaker 1: an episode about it. And while all of that was 157 00:08:31,880 --> 00:08:35,240 Speaker 1: going down, social media users found that accounts were doing 158 00:08:35,240 --> 00:08:37,520 Speaker 1: the same thing as what we're seeing in the Justin 159 00:08:37,559 --> 00:08:41,840 Speaker 1: Timberlake Britney Spears situation, repeating a pro Jonah Hill message 160 00:08:41,840 --> 00:08:45,199 Speaker 1: on posts about the situation verbatim the message in question 161 00:08:45,360 --> 00:08:48,840 Speaker 1: read quote. It sounds like Jonah Hill is communicating what 162 00:08:48,920 --> 00:08:51,480 Speaker 1: works for him in a clear way. He literally said 163 00:08:51,520 --> 00:08:53,800 Speaker 1: he supports her if that brings her happiness, it's just 164 00:08:53,880 --> 00:08:56,319 Speaker 1: not for him. I actually see nothing wrong with this. 165 00:08:57,280 --> 00:09:00,680 Speaker 1: So is it just me or is there something strangely 166 00:09:00,720 --> 00:09:03,800 Speaker 1: similar about the Timberlake tweets and the Jonah Hill tweets? 167 00:09:04,320 --> 00:09:06,480 Speaker 1: The way they kind of implore us to see the 168 00:09:06,520 --> 00:09:09,959 Speaker 1: perspective of the man accused of bad behavior, The way 169 00:09:09,960 --> 00:09:12,480 Speaker 1: they both end with some version of I see nothing 170 00:09:12,520 --> 00:09:15,200 Speaker 1: wrong with this or I see no issue. They just 171 00:09:15,240 --> 00:09:20,680 Speaker 1: seem awfully similar. Now, obviously, neither Justin Timberlake or Jonah 172 00:09:20,720 --> 00:09:23,400 Speaker 1: Hill have said anything about this, And this is where 173 00:09:23,440 --> 00:09:24,760 Speaker 1: I kind of need to give you a heads up 174 00:09:24,800 --> 00:09:26,600 Speaker 1: that if you're hoping that there's going to be some 175 00:09:26,720 --> 00:09:30,040 Speaker 1: kind of like big reveal of some smoking gun in 176 00:09:30,080 --> 00:09:34,120 Speaker 1: this episode, sadly there will not be. Even after all 177 00:09:34,120 --> 00:09:36,439 Speaker 1: the research I did for this episode, I don't feel 178 00:09:36,480 --> 00:09:39,440 Speaker 1: any closer to knowing what is actually going on with 179 00:09:39,520 --> 00:09:43,199 Speaker 1: these specific accounts repeating this specific line. You know, did 180 00:09:43,200 --> 00:09:45,520 Speaker 1: a handful of people all just decide to post the 181 00:09:45,520 --> 00:09:49,080 Speaker 1: same message for a joke or to create confusion? Is 182 00:09:49,120 --> 00:09:52,320 Speaker 1: something else going on? I don't know. But the question 183 00:09:52,440 --> 00:09:54,960 Speaker 1: that I am really curious about is are we going 184 00:09:55,000 --> 00:09:58,320 Speaker 1: to see more and more inauthentic social media activity and 185 00:09:58,360 --> 00:10:01,560 Speaker 1: accounts as a way to shape our opinions? And what 186 00:10:01,600 --> 00:10:03,880 Speaker 1: does that mean for all of us and for our 187 00:10:03,880 --> 00:10:08,320 Speaker 1: digital media landscape more broadly. Now, obviously PR is nothing new. 188 00:10:08,640 --> 00:10:11,840 Speaker 1: There's an entire lucrative industry built around making us the 189 00:10:11,920 --> 00:10:16,440 Speaker 1: public think about people, brands, and campaigns in specific ways. 190 00:10:17,160 --> 00:10:21,520 Speaker 1: But coordinating people or bots to hijack conversations on social 191 00:10:21,559 --> 00:10:26,440 Speaker 1: media to inauthentically manipulate public opinion is a very different thing. 192 00:10:26,800 --> 00:10:29,839 Speaker 1: And if it becomes just another plank of normal ways 193 00:10:29,840 --> 00:10:32,599 Speaker 1: to do PR and sway public opinion, I think it 194 00:10:32,640 --> 00:10:35,480 Speaker 1: would be a pretty big sign of this ongoing rot 195 00:10:35,559 --> 00:10:38,600 Speaker 1: in our digital landscape. So is coordinating the use of 196 00:10:38,600 --> 00:10:41,520 Speaker 1: inauthentic accounts going to be a more viable and commonplace 197 00:10:41,559 --> 00:10:44,000 Speaker 1: strategy for brands and companies looking to engage with the 198 00:10:44,040 --> 00:10:46,280 Speaker 1: public and do you remember when you could go to 199 00:10:46,360 --> 00:10:49,920 Speaker 1: the Internet to get information to help you understand something 200 00:10:50,320 --> 00:10:53,760 Speaker 1: without having to worry if some corporate or pr interest 201 00:10:54,160 --> 00:10:56,600 Speaker 1: was actually just secretly trying to push you towards having 202 00:10:56,640 --> 00:11:01,599 Speaker 1: their brand friendly opinion? Like is everything online just bots, 203 00:11:01,640 --> 00:11:02,800 Speaker 1: fakes and scams? 204 00:11:02,840 --> 00:11:03,120 Speaker 2: Now? 205 00:11:03,679 --> 00:11:08,160 Speaker 1: Well, just ask HBO, because just last week, HBO came 206 00:11:08,240 --> 00:11:12,240 Speaker 1: under fire for using inauthentic social media accounts to silence 207 00:11:12,280 --> 00:11:16,880 Speaker 1: critics of their shows. Allegedly, between June twenty twenty and 208 00:11:16,920 --> 00:11:20,920 Speaker 1: April twenty twenty one, HBO Programming chief Casey Bloyse and 209 00:11:20,960 --> 00:11:25,480 Speaker 1: Senior Vice president of Drama Programming Kathleen McCaffrey repeatedly discussed 210 00:11:25,559 --> 00:11:28,800 Speaker 1: using Berner social media accounts to directly combat critics of 211 00:11:28,840 --> 00:11:32,120 Speaker 1: their shows. On Twitter. According to Variety, there were at 212 00:11:32,240 --> 00:11:35,640 Speaker 1: least six different text message exchanges between the two executives 213 00:11:35,840 --> 00:11:38,959 Speaker 1: that involved floating the idea of using a fake Twitter 214 00:11:39,000 --> 00:11:42,559 Speaker 1: account to harshly respond to TV critics who gave negative 215 00:11:42,600 --> 00:11:45,600 Speaker 1: reviews to HBO shows. All of this came to light 216 00:11:45,720 --> 00:11:48,240 Speaker 1: thanks to a laid off staffer who used to work 217 00:11:48,240 --> 00:11:50,920 Speaker 1: on the HBO show The Idol. You remember that show? 218 00:11:51,000 --> 00:11:54,760 Speaker 1: It was like this terrible troubled show about the dark 219 00:11:54,800 --> 00:11:58,480 Speaker 1: side of Hollywood, starring and produced by the Weekend that 220 00:11:58,600 --> 00:12:02,320 Speaker 1: was canceled after one short season. The laidoff staffer who 221 00:12:02,400 --> 00:12:05,960 Speaker 1: worked on that show is suing HBO. The HBO executives, 222 00:12:06,160 --> 00:12:09,880 Speaker 1: HBO's head of Drama and The Weekend himself, and two 223 00:12:09,960 --> 00:12:13,200 Speaker 1: other producers of the show the idol for wrongful termination. 224 00:12:13,679 --> 00:12:16,240 Speaker 1: So the staffer says that HBO executives asked her to 225 00:12:16,280 --> 00:12:18,960 Speaker 1: set up fake social media accounts to counter the tweets 226 00:12:18,960 --> 00:12:21,920 Speaker 1: from professional TV critics, And it kind of looks like 227 00:12:22,000 --> 00:12:24,680 Speaker 1: that's exactly what might have happened, because in April twenty 228 00:12:24,720 --> 00:12:28,480 Speaker 1: twenty one, a newly created Twitter account from a quote 229 00:12:28,720 --> 00:12:32,840 Speaker 1: Texas Mom and herbalist Kelly Shepherd tweeted in response to 230 00:12:33,000 --> 00:12:36,400 Speaker 1: Rolling Stone TV critic Alan Steppenwall's negative review of the 231 00:12:36,520 --> 00:12:41,480 Speaker 1: HBO series The Nevers. This quote, Texas Mom wrote, Alan 232 00:12:41,640 --> 00:12:45,320 Speaker 1: is always predictably safe and scared in his opinions, and 233 00:12:45,400 --> 00:12:48,360 Speaker 1: the language from that tweet matches the directions that the 234 00:12:48,520 --> 00:12:52,480 Speaker 1: HBO executive allegedly texts to the laidoff HBO staffer, and 235 00:12:52,720 --> 00:12:56,120 Speaker 1: the account's profile picture is just some stock image that 236 00:12:56,280 --> 00:12:59,720 Speaker 1: is found used on several business websites. That same account 237 00:12:59,840 --> 00:13:04,640 Speaker 1: was also defending HBO's shows in the comments of entertainment 238 00:13:04,679 --> 00:13:08,920 Speaker 1: trade publications like Deadline. So a coordinated campaign of inauthentic 239 00:13:08,960 --> 00:13:14,160 Speaker 1: social media activity to defend bad TV shows from criticism like? 240 00:13:14,320 --> 00:13:17,560 Speaker 1: Is this the future of discourse online? 241 00:13:20,400 --> 00:13:33,680 Speaker 2: Let's take a quick break at our back. 242 00:13:35,520 --> 00:13:37,920 Speaker 1: We used to think of coordinated and authentic accounts as 243 00:13:37,960 --> 00:13:41,040 Speaker 1: being used in an attempt to disrupt political discourse and democracy, 244 00:13:41,520 --> 00:13:44,360 Speaker 1: But now are they being used to sway public opinion 245 00:13:44,360 --> 00:13:48,600 Speaker 1: about specific celebrities and even television shows by networks. So, 246 00:13:48,720 --> 00:13:50,560 Speaker 1: if I had to guess, I would say that I 247 00:13:50,559 --> 00:13:53,560 Speaker 1: think that people in entertainment saw how effective it was 248 00:13:53,600 --> 00:13:58,160 Speaker 1: to manipulate public sentiment using coordinated social media activity and 249 00:13:58,240 --> 00:14:01,240 Speaker 1: are toying with using that more. Really, and for a 250 00:14:01,280 --> 00:14:04,440 Speaker 1: great template for how successful this kind of online coordination 251 00:14:04,520 --> 00:14:06,680 Speaker 1: can be, just look at what happened during the Amber 252 00:14:06,720 --> 00:14:10,040 Speaker 1: heard Johnny Depp defamation trial that stemmed from Herd's op 253 00:14:10,200 --> 00:14:13,640 Speaker 1: ed about domestic violence. Bot Sentinel, a research firm that 254 00:14:13,720 --> 00:14:17,680 Speaker 1: uses data science and artificial intelligence to track and detect bots, trolls, 255 00:14:17,720 --> 00:14:21,120 Speaker 1: and suspect accounts on Twitter and elsewhere, released a report 256 00:14:21,200 --> 00:14:23,840 Speaker 1: after the trial focused on how Herd was treated during 257 00:14:23,880 --> 00:14:26,640 Speaker 1: the trial. The report called the targeting of Herd on 258 00:14:26,720 --> 00:14:30,120 Speaker 1: social media quote one of the worst cases of platform 259 00:14:30,160 --> 00:14:33,440 Speaker 1: manipulation and flagrant abuse from a group of Twitter accounts. 260 00:14:33,560 --> 00:14:36,520 Speaker 1: So the La Times spoke to Christopher Bouose, CEO of 261 00:14:36,560 --> 00:14:40,080 Speaker 1: Bot Sentinel, who said, we immediately observed dozens of newly 262 00:14:40,120 --> 00:14:44,800 Speaker 1: created accounts subamming negative anti Amber Herd's cashtags. Many accounts 263 00:14:44,800 --> 00:14:47,480 Speaker 1: were replying to tweets with hashtags unrelated to the tweet 264 00:14:47,520 --> 00:14:50,480 Speaker 1: they were responding to. Some accounts encouraged others to get 265 00:14:50,480 --> 00:14:54,200 Speaker 1: the hashtags trending, and the trolls were successful on multiple occasions. 266 00:14:54,360 --> 00:14:57,000 Speaker 1: So in putting together this report, Bot Sentinel looked at 267 00:14:57,000 --> 00:15:00,120 Speaker 1: more than fourteen thousand tweets that included the hashtags, and 268 00:15:00,160 --> 00:15:02,400 Speaker 1: it determined that twenty four point four percent of the 269 00:15:02,440 --> 00:15:05,480 Speaker 1: account sending those tweets had been created in the past 270 00:15:05,520 --> 00:15:08,080 Speaker 1: seven months. So this is where I have to say, 271 00:15:08,600 --> 00:15:12,600 Speaker 1: I totally got taken by what sounds like some inauthentic 272 00:15:12,640 --> 00:15:15,960 Speaker 1: social media activity about that trial. You know, I didn't 273 00:15:16,000 --> 00:15:18,560 Speaker 1: really pay a ton of attention to the trial at first, 274 00:15:18,840 --> 00:15:21,480 Speaker 1: So then when I decided I wanted to dip in 275 00:15:21,520 --> 00:15:23,200 Speaker 1: and sort of like get a sense of what was 276 00:15:23,240 --> 00:15:26,240 Speaker 1: going on. I of course went to social media, and 277 00:15:26,320 --> 00:15:30,080 Speaker 1: the social media content that I saw was so overwhelmingly 278 00:15:30,240 --> 00:15:33,120 Speaker 1: negative toward Amber Heard that it was really easy for 279 00:15:33,200 --> 00:15:36,040 Speaker 1: me to think, like, Wow, the Internet is all collectively 280 00:15:36,080 --> 00:15:39,040 Speaker 1: against Amber Herd, so she must be in the wrong here. 281 00:15:39,520 --> 00:15:42,080 Speaker 1: It's really shocking to me how effective it was at 282 00:15:42,080 --> 00:15:46,200 Speaker 1: shaping my opinion. And yep, Goot Sentinels report says that 283 00:15:46,280 --> 00:15:49,800 Speaker 1: copy pasta was indeed used in this media manipulation campaign. 284 00:15:49,880 --> 00:15:54,280 Speaker 1: To the report reads, one manipulative technique that was employed 285 00:15:54,320 --> 00:15:57,960 Speaker 1: was copy pasta or copying and pasting duplicative content to 286 00:15:58,040 --> 00:16:01,160 Speaker 1: game Twitter's trends and propagate a positive view of depth 287 00:16:01,160 --> 00:16:04,000 Speaker 1: and a negative view of Heard. One such message read 288 00:16:04,560 --> 00:16:08,160 Speaker 1: quote people turned against Amber Heard not because Johnny Depp 289 00:16:08,240 --> 00:16:11,000 Speaker 1: is a powerful man or a famous actor, but because 290 00:16:11,040 --> 00:16:13,040 Speaker 1: we watched the trial and saw who was telling the 291 00:16:13,080 --> 00:16:16,560 Speaker 1: truth and who wasn't. And again, doesn't this kind of 292 00:16:16,600 --> 00:16:21,200 Speaker 1: sound similar to the Jonah Hill and justin Timberlake's tweets Again, 293 00:16:21,400 --> 00:16:24,000 Speaker 1: we're being asked to negate any of the quote he said, 294 00:16:24,080 --> 00:16:27,080 Speaker 1: she said and focus on the perspective of the man 295 00:16:27,160 --> 00:16:30,920 Speaker 1: accused of bad behavior. I just can't shake how these 296 00:16:30,960 --> 00:16:34,760 Speaker 1: copy pasted defenses seem to be inviting us to disregard 297 00:16:34,800 --> 00:16:38,920 Speaker 1: structures that we know exist, like power dynamics and gender dynamics, 298 00:16:38,960 --> 00:16:41,960 Speaker 1: to focus on how the man didn't do anything wrong. 299 00:16:42,480 --> 00:16:46,160 Speaker 1: And they're also framed in this way to look so logical. 300 00:16:46,600 --> 00:16:49,240 Speaker 1: You know, it's logical to see that Britney Spears and 301 00:16:49,360 --> 00:16:52,640 Speaker 1: Justin Timberlake were just two consenting adults making the best 302 00:16:52,640 --> 00:16:56,480 Speaker 1: decisions for themselves. He didn't do anything wrong. It's logical 303 00:16:56,520 --> 00:16:58,640 Speaker 1: to see that Jonah Hill was just being clear about 304 00:16:58,640 --> 00:17:01,400 Speaker 1: its boundaries for a partner. He didn't do anything wrong. 305 00:17:01,840 --> 00:17:05,119 Speaker 1: It's logical to not get blindsided by celebrity or gender 306 00:17:05,200 --> 00:17:08,199 Speaker 1: dynamics and see that Johnny Depp was simply telling the 307 00:17:08,240 --> 00:17:11,119 Speaker 1: truth in court. He didn't do anything wrong. It's like 308 00:17:11,160 --> 00:17:15,560 Speaker 1: they're all framed to invite us to completely disregard everything 309 00:17:15,600 --> 00:17:18,560 Speaker 1: we spent the last ten years of me too talking about. 310 00:17:18,840 --> 00:17:21,920 Speaker 1: But again, the question is why, you know, other than 311 00:17:21,960 --> 00:17:25,120 Speaker 1: someone working on Johnny Depp or Justin Timberlake's PR team, 312 00:17:25,600 --> 00:17:29,240 Speaker 1: why would anyone want to control our feelings around celebrities 313 00:17:29,280 --> 00:17:33,000 Speaker 1: or popular culture? Like what is there to gain. Well, 314 00:17:33,119 --> 00:17:35,399 Speaker 1: maybe Star Wars can give us a little bit of 315 00:17:35,400 --> 00:17:38,680 Speaker 1: a clue. Morton Bay, a research fellow about the usc 316 00:17:38,800 --> 00:17:42,159 Speaker 1: Aberg School for Communications and Journalism Center for the Digital Future, 317 00:17:42,640 --> 00:17:46,320 Speaker 1: looked into what appeared to be a massive backlash against 318 00:17:46,320 --> 00:17:49,080 Speaker 1: the twenty seventeen Star Wars movie The Last Jedi. If 319 00:17:49,080 --> 00:17:51,200 Speaker 1: you don't remember or you're not a Star Wars person, 320 00:17:51,480 --> 00:17:53,720 Speaker 1: The Last Jedi was the first Star Wars movie to 321 00:17:53,720 --> 00:17:55,960 Speaker 1: feature a woman of color in a lead role, Kelly 322 00:17:56,040 --> 00:18:00,280 Speaker 1: Marie Trand as Rose Tico, and a small but very 323 00:18:00,359 --> 00:18:03,760 Speaker 1: vocal minority of people didn't like that. They accused the 324 00:18:03,760 --> 00:18:08,120 Speaker 1: franchise of like forcing diversity and wokeness down their throats 325 00:18:08,200 --> 00:18:11,439 Speaker 1: or some such nonsense. Rotten Tomatoes confirmed that the film's 326 00:18:11,480 --> 00:18:15,199 Speaker 1: score was seriously targeted by a coordinated campaign to tank it. 327 00:18:15,640 --> 00:18:19,600 Speaker 1: Tran faced a slew of gendered and racialized harassment online 328 00:18:19,600 --> 00:18:23,160 Speaker 1: and eventually left social media, and Morton Bay's research showed 329 00:18:23,200 --> 00:18:26,240 Speaker 1: that some of that conversation online was driven by Russian 330 00:18:26,240 --> 00:18:30,240 Speaker 1: bot campaigns and inauthentic accounts. But more interestingly is that, 331 00:18:30,280 --> 00:18:34,639 Speaker 1: according to Vox's Emily Saint, James, Ay's study further concludes 332 00:18:34,680 --> 00:18:38,280 Speaker 1: that much of the backlash was driven by political opportunism 333 00:18:38,560 --> 00:18:42,280 Speaker 1: from the American alt right, particularly members of that movement 334 00:18:42,320 --> 00:18:45,600 Speaker 1: who were deeply involved in twenty fourteen's anti feminist and 335 00:18:45,600 --> 00:18:49,240 Speaker 1: proto alright gamer Gate movement in the video game community. 336 00:18:49,760 --> 00:18:52,840 Speaker 1: In Saint James Peace called Russian trolls us Star Wars 337 00:18:52,920 --> 00:18:56,119 Speaker 1: to sow discord online. The fact that it worked is telling, 338 00:18:56,680 --> 00:19:00,919 Speaker 1: Saint James writes, quote, what bays study really got me 339 00:19:01,000 --> 00:19:03,679 Speaker 1: thinking about was how strange it was that Russian agents 340 00:19:03,680 --> 00:19:06,359 Speaker 1: would focus on Star Wars of all things, and what 341 00:19:06,480 --> 00:19:09,439 Speaker 1: seemed to be a campaign to spread dissension throughout America 342 00:19:09,600 --> 00:19:12,560 Speaker 1: dating back to before the twenty sixteen election. Whether or 343 00:19:12,640 --> 00:19:14,920 Speaker 1: not a Star Wars movie is good or bad has 344 00:19:14,960 --> 00:19:18,520 Speaker 1: little bearing on the overall twists and turns of global geopolitics, 345 00:19:18,760 --> 00:19:22,359 Speaker 1: and yet here was evidence that somebody in Russia sure disagreed. 346 00:19:22,720 --> 00:19:25,800 Speaker 1: Maybe the Russian bucks that they identified are all extra governmental, 347 00:19:26,119 --> 00:19:28,280 Speaker 1: built by trolls with spare time on their hands and 348 00:19:28,320 --> 00:19:32,240 Speaker 1: a grudge against Lucasfilms. Or maybe Bay's findings are yet 349 00:19:32,240 --> 00:19:35,800 Speaker 1: another example of how thoroughly Russian intelligence has zeroed in 350 00:19:35,880 --> 00:19:39,199 Speaker 1: on the idea that white nationalism is central to driving 351 00:19:39,200 --> 00:19:42,200 Speaker 1: a wedge in American society. If the latter is true, 352 00:19:42,560 --> 00:19:45,960 Speaker 1: then what's most unnerving about Russia's intelligence strategy and its 353 00:19:45,960 --> 00:19:49,199 Speaker 1: connection to Star Wars isn't what the strategy says about Russia, 354 00:19:49,680 --> 00:19:52,520 Speaker 1: but what it says about us. So what does it 355 00:19:52,600 --> 00:19:53,320 Speaker 1: say about us? 356 00:19:53,600 --> 00:19:53,760 Speaker 2: Well? 357 00:19:53,800 --> 00:19:57,720 Speaker 1: Saint James argues that our political landscape increasingly operates like 358 00:19:57,800 --> 00:20:03,480 Speaker 1: one big fandom, picking teams and being and staying fanatically 359 00:20:03,520 --> 00:20:06,159 Speaker 1: loyal to those teams as a marker of identity. You 360 00:20:06,200 --> 00:20:08,680 Speaker 1: know who they are in the world, and we already 361 00:20:08,720 --> 00:20:11,440 Speaker 1: know that using identity and our anxieties and hang ups 362 00:20:11,440 --> 00:20:14,120 Speaker 1: and fears that are rooted in identity has always been 363 00:20:14,160 --> 00:20:16,520 Speaker 1: one of the most effective ways to cause disruption and 364 00:20:16,600 --> 00:20:20,040 Speaker 1: manipulate people. So to be super clear, I am not 365 00:20:20,119 --> 00:20:22,560 Speaker 1: suggesting that there is a foreign campaign to get us 366 00:20:22,600 --> 00:20:26,879 Speaker 1: to think favorably about a fading pop star like justin Timberlake. 367 00:20:27,320 --> 00:20:31,560 Speaker 1: It's concerning not to mention really telling that popular culture, 368 00:20:31,920 --> 00:20:34,320 Speaker 1: you know, the music we like, the celebrities we follow, 369 00:20:34,440 --> 00:20:38,199 Speaker 1: the films we identify with, is becoming just another domain 370 00:20:38,280 --> 00:20:41,479 Speaker 1: to spread chaos and division. But it also kind of 371 00:20:41,520 --> 00:20:45,400 Speaker 1: makes sense because the content we consume is so often 372 00:20:45,480 --> 00:20:48,360 Speaker 1: tied with our identities. But I do think that if 373 00:20:48,400 --> 00:20:53,199 Speaker 1: identity based conversations online like these are artificially manipulated, it 374 00:20:53,240 --> 00:20:56,240 Speaker 1: does leave us more polarized and more susceptible to being 375 00:20:56,240 --> 00:20:59,680 Speaker 1: manipulated along highly charged tension points for more in a 376 00:20:59,760 --> 00:21:03,520 Speaker 1: fair political causes where the stakes are a whole lot higher. 377 00:21:06,520 --> 00:21:06,680 Speaker 2: More. 378 00:21:06,720 --> 00:21:19,399 Speaker 1: After a quick break, let's get right back into it. 379 00:21:21,080 --> 00:21:24,439 Speaker 1: We talked before about inauthentic social media accounts trying to 380 00:21:24,440 --> 00:21:27,879 Speaker 1: spread chaos in the twenty sixteen elections targeting black and 381 00:21:27,960 --> 00:21:31,880 Speaker 1: brown voters, while we're already seeing inauthentic accounts being used 382 00:21:31,920 --> 00:21:35,000 Speaker 1: ahead of the twenty twenty four presidential election too. Gizmoto 383 00:21:35,080 --> 00:21:38,160 Speaker 1: published a detailed report about a Twitter bot campaign. Using 384 00:21:38,200 --> 00:21:42,560 Speaker 1: research from the digital analytics firm Sabra, Siabra uncovered a 385 00:21:42,600 --> 00:21:45,960 Speaker 1: bot run pro Trump campaign. The bot farm was created 386 00:21:45,960 --> 00:21:48,439 Speaker 1: within the last eleven months solely to heat praise on 387 00:21:48,480 --> 00:21:52,840 Speaker 1: Trump while ridiculing his potential political opponents, especially those likely 388 00:21:52,840 --> 00:21:56,080 Speaker 1: to challenge him in twenty twenty four. According to Siabra researchers, 389 00:21:56,400 --> 00:21:59,600 Speaker 1: a regular conversation on sites like Facebook or Twitter will 390 00:21:59,680 --> 00:22:02,440 Speaker 1: usually track between four percent and eight percent of fake 391 00:22:02,440 --> 00:22:06,320 Speaker 1: accounts in many of these conversations surrounding Trump. That was 392 00:22:06,440 --> 00:22:09,680 Speaker 1: up to between twenty percent and forty percent. Over a 393 00:22:09,800 --> 00:22:12,520 Speaker 1: quarter of the interactions for pro Trump officials like ReBs, 394 00:22:12,600 --> 00:22:15,359 Speaker 1: Jim Jordan, and Matt Gates on Twitter came from bots. 395 00:22:15,400 --> 00:22:18,520 Speaker 1: According to this report, it is far far more than 396 00:22:18,600 --> 00:22:22,640 Speaker 1: left wing accounts experience, like Senator Warren or Representative Alexandria 397 00:22:22,640 --> 00:22:26,399 Speaker 1: Ocazio Cortez. And importantly, for all the talk we do 398 00:22:26,440 --> 00:22:30,120 Speaker 1: about Russian assets and Russian run bot campaigns, this bot 399 00:22:30,160 --> 00:22:32,920 Speaker 1: farm was likely created and being run from right here 400 00:22:32,960 --> 00:22:35,639 Speaker 1: in the United States. The company of Researchers found that 401 00:22:35,640 --> 00:22:38,880 Speaker 1: there were three massive bot farms established in April, October, 402 00:22:38,920 --> 00:22:42,240 Speaker 1: and November of last year. These interconnected bot farms were 403 00:22:42,320 --> 00:22:45,560 Speaker 1: likely created in the United States. What's more, all these 404 00:22:45,600 --> 00:22:48,600 Speaker 1: fake accounts were extremely proch Trump and attacked anybody who 405 00:22:48,680 --> 00:22:50,640 Speaker 1: made negative mention of the former president. 406 00:22:51,040 --> 00:22:51,800 Speaker 2: So that's a lot. 407 00:22:51,960 --> 00:22:54,919 Speaker 1: But how effective are these bot campaigns really at shaping 408 00:22:54,920 --> 00:22:57,879 Speaker 1: public opinion. It's actually kind of hard to say, and 409 00:22:58,119 --> 00:23:01,479 Speaker 1: also kind of in dispute. First, it's really hard for 410 00:23:01,520 --> 00:23:04,320 Speaker 1: researchers to get any kind of clear consensus about bot 411 00:23:04,359 --> 00:23:07,840 Speaker 1: activity on social media sites, especially now that Twitter has 412 00:23:07,880 --> 00:23:10,760 Speaker 1: blocked access to the API, it's harder than ever for 413 00:23:10,840 --> 00:23:13,119 Speaker 1: researchers to get a look into the back end to 414 00:23:13,160 --> 00:23:15,720 Speaker 1: make any kind of assessment about what's happening on the site. 415 00:23:16,040 --> 00:23:20,120 Speaker 1: According to research from Dahudzeman, Associate Professor of Operations Management 416 00:23:20,119 --> 00:23:23,080 Speaker 1: at MIT, even a small handful of bots can have 417 00:23:23,119 --> 00:23:26,560 Speaker 1: an impact on shaping public opinion. His research found that 418 00:23:26,600 --> 00:23:29,640 Speaker 1: a small number of highly active bots can significantly change 419 00:23:29,640 --> 00:23:33,240 Speaker 1: people's political opinions. The main factor was not how many 420 00:23:33,240 --> 00:23:36,120 Speaker 1: bots were used, but rather how many tweets each set 421 00:23:36,119 --> 00:23:39,439 Speaker 1: of bots issued. But there is some research to suggest 422 00:23:39,480 --> 00:23:43,120 Speaker 1: that the concern about bots's impact on our behavior is overblown. 423 00:23:43,440 --> 00:23:46,200 Speaker 1: Despite the presence of Russian bots trying to disrupt the 424 00:23:46,200 --> 00:23:49,399 Speaker 1: twenty sixteen election, there is some research suggesting that maybe 425 00:23:49,400 --> 00:23:53,200 Speaker 1: it wasn't all that effective in actually changing American voting behavior. 426 00:23:53,600 --> 00:23:57,320 Speaker 1: But here's the interesting thing. When inauthentic accounts and bots 427 00:23:57,359 --> 00:24:00,679 Speaker 1: are used to shape discourse in these clandestine ways at all, 428 00:24:01,080 --> 00:24:03,800 Speaker 1: it has an impact whether we're doing the thing the 429 00:24:03,880 --> 00:24:06,000 Speaker 1: bot campaign is trying to get us to do or not. 430 00:24:06,640 --> 00:24:09,280 Speaker 1: That's because the knowledge that there could be bots out 431 00:24:09,280 --> 00:24:13,120 Speaker 1: there manipulating conversations online is enough to make us change 432 00:24:13,119 --> 00:24:16,119 Speaker 1: our behavior and shape the way that we do discourse online. 433 00:24:16,600 --> 00:24:19,000 Speaker 1: According to a study by Adam Waits, a professor of 434 00:24:19,040 --> 00:24:22,680 Speaker 1: Management and Organizations at the Kellogg School, people are more 435 00:24:22,840 --> 00:24:24,959 Speaker 1: likely to assume that posts that they don't agree with 436 00:24:25,119 --> 00:24:28,119 Speaker 1: are from bots, not from real people. So if they 437 00:24:28,119 --> 00:24:31,320 Speaker 1: are bots being deployed in discourse online at all, it 438 00:24:31,440 --> 00:24:33,760 Speaker 1: kind of doesn't matter if the content that we're seeing 439 00:24:34,000 --> 00:24:36,679 Speaker 1: is being driven by bots or not. When people believe 440 00:24:36,720 --> 00:24:39,880 Speaker 1: they are interacting with a bot, they trust online discourse 441 00:24:39,960 --> 00:24:42,520 Speaker 1: less and show less willingness to engage with it. One 442 00:24:42,520 --> 00:24:46,720 Speaker 1: of the reports researchers said, viewed alongside earlier studies which 443 00:24:46,720 --> 00:24:49,560 Speaker 1: show that people perceive opposing views as more bot like, 444 00:24:50,000 --> 00:24:53,040 Speaker 1: the results of this experiment suggest that the political bias 445 00:24:53,359 --> 00:24:56,960 Speaker 1: may contribute to markers of political polarization. One of the 446 00:24:57,000 --> 00:24:59,919 Speaker 1: researchers on this study says that the research suggests does 447 00:25:00,160 --> 00:25:04,280 Speaker 1: how deep American partisan divides go, saying I was surprised 448 00:25:04,280 --> 00:25:07,600 Speaker 1: by how consistently this bias emerged. People are very quick 449 00:25:07,600 --> 00:25:11,840 Speaker 1: to dismiss other viewpoints as not only incorrect, but also 450 00:25:12,160 --> 00:25:17,199 Speaker 1: as not even human, and our current conversation around bots 451 00:25:17,400 --> 00:25:20,520 Speaker 1: might not actually even be helpful weights. The author of 452 00:25:20,560 --> 00:25:23,880 Speaker 1: the study said that belief that bot accounts are widespread 453 00:25:24,359 --> 00:25:26,840 Speaker 1: may have the effect of poisoning the well and making 454 00:25:26,880 --> 00:25:31,000 Speaker 1: all online content seem suspect. The research, he says, does 455 00:25:31,040 --> 00:25:33,840 Speaker 1: make me wonder whether all the hype around bots might 456 00:25:33,920 --> 00:25:37,480 Speaker 1: actually have a more damaging effect or more biasing effects 457 00:25:37,560 --> 00:25:40,320 Speaker 1: than the bots themselves. So it's kind of like a 458 00:25:40,359 --> 00:25:43,720 Speaker 1: double edged sword, because people should be aware of the 459 00:25:43,760 --> 00:25:46,880 Speaker 1: reality that there are bots out there trying to hijack 460 00:25:46,920 --> 00:25:51,240 Speaker 1: conversations online, but that reality could also shape the way 461 00:25:51,280 --> 00:25:53,840 Speaker 1: people see and engage in discourse in ways that make 462 00:25:53,920 --> 00:25:56,920 Speaker 1: us all more polarized and less informed, and that is 463 00:25:56,960 --> 00:26:00,000 Speaker 1: an outcome that could have very big impacts on our democracy, 464 00:26:00,280 --> 00:26:03,040 Speaker 1: which is probably why the hbos of the world should 465 00:26:03,080 --> 00:26:06,560 Speaker 1: maybe think twice before using inauthentic accounts just to make 466 00:26:06,600 --> 00:26:09,600 Speaker 1: their bad TV shows look more well liked, because they 467 00:26:09,680 --> 00:26:12,960 Speaker 1: might think they're just making it seem like people like 468 00:26:13,040 --> 00:26:17,000 Speaker 1: our Texas herbalist mom Kelly Shepherd are just really going 469 00:26:17,000 --> 00:26:20,280 Speaker 1: to bed and defending their programming, But in actuality, they're 470 00:26:20,320 --> 00:26:23,400 Speaker 1: making all of us trust online discourse and in turn 471 00:26:23,520 --> 00:26:26,400 Speaker 1: each other less And I'm sad to say that none 472 00:26:26,440 --> 00:26:29,320 Speaker 1: of this shows any signs of slowing down anytime soon. 473 00:26:29,840 --> 00:26:33,080 Speaker 1: In fact, kind of the opposite. And even though Elon 474 00:26:33,200 --> 00:26:36,240 Speaker 1: Musk pledged to crack down on bots on Twitter, you 475 00:26:36,400 --> 00:26:38,600 Speaker 1: might remember that he even tried to use the prevalence 476 00:26:38,640 --> 00:26:41,200 Speaker 1: of bots to weavel out of the sale way back when, 477 00:26:41,680 --> 00:26:44,240 Speaker 1: and is now even floating the idea of having people 478 00:26:44,280 --> 00:26:47,120 Speaker 1: paid to use Twitter to curb bots. The problem has 479 00:26:47,160 --> 00:26:50,200 Speaker 1: actually gotten worse under his tenure as the owner of Twitter, 480 00:26:50,640 --> 00:26:52,960 Speaker 1: and it really does not sound like his team is 481 00:26:53,000 --> 00:26:54,840 Speaker 1: doing a whole lot to fix it. A team of 482 00:26:54,880 --> 00:26:58,800 Speaker 1: researchers at Queensland University of Technology who track misinformation and 483 00:26:58,800 --> 00:27:02,040 Speaker 1: bought activity and social media for several years, including until 484 00:27:02,080 --> 00:27:06,080 Speaker 1: Musk took over, found that automated inauthentic social media accounts 485 00:27:06,119 --> 00:27:10,800 Speaker 1: so like really botspots remained active on the platform long 486 00:27:10,880 --> 00:27:13,880 Speaker 1: after their research had discovered them, showing that Twitter has 487 00:27:13,920 --> 00:27:18,359 Speaker 1: just not really been effective at suspending bought accounts. And 488 00:27:18,440 --> 00:27:21,120 Speaker 1: with the rise of technology like AI, this is all 489 00:27:21,160 --> 00:27:24,240 Speaker 1: poised to get a lot worse because AI powered bots 490 00:27:24,320 --> 00:27:27,320 Speaker 1: can look and sound more and more like authentic humans. 491 00:27:28,600 --> 00:27:30,520 Speaker 1: So what's at stake with all this? Like why does 492 00:27:30,520 --> 00:27:32,840 Speaker 1: any of this matter? Well, I think the reason I 493 00:27:32,880 --> 00:27:35,199 Speaker 1: get so up in arms about this is that at 494 00:27:35,200 --> 00:27:38,119 Speaker 1: the most basic level, people ought to be able to 495 00:27:38,160 --> 00:27:41,560 Speaker 1: trust that the conversation they're having online are authentic, whether 496 00:27:41,560 --> 00:27:45,000 Speaker 1: they're about celebrities like Britney Spears and the failed actors 497 00:27:45,000 --> 00:27:47,639 Speaker 1: like Justin Timber like they end up having relationships with, 498 00:27:48,359 --> 00:27:50,080 Speaker 1: or about who you should be voting for and what 499 00:27:51,000 --> 00:27:53,320 Speaker 1: We should be able to turn to our biggest social 500 00:27:53,359 --> 00:27:59,560 Speaker 1: media platforms to get accurate, thoughtful content and information from humans. Otherwise, 501 00:27:59,640 --> 00:28:03,200 Speaker 1: what is point when you get on social media, especially 502 00:28:03,280 --> 00:28:05,080 Speaker 1: if you're trying to form an opinion about something that 503 00:28:05,160 --> 00:28:08,240 Speaker 1: is complicated or complex or nuanced. You should be able 504 00:28:08,240 --> 00:28:11,639 Speaker 1: to do that without interference by bots secretly trying to 505 00:28:11,640 --> 00:28:13,840 Speaker 1: push you to think a certain way. And I think 506 00:28:13,840 --> 00:28:16,840 Speaker 1: it is related to this overall feeling that more and 507 00:28:16,880 --> 00:28:19,520 Speaker 1: more of the Internet is just a landscape or a 508 00:28:19,560 --> 00:28:23,800 Speaker 1: marketplace for scams and bots, Like what happened to an 509 00:28:23,840 --> 00:28:27,480 Speaker 1: Internet that is actually based on community building and accurate 510 00:28:27,520 --> 00:28:32,159 Speaker 1: information sharing? And I think increasingly the ultimate why of 511 00:28:32,240 --> 00:28:36,720 Speaker 1: inauthentic accounts and bot campaigns might not necessarily just to 512 00:28:36,760 --> 00:28:39,960 Speaker 1: be to change our behavior and sway our opinions, but 513 00:28:40,480 --> 00:28:43,760 Speaker 1: to keep us fearful and distrustful of one another. You know, 514 00:28:43,840 --> 00:28:46,360 Speaker 1: if we're all accusing each other of not just being 515 00:28:46,360 --> 00:28:50,760 Speaker 1: misinformed or wrong, but not even being human. It's like 516 00:28:50,920 --> 00:28:52,680 Speaker 1: I don't have to listen to what you think, or 517 00:28:52,760 --> 00:28:55,640 Speaker 1: engage you or try to understand your perspective if you're 518 00:28:55,760 --> 00:28:58,280 Speaker 1: just a bot. I think it's another way to keep 519 00:28:58,360 --> 00:29:03,360 Speaker 1: us divided, us easier to manipulate, and ultimately we all 520 00:29:03,400 --> 00:29:06,360 Speaker 1: lose out if the Internet becomes just another place to 521 00:29:06,400 --> 00:29:15,680 Speaker 1: manipulate us, divide us, and keep us polarized. Got a 522 00:29:15,720 --> 00:29:17,800 Speaker 1: story about an interesting thing in tech, or just want 523 00:29:17,800 --> 00:29:19,800 Speaker 1: to say hi? You can read us at Hello at 524 00:29:19,800 --> 00:29:22,520 Speaker 1: tangodi dot com. You can also find transcripts for today's 525 00:29:22,520 --> 00:29:24,960 Speaker 1: episode at tenggody dot com. There Are No Girls on 526 00:29:25,000 --> 00:29:27,320 Speaker 1: the Internet was created by me Bridget Todd. It's a 527 00:29:27,360 --> 00:29:30,719 Speaker 1: production of iHeartRadio and Unbossed. Creative. Jonathan Strickland is our 528 00:29:30,720 --> 00:29:34,040 Speaker 1: executive producer. Tarry Harrison is our producer and sound engineer. 529 00:29:34,440 --> 00:29:37,680 Speaker 1: Michael Almado is our contributing producer. I'm your host, Bridget Todd. 530 00:29:38,560 --> 00:29:40,280 Speaker 1: If you want to help us grow, rate and review 531 00:29:40,360 --> 00:29:43,960 Speaker 1: us on Apple Podcasts For more podcasts from iHeartRadio, check 532 00:29:43,960 --> 00:29:46,320 Speaker 1: out the iHeartRadio app, Apple Podcasts, or wherever you get 533 00:29:46,320 --> 00:29:58,120 Speaker 1: your podcasts.