1 00:00:04,160 --> 00:00:05,880 Speaker 1: There Are No Girls on the Internet. As a production 2 00:00:05,920 --> 00:00:12,960 Speaker 1: of My Heart Radio and Unbust Creative, I'm Bridget Tod 3 00:00:13,200 --> 00:00:14,880 Speaker 1: and this is there Are No Girls on the Internet. 4 00:00:17,920 --> 00:00:22,599 Speaker 1: It's that time again. That's right, Happy Spotify rap season 5 00:00:22,640 --> 00:00:26,120 Speaker 1: to all who celebrate. For users of the music streaming 6 00:00:26,120 --> 00:00:28,720 Speaker 1: platform Spotify, this is the time of year when we 7 00:00:28,760 --> 00:00:31,880 Speaker 1: get are wrapped, a visual breakdown of how much and 8 00:00:32,040 --> 00:00:34,280 Speaker 1: what kinds of music we spent the year listening to 9 00:00:35,440 --> 00:00:39,159 Speaker 1: Spotify first debut It's Year in Music in but two 10 00:00:39,240 --> 00:00:42,400 Speaker 1: years later they added statistics about which artists and how 11 00:00:42,440 --> 00:00:46,600 Speaker 1: much time Spotify users spent listening. In twenty nineteen artists. 12 00:00:46,720 --> 00:00:50,320 Speaker 1: Jule Hamm, who was then an Internet Spotify designed the 13 00:00:50,360 --> 00:00:54,000 Speaker 1: pleasing social media friendly layout to communicate this data visually, 14 00:00:54,480 --> 00:00:56,920 Speaker 1: which leads us to the Spotify Wrap that takes over 15 00:00:56,960 --> 00:01:00,320 Speaker 1: our social media feeds this time of year, and it 16 00:01:00,520 --> 00:01:04,200 Speaker 1: really takes over. According to CNN, in the first hour 17 00:01:04,280 --> 00:01:08,319 Speaker 1: of Spotify Wrap release, the Twitter search showed the hashtag 18 00:01:08,400 --> 00:01:13,039 Speaker 1: Spotify wrapped had been used over fourteen thousand times. So 19 00:01:13,240 --> 00:01:16,360 Speaker 1: why do we love Spotify wraps so much? And what 20 00:01:16,440 --> 00:01:19,000 Speaker 1: does it tell us about our online selves? Now we 21 00:01:19,120 --> 00:01:22,360 Speaker 1: know that Spotify relies on algorithms and artificial intelligence to 22 00:01:22,440 --> 00:01:24,880 Speaker 1: surface music that we might like. It's actually one of 23 00:01:24,880 --> 00:01:27,960 Speaker 1: the main draws of Spotify over other music streaming platforms. 24 00:01:28,240 --> 00:01:31,160 Speaker 1: Who doesn't like the idea of a robot curating personalized 25 00:01:31,160 --> 00:01:33,800 Speaker 1: playlists for us while we sleep. But if we're consuming 26 00:01:33,840 --> 00:01:36,600 Speaker 1: music because Spotify's algorithm has picked it for us based 27 00:01:36,600 --> 00:01:39,160 Speaker 1: on our habits and what that algorithm has been able 28 00:01:39,200 --> 00:01:41,720 Speaker 1: to glean about our habits and identity, that means our 29 00:01:41,760 --> 00:01:44,240 Speaker 1: Spotify wrapped isn't so much a reflection of who we 30 00:01:44,319 --> 00:01:46,600 Speaker 1: truly are in our heart of hearts as it is 31 00:01:46,600 --> 00:01:50,000 Speaker 1: a reflection of who we are as defined by Spotify's AI. 32 00:01:50,360 --> 00:01:53,040 Speaker 1: They don't just show me my own tastes, preferences, and identity, 33 00:01:53,080 --> 00:01:55,760 Speaker 1: they also shape them. So in Spotify gives me my 34 00:01:55,800 --> 00:01:57,800 Speaker 1: wrap for the year, they're not just showing me that 35 00:01:57,840 --> 00:02:00,120 Speaker 1: these are the preferences of a thirtysomething black que or 36 00:02:00,160 --> 00:02:04,200 Speaker 1: already woman. It's also kind of creating what that identity means. 37 00:02:04,960 --> 00:02:07,960 Speaker 1: When we post our Spotify wrapped on social media, we're 38 00:02:08,000 --> 00:02:09,960 Speaker 1: trying to tell the world something about who we are, 39 00:02:10,440 --> 00:02:12,560 Speaker 1: and we're looking to see if our tastes are more impressive, 40 00:02:12,720 --> 00:02:15,639 Speaker 1: more cringeing more hip or more refined than our friends. 41 00:02:16,720 --> 00:02:18,960 Speaker 1: And in this way, Spotify has done a great job 42 00:02:19,000 --> 00:02:21,120 Speaker 1: of turning surveillance, the kind of thing that many of 43 00:02:21,160 --> 00:02:24,200 Speaker 1: us would be uncomfortable with in any other context, into 44 00:02:24,200 --> 00:02:28,680 Speaker 1: a fun digital personal branding opportunity. To be clear, I 45 00:02:28,720 --> 00:02:32,080 Speaker 1: am very much not above this, And in this special 46 00:02:32,320 --> 00:02:34,440 Speaker 1: holiday episode of There Are No Girls on the Internet, 47 00:02:34,800 --> 00:02:37,280 Speaker 1: I'm sitting down with my producer Mike to take my 48 00:02:37,480 --> 00:02:39,880 Speaker 1: very first look at my own Spotify Wrapped for the 49 00:02:39,960 --> 00:02:43,080 Speaker 1: year and dig into what it says about my identity 50 00:02:43,120 --> 00:02:46,200 Speaker 1: in this very weird digital age we all find ourselves in. 51 00:02:46,639 --> 00:02:48,400 Speaker 1: I just hope it's not too embarrassing to do in 52 00:02:48,440 --> 00:02:51,679 Speaker 1: this podcast. So I've really got to start this conversation 53 00:02:51,800 --> 00:02:55,480 Speaker 1: by lifting up Jewel Ham, the multimedia visual artist who 54 00:02:55,520 --> 00:02:59,320 Speaker 1: created what we know as Spotify Wrapped today. Spotify Wrapped 55 00:02:59,440 --> 00:03:03,760 Speaker 1: did exist before Jewel, but the kind of Spotify rap 56 00:03:03,840 --> 00:03:06,560 Speaker 1: that we see taking over her social media feeds if 57 00:03:06,560 --> 00:03:10,920 Speaker 1: you're feeding anything like mine, Jewel really did champion when 58 00:03:11,000 --> 00:03:14,120 Speaker 1: she was an intern at Spotify in twenty nineteen, and 59 00:03:14,240 --> 00:03:16,400 Speaker 1: I feel like she really didn't get her do that 60 00:03:16,560 --> 00:03:19,000 Speaker 1: is tail us old as time when it comes to technology. 61 00:03:19,160 --> 00:03:22,680 Speaker 1: Whenever there is something cool or interesting or unique or creative, 62 00:03:23,360 --> 00:03:26,600 Speaker 1: especially if it's not a hard tech thing. Nine times 63 00:03:26,600 --> 00:03:28,840 Speaker 1: out of ten there is a woman and a black 64 00:03:28,880 --> 00:03:31,959 Speaker 1: woman behind it. So shout out to Jewel. Also Howard 65 00:03:32,040 --> 00:03:35,040 Speaker 1: University grad. Uh, So we wouldn't even be having this 66 00:03:35,120 --> 00:03:38,160 Speaker 1: conversation if not for her. It is interesting how the 67 00:03:38,960 --> 00:03:41,680 Speaker 1: Wrapped is such a visual thing when we think about it, 68 00:03:41,680 --> 00:03:45,400 Speaker 1: because it's it's audio, right, we're listening to and it's 69 00:03:45,520 --> 00:03:49,200 Speaker 1: a list of the top songs that were tracks that 70 00:03:49,240 --> 00:03:51,800 Speaker 1: we've listened to. But when I think about rap, I 71 00:03:51,800 --> 00:03:56,240 Speaker 1: think about a list that looks aesthetically good, is tailor 72 00:03:56,320 --> 00:03:58,600 Speaker 1: made for social media that I think that's really what 73 00:03:59,480 --> 00:04:02,200 Speaker 1: made it a thing that people talk about. Oh absolutely, 74 00:04:02,240 --> 00:04:05,200 Speaker 1: And I think like that's something that's so funny about 75 00:04:05,280 --> 00:04:09,520 Speaker 1: Spotify Wrapped. I even feel a little bit weird making 76 00:04:09,520 --> 00:04:12,360 Speaker 1: this episode because I feel like Spotify Wrapped posting it 77 00:04:12,400 --> 00:04:14,880 Speaker 1: on social media is a little bit like that old 78 00:04:14,960 --> 00:04:18,359 Speaker 1: George Carlin bit where my stuff is stuff and everybody 79 00:04:18,360 --> 00:04:21,440 Speaker 1: else's stuff is ship. Like everybody thinks they're Spotify Wraps 80 00:04:21,440 --> 00:04:24,520 Speaker 1: has something interesting or unique about them personally, and then 81 00:04:24,760 --> 00:04:27,480 Speaker 1: nobody actually wants to look at anybody else's Spotify wrapped, 82 00:04:27,600 --> 00:04:29,240 Speaker 1: you're just like, oh, good for you. You listen to 83 00:04:29,240 --> 00:04:32,039 Speaker 1: Taman Paulo, well done. But when you post your own, 84 00:04:32,160 --> 00:04:34,520 Speaker 1: you feel like you're really saying something about who you 85 00:04:34,520 --> 00:04:38,080 Speaker 1: are as a person. And kudos the Spotify because that 86 00:04:38,279 --> 00:04:41,240 Speaker 1: is some really good marketing that that they have been 87 00:04:41,279 --> 00:04:46,640 Speaker 1: able to make it so that people feel so like 88 00:04:46,720 --> 00:04:49,800 Speaker 1: it really says something about who they are when they 89 00:04:49,839 --> 00:04:53,760 Speaker 1: post this on their social media feed. It's true And 90 00:04:53,880 --> 00:04:57,360 Speaker 1: as like a data person, I really thinking about like 91 00:04:57,400 --> 00:05:00,479 Speaker 1: what it said about me, Like my wrapped is crazy. 92 00:05:00,600 --> 00:05:04,480 Speaker 1: This year, I was so surprised at what was there. Uh, 93 00:05:06,400 --> 00:05:08,400 Speaker 1: but I think it was like, a it's not like 94 00:05:08,440 --> 00:05:11,719 Speaker 1: it's a representative sample of what I've been listening to 95 00:05:11,839 --> 00:05:13,560 Speaker 1: during the year. It's a sample of what I've been 96 00:05:13,560 --> 00:05:16,359 Speaker 1: listening to on Spotify. So I was like, what do 97 00:05:16,360 --> 00:05:18,520 Speaker 1: I use Spotify for? Use it to play albums and 98 00:05:18,560 --> 00:05:22,760 Speaker 1: I use it to create stations. I used to hate 99 00:05:22,800 --> 00:05:26,360 Speaker 1: the idea of just trusting an algorithm to choose media 100 00:05:26,480 --> 00:05:28,640 Speaker 1: for me to listen to, but I guess at some 101 00:05:28,680 --> 00:05:31,280 Speaker 1: point during the pandemic, I just completely said fuck it 102 00:05:31,360 --> 00:05:33,600 Speaker 1: and went with it. And it's actually great, Like they 103 00:05:33,640 --> 00:05:37,920 Speaker 1: do an amazing job of just creating a playlist of 104 00:05:37,960 --> 00:05:40,560 Speaker 1: songs that I want to hear based on something that 105 00:05:40,600 --> 00:05:42,600 Speaker 1: I put up there. Okay, so I have so much 106 00:05:42,640 --> 00:05:44,800 Speaker 1: to say about this. First of all, it is so funny. 107 00:05:44,880 --> 00:05:48,560 Speaker 1: So as we know, Spotify is definitely algorithmically driven, and 108 00:05:48,600 --> 00:05:51,599 Speaker 1: so I was like, oh, uh, looks like you've spent 109 00:05:51,680 --> 00:05:54,040 Speaker 1: a lot of time listening to sad As Bo Burnham 110 00:05:54,160 --> 00:05:56,320 Speaker 1: songs this year, And it's like, that's what you fucking 111 00:05:56,360 --> 00:05:59,000 Speaker 1: surface me algorithm. You know, you know, fit's what I 112 00:05:59,040 --> 00:06:01,279 Speaker 1: listened to because you've been servicing it to be this 113 00:06:01,320 --> 00:06:05,680 Speaker 1: whole year. So absolutely true. It is the it's like 114 00:06:06,360 --> 00:06:09,320 Speaker 1: the kind of cheeky little like, oh, it looks like 115 00:06:09,400 --> 00:06:11,400 Speaker 1: you spend a lot of time listening to BA. It's 116 00:06:11,440 --> 00:06:14,760 Speaker 1: like they know, because they're elgo with them services surfaces 117 00:06:14,800 --> 00:06:18,160 Speaker 1: it to you. So absolutely true. And I do think 118 00:06:18,200 --> 00:06:20,400 Speaker 1: this year, you know, I'm going to dig into my 119 00:06:20,400 --> 00:06:23,320 Speaker 1: own Spotify raft in a minute, which I hope is 120 00:06:23,440 --> 00:06:29,960 Speaker 1: not embarrassing. But I do think in pre pandemic, I 121 00:06:30,000 --> 00:06:34,120 Speaker 1: would definitely be excited to show my Spotify raft on 122 00:06:34,160 --> 00:06:36,360 Speaker 1: social media. Even though I just said, like people post 123 00:06:36,400 --> 00:06:38,400 Speaker 1: there so their Spotify raft on social media and does 124 00:06:38,440 --> 00:06:41,080 Speaker 1: anybody really care? I definitely was not above that and 125 00:06:41,120 --> 00:06:43,080 Speaker 1: definitely did it and like we'll do it this year 126 00:06:43,120 --> 00:06:48,560 Speaker 1: as well. But I pre pandemic, I was much prouder 127 00:06:48,600 --> 00:06:51,200 Speaker 1: of the music because I feel like it said something 128 00:06:51,360 --> 00:06:57,320 Speaker 1: different about me. I feel like, and especially weird years, 129 00:06:57,680 --> 00:06:59,560 Speaker 1: I don't know what they say about me. I feel 130 00:06:59,560 --> 00:07:01,880 Speaker 1: like it's like, Wow, what a weird year I've had. 131 00:07:02,760 --> 00:07:04,280 Speaker 1: I can't even tell you if I listened to a 132 00:07:04,360 --> 00:07:07,400 Speaker 1: lot of music, some of the stats on my Spotify wrap, 133 00:07:07,400 --> 00:07:08,840 Speaker 1: and I'm like, did I really listen to that song? 134 00:07:08,880 --> 00:07:11,520 Speaker 1: This much? Like that seems weird. I just think it's 135 00:07:11,520 --> 00:07:14,200 Speaker 1: a weird year. And as we experience things in our 136 00:07:14,240 --> 00:07:18,240 Speaker 1: real life like the pandemic and the insurrection and like 137 00:07:18,640 --> 00:07:22,800 Speaker 1: other real life crises, it is so interesting to see 138 00:07:23,560 --> 00:07:28,320 Speaker 1: how that is reflected through data points that we consumed 139 00:07:28,520 --> 00:07:32,280 Speaker 1: on a digital interface. It's very interesting and I feel like, yeah, 140 00:07:32,320 --> 00:07:35,560 Speaker 1: pre pandemic, I would have said my Spotify wrap says 141 00:07:35,600 --> 00:07:37,760 Speaker 1: something about me. I don't know what it is, but 142 00:07:37,880 --> 00:07:41,040 Speaker 1: something this year especially, I'm like, I don't even know 143 00:07:41,080 --> 00:07:43,400 Speaker 1: if I can if I trust this because it seems 144 00:07:43,480 --> 00:07:45,880 Speaker 1: so out of whack. With how I feel and who 145 00:07:45,920 --> 00:07:49,280 Speaker 1: I am. It's been a weird year. Yeah, it's been 146 00:07:49,320 --> 00:07:55,360 Speaker 1: a weird year. So should I get into my Spotify raft? Yeah, 147 00:07:55,400 --> 00:07:58,160 Speaker 1: I think you should. I think uh, you know, it's 148 00:07:58,840 --> 00:08:00,760 Speaker 1: for at the end of the year to December. It's 149 00:08:00,840 --> 00:08:03,840 Speaker 1: nice to take a break from a lot of the 150 00:08:03,880 --> 00:08:06,600 Speaker 1: negative stuff we often talk about and let's just talk 151 00:08:06,600 --> 00:08:08,800 Speaker 1: about some music. Let's talk about some music. So this 152 00:08:08,880 --> 00:08:10,720 Speaker 1: is I'm gonna dig into this. I guess we should 153 00:08:11,280 --> 00:08:14,000 Speaker 1: get into it. Yeah, if you recognize that that it's 154 00:08:14,040 --> 00:08:20,360 Speaker 1: actually my first number one, Okay, I thought it was clever, 155 00:08:21,840 --> 00:08:24,480 Speaker 1: So I'm gonna I'm gonna give you all my top three. 156 00:08:25,000 --> 00:08:28,760 Speaker 1: My number one play a song of one is Doja 157 00:08:28,800 --> 00:08:32,920 Speaker 1: Cats get into It, And honestly, I'm not mad at it. 158 00:08:33,080 --> 00:08:36,640 Speaker 1: I love Doja Cat. She could be a little problematic, 159 00:08:36,720 --> 00:08:40,320 Speaker 1: but you know, she's my she I'm I'm I like her. Uh, 160 00:08:40,520 --> 00:08:44,160 Speaker 1: get into It is definitely um a song that I 161 00:08:44,160 --> 00:08:47,760 Speaker 1: I listened to pretty frequently and it's not surprising to 162 00:08:47,760 --> 00:08:50,000 Speaker 1: me that it's I'm a little surprised is my number one, 163 00:08:50,440 --> 00:08:52,959 Speaker 1: but I'm not mad at it. I'll take it. I 164 00:08:53,000 --> 00:08:56,560 Speaker 1: feel like Dojaquette has songs that just get in your head. 165 00:08:57,840 --> 00:09:02,280 Speaker 1: That is absolutely true. I spent probably a year talking 166 00:09:02,320 --> 00:09:06,720 Speaker 1: about how I wasn't how I wasn't a cat, I 167 00:09:06,800 --> 00:09:09,640 Speaker 1: was a cow move if you know that do Jaquette song, 168 00:09:09,880 --> 00:09:13,680 Speaker 1: So definitely do Jaquete earworms. Does she have a lot 169 00:09:13,720 --> 00:09:16,640 Speaker 1: going on on TikTok o her being my number one? 170 00:09:16,880 --> 00:09:20,320 Speaker 1: It's definitely the TikTok influence on my listening habits. A 171 00:09:20,320 --> 00:09:22,640 Speaker 1: couple of other songs that are big on my Spotify Wrapped, 172 00:09:23,000 --> 00:09:26,719 Speaker 1: Pink Panthers, another musician who was huge on TikTok who 173 00:09:26,760 --> 00:09:29,960 Speaker 1: was in my Spotify wrapped but the Anxiety and Willow 174 00:09:30,000 --> 00:09:34,559 Speaker 1: Smith that song meet Me at Our spot huge on TikTok. 175 00:09:34,679 --> 00:09:36,719 Speaker 1: I'm sorry for just singing. I know that probably sounded bad, 176 00:09:36,800 --> 00:09:41,600 Speaker 1: but uh yeah, I definitely see the TikTok influence on 177 00:09:41,760 --> 00:09:45,200 Speaker 1: my uh Spotify wrap Definitely a lot of songs I 178 00:09:45,200 --> 00:09:48,400 Speaker 1: heard I didn't know before I heard on TikTok and 179 00:09:48,440 --> 00:09:50,920 Speaker 1: then found on Spotify and saved and listened to over 180 00:09:50,960 --> 00:09:54,520 Speaker 1: and over and over again in one apparently so so 181 00:09:54,600 --> 00:09:59,160 Speaker 1: now we've got the TikTok algorithm what you see which 182 00:09:59,200 --> 00:10:03,199 Speaker 1: then drives listen to Spotify. Well yeah, I often feel 183 00:10:03,200 --> 00:10:05,719 Speaker 1: like the algorithms are working in tandem. It is like 184 00:10:05,840 --> 00:10:10,040 Speaker 1: the TikTok algorithms. Algorithms got me, the Spotify algorithms got me. 185 00:10:10,080 --> 00:10:13,240 Speaker 1: It's like like an intersecting algorithm. I'm just a slave 186 00:10:13,320 --> 00:10:15,360 Speaker 1: to whatever they surface to me, and I'm just like, yes, 187 00:10:15,800 --> 00:10:20,760 Speaker 1: give me more. Yes, that'll be a new area of 188 00:10:20,840 --> 00:10:25,680 Speaker 1: intersectionality of like how the algorithms interesting. Honestly, I'm speaking 189 00:10:25,679 --> 00:10:28,480 Speaker 1: of bow Burnham. He's got this line in Inside where 190 00:10:28,520 --> 00:10:31,240 Speaker 1: he's like, Daddy made some content just for you, so 191 00:10:31,320 --> 00:10:34,960 Speaker 1: open wide. That's kind of how I feel, just like, yes, 192 00:10:35,080 --> 00:10:38,760 Speaker 1: give it to me, give it to me, all right. 193 00:10:38,840 --> 00:10:42,400 Speaker 1: So let's get into my number two on my Spotify raft, 194 00:10:42,440 --> 00:10:45,920 Speaker 1: which is a little bit of an outlier. Um. It 195 00:10:46,200 --> 00:10:50,360 Speaker 1: is the song Jet by Wings. If you are a 196 00:10:50,400 --> 00:10:52,880 Speaker 1: listener who is in your twenties or early thirties, you're 197 00:10:52,920 --> 00:10:56,000 Speaker 1: probably like, I don't know what this is. I think 198 00:10:56,040 --> 00:10:59,280 Speaker 1: people know Wings. I don't. I don't think people know Wings. 199 00:11:00,080 --> 00:11:02,720 Speaker 1: So for people who don't know Wings, Wings is a 200 00:11:03,400 --> 00:11:06,120 Speaker 1: It was an offshoot ban of the Beatles started by 201 00:11:06,160 --> 00:11:10,920 Speaker 1: Paul Paul McCartney. Sir Paul McCartney, Sir Paul McCartney in 202 00:11:10,960 --> 00:11:14,240 Speaker 1: the eighties. The song came out in so definitely not 203 00:11:14,440 --> 00:11:17,960 Speaker 1: a current hit. I have a soft spot for it. 204 00:11:18,000 --> 00:11:21,319 Speaker 1: I feel like people get down on Wings. I really 205 00:11:21,320 --> 00:11:24,400 Speaker 1: have a soft spot for Wings. And fun fact, this 206 00:11:24,520 --> 00:11:27,319 Speaker 1: was not the only Wing song to make an appearance 207 00:11:27,360 --> 00:11:29,920 Speaker 1: on a Spotify raft, which I don't even really know 208 00:11:29,960 --> 00:11:32,679 Speaker 1: what to make of. I think you really like Wings, 209 00:11:33,160 --> 00:11:35,760 Speaker 1: I guess so. And also, when I as a kid, 210 00:11:35,800 --> 00:11:37,920 Speaker 1: my dad would sing this song to me because he 211 00:11:38,000 --> 00:11:42,240 Speaker 1: said that Jet was a nickname for Bridget. It is not. 212 00:11:42,480 --> 00:11:44,320 Speaker 1: The song is not about a girl named Bridget, but 213 00:11:45,720 --> 00:11:47,480 Speaker 1: I believed that when I was young, I could see 214 00:11:47,480 --> 00:11:50,160 Speaker 1: it would be appealing to think that it was that 215 00:11:50,600 --> 00:11:55,880 Speaker 1: apologist singing to a girl named Bridget. Yeah, it really was. 216 00:11:55,960 --> 00:11:57,600 Speaker 1: And so it's just it's a song that has us. 217 00:11:57,679 --> 00:12:00,640 Speaker 1: It's it's I'm almost embarrassed that it's as high as 218 00:12:00,679 --> 00:12:02,720 Speaker 1: it is. But you know what, can I say? I 219 00:12:02,760 --> 00:12:05,000 Speaker 1: like Wings? I like you know what? And I got 220 00:12:05,000 --> 00:12:07,040 Speaker 1: two Wings songs on the on the Spotify wrapped and 221 00:12:07,040 --> 00:12:11,800 Speaker 1: I'm not ashamed. Yeah, nothing to be ashamed. It's a pandemic. 222 00:12:12,600 --> 00:12:14,960 Speaker 1: It's a pandemic. Listen to as much Wings as you 223 00:12:15,000 --> 00:12:17,360 Speaker 1: need to get you through this pandemic. Listen know whatever 224 00:12:17,400 --> 00:12:19,960 Speaker 1: you want, like, don't be ashamed of your Spotify wrap 225 00:12:20,040 --> 00:12:22,160 Speaker 1: Listen to whatever you want in this pandemic, whatever you 226 00:12:22,200 --> 00:12:24,560 Speaker 1: need to get you through. My number three song on 227 00:12:24,559 --> 00:12:29,760 Speaker 1: Spotify wraps again is another TikTok classic, Nobody by meets 228 00:12:30,000 --> 00:12:33,440 Speaker 1: mitsky Uh. That was a little bit of a TikTok meme. 229 00:12:33,760 --> 00:12:37,120 Speaker 1: And again I'm I'm kind of grateful for TikTok as 230 00:12:37,120 --> 00:12:39,080 Speaker 1: I've gotten into it as a platform. Look, I feel 231 00:12:39,080 --> 00:12:41,880 Speaker 1: like I hear new music all the time that I 232 00:12:41,880 --> 00:12:46,000 Speaker 1: wouldn't have heard, you know, being someone who is we'll 233 00:12:46,040 --> 00:12:50,000 Speaker 1: say a bit older than the typical TikTok demographic, getting 234 00:12:50,040 --> 00:12:52,400 Speaker 1: insight into different kinds of music has been really interesting. 235 00:12:52,440 --> 00:12:55,600 Speaker 1: So yeah, of my top three, two of them are 236 00:12:55,640 --> 00:13:01,240 Speaker 1: definitely TikTok, TikTok algorithm chosen for me. You have to 237 00:13:01,240 --> 00:13:03,960 Speaker 1: give me some tips on how to use TikTok because 238 00:13:04,440 --> 00:13:07,920 Speaker 1: mine is entirely animals, which like, I like animals, I 239 00:13:08,040 --> 00:13:11,640 Speaker 1: like funny animal content, but I'd like something else in there. 240 00:13:11,679 --> 00:13:14,640 Speaker 1: But it doesn't serve me any music. How do how 241 00:13:14,679 --> 00:13:17,040 Speaker 1: do you get to serve your music? I don't know. 242 00:13:17,120 --> 00:13:19,800 Speaker 1: I guess I just the algorithm. Gods have have chosen 243 00:13:19,840 --> 00:13:23,439 Speaker 1: me to get those A cat all the time on 244 00:13:23,520 --> 00:13:29,439 Speaker 1: TikTok so. A couple of other things. Things to note 245 00:13:29,480 --> 00:13:32,400 Speaker 1: from my Spotify rapped one is that I was definitely 246 00:13:32,440 --> 00:13:37,199 Speaker 1: all my black girl feeling myself ship. Uh, very overrepresented 247 00:13:37,240 --> 00:13:40,040 Speaker 1: person on my town. My Spotify rapp was Megan the Stallion, 248 00:13:40,800 --> 00:13:44,599 Speaker 1: specifically Megan the Stallion's thought shit And I remember, I 249 00:13:44,600 --> 00:13:47,040 Speaker 1: don't know if this is weird to say when there 250 00:13:47,040 --> 00:13:49,520 Speaker 1: are no girls on the internet. One the Shorty Award 251 00:13:49,720 --> 00:13:52,960 Speaker 1: for our mini series and Disinformation, that was really when 252 00:13:53,120 --> 00:13:57,640 Speaker 1: Megan the Stallion's Thoughts ship creeped into my Spotify plays more. 253 00:13:57,760 --> 00:14:00,640 Speaker 1: She has this line where she says, bitch on the 254 00:14:00,640 --> 00:14:04,720 Speaker 1: ship per the Recording Academy, and when we won the 255 00:14:04,760 --> 00:14:07,079 Speaker 1: Shorty Award, that really was like a line that I 256 00:14:07,080 --> 00:14:10,120 Speaker 1: identified with this, like, oh we uh also are the 257 00:14:10,160 --> 00:14:15,720 Speaker 1: ship per Recording Academy or a Shorty Academy. Yeah, I'll 258 00:14:15,720 --> 00:14:21,320 Speaker 1: take it. And it really makes sense because my according 259 00:14:21,320 --> 00:14:24,600 Speaker 1: to Spotify Rapped, the mood for my music that I 260 00:14:24,600 --> 00:14:27,960 Speaker 1: listened to one was confident and bold and so I 261 00:14:28,160 --> 00:14:29,720 Speaker 1: got I think I got a Megan the Stallion to 262 00:14:29,800 --> 00:14:45,000 Speaker 1: thank for them. Let's take a quick break cut back. 263 00:14:45,680 --> 00:14:48,480 Speaker 1: Another artist that I was not surprised to see but 264 00:14:48,560 --> 00:14:50,760 Speaker 1: makes a lot of sense was d m X. I 265 00:14:50,880 --> 00:14:53,440 Speaker 1: was a huge DMX man when I was a kid. 266 00:14:53,800 --> 00:14:58,080 Speaker 1: Loves d m X, and DMX died and I will 267 00:14:58,160 --> 00:15:00,880 Speaker 1: never forget it. I was the day where I had 268 00:15:00,920 --> 00:15:03,600 Speaker 1: back to back to back to back meetings and calls 269 00:15:03,600 --> 00:15:06,440 Speaker 1: and zooms, like professional meetings, and I got the push 270 00:15:06,480 --> 00:15:09,600 Speaker 1: notification on my phone that DMX had passed away, and 271 00:15:10,200 --> 00:15:12,200 Speaker 1: all through these meetings where I had to have my 272 00:15:12,360 --> 00:15:15,920 Speaker 1: like fake zoom voice on, you know that fake enthusiastic 273 00:15:16,000 --> 00:15:17,640 Speaker 1: voice that you do when you're on zooms and you 274 00:15:17,920 --> 00:15:20,080 Speaker 1: kind of want to die, but you keep got three more. 275 00:15:20,800 --> 00:15:25,320 Speaker 1: I had to keep that voice on, wanting so badly 276 00:15:25,360 --> 00:15:27,400 Speaker 1: to do nothing but blast DMX. And I remember when 277 00:15:27,440 --> 00:15:30,560 Speaker 1: the final call happened at like six thirty and I 278 00:15:30,600 --> 00:15:33,520 Speaker 1: was like, okay, thanks guys, and then I turned off 279 00:15:33,520 --> 00:15:36,520 Speaker 1: my computer and I blasted X Gonna give It to You, 280 00:15:36,520 --> 00:15:38,680 Speaker 1: which is my favorite DMX song. And so it's not 281 00:15:38,720 --> 00:15:41,360 Speaker 1: surprising to be the DMX is so heavily in my 282 00:15:41,400 --> 00:15:44,520 Speaker 1: Spotify rap because I remember when he died and it really. 283 00:15:44,520 --> 00:15:47,080 Speaker 1: It hit me hard and I I listened to a 284 00:15:47,080 --> 00:15:49,760 Speaker 1: lot of DMX. I guess yeah. I feel like everybody 285 00:15:49,760 --> 00:15:51,640 Speaker 1: in America was listening to d m X after that. 286 00:15:52,760 --> 00:15:54,280 Speaker 1: Was X Gonna give It to you in a movie? 287 00:15:54,960 --> 00:15:57,280 Speaker 1: I feel like I know exactly what you're thinking of. 288 00:15:57,400 --> 00:15:59,520 Speaker 1: It's in an episode of Rick and Morty. That's why 289 00:15:59,600 --> 00:16:03,440 Speaker 1: you're thinking of It wasn't in a movie. It was, 290 00:16:03,520 --> 00:16:06,800 Speaker 1: definitely it probably was, but I know I know what 291 00:16:06,840 --> 00:16:11,160 Speaker 1: you're remembering it from. Okay. Another song on my Spotify 292 00:16:11,160 --> 00:16:12,800 Speaker 1: wrapped list that I was not surprised to see at 293 00:16:12,840 --> 00:16:15,800 Speaker 1: all is Daft Punks Georgio. That is a song that 294 00:16:15,840 --> 00:16:20,080 Speaker 1: I deeply, deeply, deeply funk with whenever I have a 295 00:16:20,080 --> 00:16:23,360 Speaker 1: podcast interview where I want to edit it to bring 296 00:16:23,400 --> 00:16:25,920 Speaker 1: a certain kind of tenderness or a certain kind of beauty, 297 00:16:26,040 --> 00:16:28,920 Speaker 1: or a certain kind of meaning. I listened to that 298 00:16:29,040 --> 00:16:33,840 Speaker 1: song because it's a song of a uh really famous 299 00:16:33,880 --> 00:16:37,280 Speaker 1: producer basically telling his life story of how he got 300 00:16:37,320 --> 00:16:42,600 Speaker 1: into music, how he got into producing, and it's so 301 00:16:42,920 --> 00:16:46,360 Speaker 1: lovingly edited that it's such a good reminder of the 302 00:16:46,480 --> 00:16:49,920 Speaker 1: power of listening to someone tell their own story in 303 00:16:50,000 --> 00:16:53,000 Speaker 1: their own words. My whole life. I've always had a 304 00:16:53,080 --> 00:16:56,360 Speaker 1: thing for listening to someone tell their own story and 305 00:16:56,400 --> 00:16:58,400 Speaker 1: their own words, and I feel like that was a 306 00:16:58,480 --> 00:17:00,360 Speaker 1: much of a In terms of music, that was a 307 00:17:00,440 --> 00:17:02,200 Speaker 1: much bigger thing, kind of back in the day in 308 00:17:02,200 --> 00:17:05,560 Speaker 1: the nineties. You know, spoken word. I've always had a 309 00:17:05,600 --> 00:17:09,240 Speaker 1: song if it's someone giving a speech or explaining something 310 00:17:09,280 --> 00:17:11,440 Speaker 1: with a little bit of like sound design around it, 311 00:17:11,920 --> 00:17:13,920 Speaker 1: love it. A couple of other examples of that would 312 00:17:13,920 --> 00:17:17,760 Speaker 1: be UM, Mutual Slump by DJ Shadow, where it's DJ 313 00:17:17,840 --> 00:17:20,280 Speaker 1: Shadows then girlfriend is talking about how much she loved 314 00:17:20,240 --> 00:17:23,680 Speaker 1: remember Zannah do the roller skating? How much she loved Zanna? 315 00:17:23,760 --> 00:17:27,040 Speaker 1: Do you love it? Um, Everybody's free to wear sunscreen 316 00:17:27,119 --> 00:17:31,840 Speaker 1: the Boss Learnman thing where he's like, no matter okay, graduates, 317 00:17:31,840 --> 00:17:35,560 Speaker 1: no matter what you do? Where sunscreen? Uh. Little fluffy 318 00:17:35,640 --> 00:17:39,480 Speaker 1: Clouds by the Orb where it's just a beautiful um, 319 00:17:40,560 --> 00:17:43,199 Speaker 1: a beautiful voice over of a woman describing what the 320 00:17:43,200 --> 00:17:45,560 Speaker 1: clouds and the skies were like when she was young. 321 00:17:45,880 --> 00:17:48,239 Speaker 1: You know, when people make podcasts, if you're doing an 322 00:17:48,240 --> 00:17:51,199 Speaker 1: interview in the field, you often have to get levels, 323 00:17:51,240 --> 00:17:53,679 Speaker 1: and so you might ask the subject a question, you know, 324 00:17:53,680 --> 00:17:55,600 Speaker 1: what did you ever breakfast? So that you can check 325 00:17:55,640 --> 00:17:58,879 Speaker 1: the levels. And I always from that song. I always 326 00:17:58,880 --> 00:18:02,159 Speaker 1: asked what because of that song? I always asked what 327 00:18:02,200 --> 00:18:04,199 Speaker 1: were the skies like when you were young? And the 328 00:18:04,280 --> 00:18:06,240 Speaker 1: kind of answers that you get when you ask somebody 329 00:18:06,320 --> 00:18:08,399 Speaker 1: what were this guy's like when you were young, It's 330 00:18:08,440 --> 00:18:11,879 Speaker 1: really interesting. I was once interviewing a French migrant and 331 00:18:11,960 --> 00:18:14,280 Speaker 1: he said, I never saw these guys. I don't know, 332 00:18:14,560 --> 00:18:16,600 Speaker 1: and I remember thinking that was such a powerful answer. 333 00:18:20,840 --> 00:18:32,280 Speaker 1: More after a quick break, let's get right back into it. 334 00:18:33,359 --> 00:18:38,200 Speaker 1: I have always had a thing for someone explaining something 335 00:18:38,240 --> 00:18:41,600 Speaker 1: that's meaningful to them with beautiful sound design. It will 336 00:18:41,600 --> 00:18:44,800 Speaker 1: always be something that like just getting like feels right 337 00:18:44,840 --> 00:18:47,760 Speaker 1: in my brain. Yeah, those songs were all like podcast. 338 00:18:48,760 --> 00:18:52,960 Speaker 1: I feel like spoken word tracks of the nineties were 339 00:18:53,040 --> 00:18:57,560 Speaker 1: the first podcast, like like right in, let me know 340 00:18:57,600 --> 00:19:00,560 Speaker 1: what you think. This is my big theory. Boken word 341 00:19:00,600 --> 00:19:04,440 Speaker 1: tracks of the nineties were the first podcast. Somebody proved 342 00:19:04,440 --> 00:19:07,200 Speaker 1: me wrong? Can you give us a little taste of 343 00:19:07,280 --> 00:19:18,159 Speaker 1: year Spotify raft mine? Oh no, I don't know immediately, No, No, 344 00:19:18,520 --> 00:19:22,639 Speaker 1: it's what was on there. I feel like the early 345 00:19:22,680 --> 00:19:26,160 Speaker 1: two thousand's are gonna call me and want their music back, 346 00:19:26,640 --> 00:19:31,600 Speaker 1: So let's just skip it. So for better or for worse, 347 00:19:31,920 --> 00:19:34,840 Speaker 1: that is what my Spotify says, I spent my year consuming, 348 00:19:35,320 --> 00:19:37,240 Speaker 1: and to me it kind of shows that I was 349 00:19:37,280 --> 00:19:39,840 Speaker 1: bouncing back and forth between the new, you know, the 350 00:19:39,880 --> 00:19:43,240 Speaker 1: TikTok field songs taking over my earbuds, and the old, 351 00:19:43,560 --> 00:19:45,720 Speaker 1: the reliable songs I grew up with that have stuck 352 00:19:45,760 --> 00:19:48,280 Speaker 1: with me. I think it's a fair reflection of the 353 00:19:48,320 --> 00:19:50,639 Speaker 1: year I've had a little all over the place, to 354 00:19:50,680 --> 00:19:53,960 Speaker 1: be sure, and perpetually caught between looking to the familiar, 355 00:19:54,000 --> 00:19:56,679 Speaker 1: to fine comfort, while also trying to look toward the future, 356 00:19:56,880 --> 00:20:00,119 Speaker 1: whatever that holds. So this is where you come in. 357 00:20:00,800 --> 00:20:03,840 Speaker 1: What was your Spotify wraps like this year and what 358 00:20:03,920 --> 00:20:06,399 Speaker 1: do you feel like it says about you? Give our 359 00:20:06,520 --> 00:20:09,920 Speaker 1: voicemail a call at five seven one three one oh 360 00:20:09,960 --> 00:20:12,760 Speaker 1: eighteen fifty nine and leave us a voicemail and let 361 00:20:12,800 --> 00:20:15,520 Speaker 1: us know and you might even hear yourself on a 362 00:20:15,560 --> 00:20:17,600 Speaker 1: future episode of There Are No Girls on the Internet. 363 00:20:18,320 --> 00:20:21,840 Speaker 1: That's five seven one three one oh eight fifty nine. 364 00:20:27,240 --> 00:20:29,200 Speaker 1: Got a story about an interesting thing in tech, or 365 00:20:29,320 --> 00:20:31,159 Speaker 1: just want to say hi? You can reach us at 366 00:20:31,200 --> 00:20:33,480 Speaker 1: Hello at tang Godi dot com. You can also find 367 00:20:33,480 --> 00:20:36,359 Speaker 1: transcripts for today's episode at tangodi dot com. There Are 368 00:20:36,359 --> 00:20:38,520 Speaker 1: No Girls on the Internet was created by me Bridget Todd. 369 00:20:38,920 --> 00:20:41,919 Speaker 1: It's a production of iHeart Radio and Unboss creative Jonathan 370 00:20:42,000 --> 00:20:45,000 Speaker 1: Strickland as our executive producer. Tara Harrison is our producer 371 00:20:45,000 --> 00:20:48,520 Speaker 1: and sound engineer. Michael Amato is our contributing producer. I'm 372 00:20:48,520 --> 00:20:51,400 Speaker 1: your host, Bridget Todd. If you want to help us grow, 373 00:20:51,600 --> 00:20:54,760 Speaker 1: rate and review us on Apple Podcasts. For more podcasts 374 00:20:54,760 --> 00:20:56,800 Speaker 1: from my heart Radio, check out the iHeart Radio app, 375 00:20:56,840 --> 00:21:02,560 Speaker 1: Apple podcast or wherever you get your podcasts. And then 376 00:21:03,000 --> 00:21:08,679 Speaker 1: I have been home with and then I have been 377 00:21:08,880 --> 00:21:09,520 Speaker 1: he with