vastava
Non notée
Année : 2020
Nombre de saisons : 1
Durée moyenne d'un épisode : 18 minutes
Genre(s) :
Another commentary + video essay gal, but I use data analysis for the bulk of my arguments. I bring my data science background to answer questions that are mostly stupid, sometime serious on this channel — videos on analyzing youtube drama, Internet culture, film, television and anything else that's of interest to me! I try to post tutorials and relevant code for each of my projects on Medium: https://medium.com/@vastava
Saisons
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Épisodes
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Épisode 1 - Most viewed Wikipedia article by day (Jan - July 2020)
19 juillet 2020
Built in d3.js! Watch the second part: https://www.youtube.com/watch?v=XP0Xft-MNqs&feature=youtu.be&ab_channel=vastava Tutorial on how I scraped the data and built the chart: https://towardsdatascience.com/scraping-wikipedia-page-views-to-make-a-2020-rewind-c9bcac97fa38 Data source: Wikipedia API Music: Night Out - LiQWYD
Épisode 2 - Proving the Eragon film sucks with data science *sentiment analysis*
29 août 2020
Using sentiment analysis to compare the book and film adaptation of Eragon! reinforcing that's it's just the worst. 0:00 Intro 0:40 Why I chose Eragon for this analysis 2:25 What is Sentiment Analysis? 3:06 My methodology 4:15 Set-up for comparison 6:50 Sentiment comparison between book and film 17:23 Intro for emotional analysis 17:56 My methodology (again) 19:44 Analyzing the emotionality of book and film 20:57 Just end it already #vastava #eragon #datascience
Épisode 3 - Is Sonic fan art disturbing? *data analysis*
5 septembre 2020
I analyzed Sonic the Hedgehog fan art with data science! Thank you to LargeStupidity for pointing out that DeviantArt only implemented tags in 2014, which might have affected the scraping process. This likely explains the distribution of artworks that I showed at 5:50. Link to images, code and write-up: https://towardsdatascience.com/analyzing-sonic-fan-art-with-data-science-fddcaa8bbb68 0:00 Intro 1:34 The strange world of Sonic fan art 2:15 How much Sonic fan art is there? 2:30 Comparing Sonic to other characters and their fan art 4:13 Why is Sonic so popular? 5:06 My methodology for this project 5:58 Looking over the most viewed artworks in the dataset 13:42 I’m a liar!! 14:12 Analyzing the tags of artworks 15:30 The *furry* discussion 17:33 Continued network analysis 20:00 Conclusions 21:38 Jeez just end it already #vastava #sonic #fanart
Épisode 4 - The cultural impact of Content Cop *data analysis*
14 septembre 2020
FLASH WARNING starts at 14 minutes and lasts 7 seconds (idk why it glitched out, so sorry to anyone this affects) I included a hint for my next video topic at the end of this video. Solve it and I guess you'll get my respect? And bragging rights of course. Link to write-up and code for this project: https://medium.com/@vastava.writes/how-to-scrape-socialblade-for-youtube-subscription-data-ec7c4bde6933 0:00 Intro 0:36 What is Content Cop? 1:24 Research question + Methodology 2:39 General analysis of Content Cop 5:04 Individual channel analysis 5:16 Jinx 6:32 The Fine Brothers 7:32 Keemstar 8:52 HowToPRANKItUp 9:23 LeafyIsHere 10:50 Tana Mongeau 11:23 Brief aside on the n word 12:22 Tana continued 13:52 Ricegum 14:19 iDubbbz 14:32 Why has he stopped making content cops? 15:52 Wrap up Thoughts, comments, concerns? reach me at vastava.writes@gmail.com #vastava #idubbbz #contentcop
Épisode 5 - TikTok mansions during pandemic: whY *i made a map*
5 octobre 2020
I analyzed the exploding phenomenon of content mansions, and the implications of large gatherings, travel and group living given that we are in a PANDEMIC. Link to map: https://vastava.github.io/tiktok-mansions-map/story.html Code: https://github.com/vastava/tiktok-mansions-map 0:00 Intro 0:41 What is a content house? 1:29 Methodology 4:09 THE MAP (and the LA scene) 4:38 Influencer Parties and Content Houses (Alpha House) 5:30 These mansions are super spreaders (Alpha House/V@ult House) 7:51 Content Houses are becoming more corporate 8:26 Content House DRAMA 9:26 Who are these agencies?? (Not a Content House) 10:06 The Clubhouse Lore 11:22 Content House networks + collabs 11:58 Non-LA mansions 12:27 Some are responsible 12:46 International Content Houses 13:04 Traveling? (Jet House) 13:26 Public backlash (G.O.A.T. House) 13:47 Where are the parents?? (Byte Squad) 14:32 Criticism + Accountability 15:11 Entrepreneurs lol (Launch House) 15:25 Why does this matter? 15:59 You’re just making kids sad + jealous 16:57 I’m not hating on the influencers 18:17 It’s not just the parties 19:04 Wrap up Other videos you might enjoy: The cultural impact of Content Cop *data analysis*: https://www.youtube.com/watch?v=EJpJWYTdtPc Is Sonic fan art disturbing? *data analysis*: https://www.youtube.com/watch?v=EJpJWYTdtPc #vastava #TikTok #InfluencerParties #ContentHouse
Épisode 6 - D'Angelo Wallace is the new (better) content cop *data analysis*
15 octobre 2020
I analyzed the sudden rise of D'Angelo Wallace, and how it is almost eerily reminiscent of iDubbbz' rise to fame in 2016. Tiffany's video on the rise of D'Angelo Wallace: https://www.youtube.com/watch?v=op7lvNWMV0M Code I used to scrape the data from SocialBlade: https://medium.com/swlh/how-to-scrape-socialblade-for-youtube-subscription-data-ec7c4bde6933 0:00 Intro + thank you! 0:38 Who is D’angelo Wallace? 1:41 Enter, iDubbbz 2:04 Comparing iDubbbz’ and D’angelo’s channel growth 3:37 Comparing total subscribers 4:35 What does the future look like for D’angelo? 5:27 There are so many parallels between them 7:27 There are also key differences between the two 9:14 The ~gender~ discussion 9:50 WOC paved the way for beauty commentary 10:33 Men and Beauty on YouTube 11:46 Why did D’angelo’s channel take off over WOC creators? 13:08 The glass escalator 14:30 What’s next for me? Other videos you might enjoy: The cultural impact of Content Cop *data analysis*: https://www.youtube.com/watch?v=EJpJWYTdtPc TikTok mansions during pandemic: whY *data analysis*: https://youtu.be/BlrsnoHYW_I #vastava #commentary #dangelo
Épisode 7 - The debate drinking game (with data science!)
21 octobre 2020
Register to vote! https://www.usa.gov/register-to-vote Tutorial for how I did this: https://towardsdatascience.com/the-debate-drinking-game-with-data-science-7a0c0692e5a Code: https://github.com/vastava/data-science-projects/tree/master/debate-drinking-game 0:00 Intro 1:09 Methodology 2:11 Biden’s most common phrases (1 - 5) 5:39 Biden’s most common phrases (6 - 10) 9:11 The FINAL rules for Biden 9:23 Trump’s most common phrases (1 - 5) 12:25 Trump’s most common phrases (6 - 10) 15:11 The FINAL rules for Trump and Biden 15:25 Some final thoughts on this analysis 17:12 Outro Other videos you might enjoy TikTok mansions during pandemic: whY *data analysis*: https://youtu.be/BlrsnoHYW_I D'Angelo Wallace is the new content cop: https://www.youtube.com/watch?v=QwSFK3aI2hw&t=8s&ab_channel=vastava #vastava #2020election #debates
Épisode 8 - Timeline of COVID-19 deaths by country (and Trump's comments on the virus)
29 octobre 2020
An animated line chart race of coronavirus deaths by country from January until now, along with a timeline of Trump's responses and quotes regarding COVID-19. This video was heavily inspired by carykh and Lessons From the Data. Check out their content! carykh's Wikipedia rewind: https://www.youtube.com/watch?v=PranVQik0yg LFTD's UK coronavirus deaths timeline: https://www.youtube.com/watch?v=O0-pzyv84ag Follow me on twitter! https://twitter.com/vastava_ Data scraped from worldometers: https://www.youtube.com/redirect?q=https%3A%2F%2Fwww.worldometers.info%2Fcoronavirus%2F&redir_token=QUFFLUhqa3cyLUowVVlGdHgxbFEzU2Z1SU52WDg3eExIUXxBQ3Jtc0tuamF1b0NObkZqbnkwaGZkX0dtUXR2YzdwdEFWakFhMFpoMUl3R0c0all1NElHLTNONGs2OE1peXlsSHJnWF9uNnF2NlJKUlZ0YVJmY21PYVNKa0xtNlM3Tmc3bGRpX3pXZHpXdUljcHVkY2ZCbV95RQ%3D%3D&v=ujQ9Gy0LWiQ&event=video_description Chart made using d3.js. Music: Skyline by Juju Mas Other videos you might enjoy: The debate drinking game (with data science!): https://www.youtube.com/watch?v=75AQqX8W0Hc Most viewed Wikipedia article by day: https://www.youtube.com/watch?v=39nV7f3HZg0 #vastava #datavisualization #covid19
Épisode 9 - COVID-19 response and the "developed world" | data analysis
27 novembre 2020
Link to the interactive dashboard for other countries + explanation of the model: https://vastava.github.io/coronavirus-dashboard/ 0:00 Intro 0:41 Who determines which country is “prepared”? 1:45 Outbreaks and the “developed world” 3:57 Methodology 4:55 The model 5:16 The U.S. example 6:18 Growth rate explanation 7:57 Growth rate for other countries 9:17 The South Korea example 10:19 Back to the U.S. 11:16 These numbers don’t necessarily reflect reality 12:22 What is the takeaway? Were the lockdowns effective? 14:00 Wrap it up! Relevant articles: The Atlantic | How the Pandemic defeated America: https://www.theatlantic.com/magazine/archive/2020/09/coronavirus-american-failure/614191/ NYT | He Could Have Seen What Was Coming: Behind Trump’s Failure on the Virus: https://www.nytimes.com/2020/04/11/us/politics/coronavirus-trump-response.html Time | Why the U.S. is Losing the War on COVID-19: https://time.com/5879086/us-covid-19/ Follow me on twitter! https://twitter.com/vastava_ #vastava #covid19 #datascience
Épisode 10 - I trained an AI to tell me CORPSE's music genre | data science
4 décembre 2020
In which I build a machine learning classification model to finally tell me wtf kind of music Corpse Husband makes. Here's the code for the genre classification model and the data, if you want to take a look: https://github.com/vastava/data-science-projects/tree/master/spotify-genre-classifier 0:00 Intro 0:14 Who is CORPSE? 0:37 Corpse’s music 1:26 No one knows what genre this is 2:35 Data Collection 4:10 Exploratory Data Analysis (and why it’s important) 6:15 Feature Engineering 7:51 Classification Algorithms 8:05 What is a Random Forest model? 9:05 The model! 9:51 The predictions! 12:15 Ways to improve the model 13:19 Wrap it up! Follow me on twitter! https://twitter.com/vastava_ #vastava #corpsehusband #datascience
Épisode 11 - Exploring Star Wars Legends vs. Canon, using data! #DisneyMustPay
3 janvier 2021
In which I use data to compare Star Wars Legends and Canon, and explore the repercussions of non-canonizing much of the Star Wars extended universe. Link to interactive timeline: https://vastava.github.io/starwars-timeline/ Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 0:38 Disney-Lucasfilm acquisition, EU, 1:10 What is “Star Wars Legends”? 2:07 Legends vs. Canon Timelines 3:00 The BBY/ABY system 3:45 The current Canon timeline 5:31 Key differences between Legends and Canon 7:04 A more in-depth comparison 7:29 Ancient history 7:55 Space exploration and technology 8:57 The retcon of Mandalorian Culture 10:35 Legends and Canon diverge after original trilogy 11:20 Why did Disney get rid of Star Wars legends? 11:49 Enter Dave Filoni 12:24 Source media methodology 13:13 The EU comprises ~90% of Star Wars lore 14:10 The EU is almost *too* comprehensive 14:27 Mando S2 spoilers 15:12 Fandoms are obsessed with speculation and theories 16:13 Star Wars storytellers have always been constrained 16:40 Star Wars is getting predictable // plot twists 17:15 Sequel trilogy, Mando spoilers 17:47 Do the fans know too much? 18:32 The timelines are going to get more complex 18:55 Mando S2 Finale spoilers 19:45 Problems arise in nonlinear storytelling 20:00 Legends stories are forming the backbone of the “Filoni-verse” 20:29 #DisneyMustPay 21:27 Disney writers are absorbing elements of Legends stories into the current canon 22:45 You gotta pay, Disney. 23:02 Outro 23:41 My plans for the new year #vastava #starwars #DisneyMustPay
Épisode 12 - 2020 Rewind! Wikipedia's most viewed articles, by day (July 2020 - Jan 2021)
9 janvier 2021
Live through the wild events of 2020, through the lens of the most popular articles on Wikipedia. Built in d3.js! Watch the first half here (Jan - July 2020): https://www.youtube.com/watch?v=39nV7f3HZg0&ab_channel=vastava Tutorial on how I scraped the data and built the chart: https://towardsdatascience.com/scraping-wikipedia-page-views-to-make-a-2020-rewind-c9bcac97fa38 Follow me on twitter! https://twitter.com/vastava_ Data source: Wikipedia API Music: Night Out - LiQWYD #vastava #rewind #2020
Épisode 13 - Slang words of 2020, using data science!
14 janvier 2021
In which I determine the slang words that took over the Internet in 2020, using Google Trends and data science! Inspiration from The Pudding: https://googletrends.github.io/year-in-language/index.html# Link to code/write-up: https://towardsdatascience.com/the-hottest-slang-of-2020-according-to-google-trends-data-7bae348409b4 0:00 Intro 0:20 Methodology 1:34 Example words that aren’t considered ‘slang’ 3:09 Google trends is kinda wack 4:24 The top 10 words! 5:00 #10 5:40 #9 6:18 #8 7:24 #7 7:53 #6 8:26 #5 9:00 #4 9:18 #3 10:18 #2 10:50 #1 11:35 Most of these words aren’t “traditional” slang 12:26 Examining the origins of slang? 13:17 Outro Follow me on twitter! https://twitter.com/vastava_ Other videos you might enjoy: 2020 Rewind! Wikipedia's most viewed articles, by day (July 2020 - Jan 2021): https://www.youtube.com/watch?v=XP0Xft-MNqs The debate drinking game (with data science!): https://www.youtube.com/watch?v=75AQqX8W0Hc D'angelo Wallace is the new (better) content cop *data analysis*: https://www.youtube.com/watch?v=QwSFK3aI2hw #vastava #2020slang #datascience
Épisode 14 - Six months later: comparing Shane and Jenna's absences from Youtube | data analysis
25 janvier 2021
In which I use data to compare the channel growths of Shane Dawson and Jenna Marbles six months after the pair made apology videos and left Youtube for past offensive content. I wrote a tutorial for how to scrape data from SocialBlade for this type of analysis: https://medium.com/swlh/how-to-scrape-socialblade-for-youtube-subscription-data-ec7c4bde6933 0:00 Intro 0:55 Shane and Jenna’s apology videos were very different 2:14 But there are a lot of parallels 3:46 Shane’s subscriber growth 4:50 Quick explanation of Youtube rounding 6:11 Jenna’s subscriber growth 7:17 Shane vs. Jenna (subscribers) 7:54 Total video views (and deletion of past content) 9:32 Deep dive into Jenna’s deleted views 10:48 Deep dive into Shane’s deleted views 12:16 Signs pointing towards Shane’s return 12:53 Let’s talk about Ryland and Julien 13:26 Jenna wanted to leave. Shane didn’t 14:05 This is not Shane’s first rodeo 15:02 Shane’s history of deleting past content 16:08 Shane’s track record and looking ahead 17:42 How will the Internet react when he returns? 18:41 Outro Follow me on twitter! https://twitter.com/vastava_ #vastava #jennamarbles #shanedawson
Épisode 15 - Watch $GME, $AMC and other "meme stock" prices rise
31 janvier 2021
Given the current brawl between Reddit and Wall Street, I wanted to make an animated timeline of the "meme stock" share prices at the center of it, and also of various trading subreddits' growth. For the first graph, I collected data for Gamestop (GME), Express (EXPR), Blackberry (BB), AMC, Bed Bath and Beyond (BBBY), Naked (NAKD), Nokia (NOK) and KOSS. For the second, I collected data for r/WallStreetBets, r/Stocks, r/WallStreetBetsELITE, r/WallStreetBetsNew and r/SatoshiStreetBets. 0:00 Closing share prices 2:12 Subreddit growth Follow me on twitter! https://twitter.com/vastava_ Other videos you might enjoy: 2020 Rewind! Wikipedia's most viewed articles, by day (July 2020 - Jan 2021) https://www.youtube.com/watch?v=XP0Xft-MNqs Timeline of COVID-19 deaths by country (and Trump's comments on the virus) https://www.youtube.com/watch?v=MjWYktUThmA #vastava #gamestop #wallstreetbets
Épisode 16 - No, community posts aren't broken (from a data scientist's perspective)
4 février 2021
In which I debunk Spiffing Brit's latest video about how community posts are supposedly a "glitch" that can be used to game Youtube's recommendation algorithm. Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 0:42 Would you expect that community posts only go to subscribers? 1:08 There are lots of people “in your community” who aren’t subscribed 1:53 “The algorithm” and misconceptions 2:42 Flawed assumption of how community posts are promoted 3:30 The algorithm takes into consideration user history 4:16 Which explains why community posts are showed to non-subscribers 4:59 Engagement Rate of community posts 5:23 “Engagement” is differently defined for community posts and videos 6:08 Impressions vs. Views 7:23 You also can’t compare likes on posts vs. videos 8:42 Uses are predisposed to interact with polls, not videos 9:11 Why does this mean the feature is “broken”? 9:43 Community posts are working as intended 10:32 Brief recap of my points thus far 10:52 Interaction on Youtube videos is rare 11:12 Community posts solve the “rare feedback” problem 12:00 Community posts drive interaction and engagement 12:32 Let’s talk about viewer demographics 13:22 Why I think the community posts worked so well for Spiffing Brit 13:44 These posts are not getting promoted to *everyone* 14:46 Let’s talk about sample sizes 15:19 This “hack” has more to do with the content, not the feature 16:10 The feature isn’t “broken” 16:34 Responding to pt. 2 17:50 If the feature was so easily “fixed”, was it ever broken? 18:17 I’m not a hater 19:00 Outro Other videos you might enjoy: The cultural impact of Content Cop *data analysis* https://www.youtube.com/watch?v=EJpJWYTdtPc TikTok mansions during pandemic: whY *i made a map* https://www.youtube.com/watch?v=BlrsnoHYW_I&t=4s Six months later: comparing Shane and Jenna's absences from Youtube | data analysis https://www.youtube.com/watch?v=hu2haO42bmc #vastava #TheSpiffingBrit #algorithm
Épisode 17 - Did 2020 break Rotten Tomatoes? | data analysis
18 février 2021
In which I scraped and analyzed a ton of Rotten Tomatoes data to examine why Wonder Woman 1984's Rotten Tomatoes score dropped so quickly, and whether or not this has happened for other films. Link to scraping tutorial: https://vastava.medium.com/how-to-scrape-rotten-tomatoes-for-historic-tomatometer-scores-426f01a55a0d My medium: https://vastava.medium.com/ Follow me on twitter! https://twitter.com/vastava_ Dan Murrell’s videos which inspired this project Why Wonder Woman 1984's Reviews Dropped So Fast - Charts With Dan! https://www.youtube.com/watch?v=ylQ6ey-aWAc&ab_channel=DanMurrell Can You Trust Early Reviews? - Charts with Dan! https://www.youtube.com/watch?v=nCGBAGwQFa4&ab_channel=DanMurrell 0:00 Intro 0:24 WW84 and Rotten Tomatoes 0:58 The Dan Murrell disclaimer 2:23 How I scraped the data 2:57 The RT webdevs can go to hell!! 3:32 The analysis 3:53 What does “Certified Fresh” mean? 4:22 The data of WW84’s RT score 5:24 Enter conspiracy theories about film critics 6:12 WW84 was NOT the only 2020 film that experienced a drop in ratings 7:06 Some films were consistently reviewed 7:47 The effect might be more pronounced for “blockbusters” 8:24 Let’s look at the number of reviews for each film 9:34 Early critic reviews and release dates 10:10 WW84 had a staggered embargo 11:10 That conspiracy theory is disrespectful to critics… 11:24 Critics vs. Audiences 11:47 Audience reviews are all over the place 12:53 Critics may have been influenced by public opinion 13:59 The 2020 viewing experience is very isolating 14:33 The RT scoring system is binary 15:14 “Soul” is an example of a true certified fresh film 15:38 WW84’s quality is… ambiguous 16:28 There are many possible causes for this “critical whiplash” 16:46 Birds of Prey and the theatergoing experience 17:40 What about before 2020? 17:55 Let’s look at some Disney remakes 18:26 We didn’t have a lot of viewing options in 2020 19:15 Godzilla: KOTM and the critics/audience divide 19:54 Captain Marvel and “review bombing” 21:17 Critical reviews were largely unaffected 21:43 The Last Jedi 22:15 The WW controversy 22:46 There is a pre-2020 film that lost its certified fresh rating 23:44 Takeaways 24:15 Outro Other videos you might enjoy: Exploring Star Wars Legends vs. Canon, using data! https://www.youtube.com/watch?v=JTJ9IAvoB7c Is Sonic fan art disturbing? *data analysis* https://www.youtube.com/watch?v=x_XR-K1cL7w #vastava #ww84 #rottentomatoes
Épisode 18 - Best selling manga issues by week (2012-2021) | bar chart race
27 février 2021
Animated data visualization of the best-selling manga issues by week. Data was scraped from Oricon's weekly top 10 ranking of manga sales, and was auto-translated using Google Translate's API. Please forgive any translation errors! Chart was built in d3.js ☺ Link to code/tutorial: coming soon! Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 0:10 2012-14 1:10 2015 3:53 2016 6:36 2017 8:48 2018 9:34 2019 11:51 2020 14:11 2021 14:40 Outro Other videos you might enjoy: 2020 Rewind! Wikipedia's most viewed articles, by day (July 2020 - Jan 2021) https://www.youtube.com/watch?v=XP0Xft-MNqs Is Sonic fan art disturbing? *data analysis* https://www.youtube.com/watch?v=x_XR-K1cL7w I trained an AI to tell me CORPSE's music genre | data science https://www.youtube.com/watch?v=VTU6Jla70VY Why is Attack on Titan SO Popular? | data analysis https://www.youtube.com/watch?v=4jXjDofVBoY Music used: KOMODOI - Cloudy BODYSURFER - Call Your Grandma Zorozo - Ghost of the Samurai Harris Heller - Spring Rollin’ #vastava #manga #dataviz
Épisode 19 - Why is Attack on Titan SO popular? | data analysis of manga and anime industries
5 mars 2021
In which I use manga sales and anime viewership data to analyze Attack on Titan's insane popularity. Link to code/write-up: coming soon Follow me on twitter! https://twitter.com/vastava_ Check out Super Eyepatch Wolf's analysis (which inspired this video)! https://www.youtube.com/watch?v=FaPSzvMO8Fo 0:00 Intro 1:37 Manga is extremely popular in Japan 2:05 Manga that are adapted into anime are usually best-sellers 2:31 Attack on Titan yearly manga sales 3:01 AoT yearly sales rank among manga 3:30 Why did AoT experience a drop in sales? 4:01 The anime’s success was a huge surprise to those working on it 5:03 Fans outside of Japan typically discover a series as an anime before manga 5:55 English copies started to sell after the anime premiered 6:21 shameless pluggg + chart explanation 7:18 Looking at AoT’s manga sales by issue 7:42 The Toonami effect 9:14 The Walking Dead, zombies and kaiju 10:15 Publishing rights are different in Japan 10:45 Celebrity culture and manga authors 11:14 Publishers rely on anime to generate revenue 11:52 Anime was pretty mainstream by the 2010s 12:27 Shonen vs. Shojo manga 12:52 Weekly Shonen Jump, ToC rankings and cancellations 14:08 Shonen Jump stories are among the most successful manga/anime 14:49 Attack on Titan and Bessatsu Shonen 15:56 Why did such a small studio end up animating AoT? 16:26 AoT overcome many hurdles and is still successful 17:09 AoT’s cultural impact is undeniable 17:51 Outro Other videos you might enjoy: Best selling manga issues by week (2012-2021) | bar chart race https://www.youtube.com/watch?v=grlqz3gmOlU Exploring Star Wars Legends vs. Canon, using data! https://www.youtube.com/watch?v=JTJ9IAvoB7c I trained an AI to tell me CORPSE's music genre | data science https://www.youtube.com/watch?v=VTU6Jla70VY #vastava #attackontitan #shingekinokyojin
Épisode 20 - 2020 Vision demystified: how this channel can "predict" a death before it happens
25 mars 2021
In which I debunk the 2020 Vision channel, a new internet hoax that can seemingly "predict" a celebrity death before it happens, and has a lot of people freaked out. I use my background in data science, open source investigation and Youtube studio to get to the bottom of this mystery! Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 1:07 How is the upload date from 2017? 1:58 Evidence (Wayback Machine, Socialblade, etc.) 3:11 But how is the exact date of death in the video itself? 5:14 Okay, but how is the NAME in the video? 6:10 My theory (they used code) 7:48 All of the celebrities were known in 2017 9:04 The Irrefutable Proof 10:24 There have been other similar hoaxes on Youtube 11:31 But 2020 Vision’s core concept is gross 11:57 We don’t know their intention 12:33 Copycat channels 13:21 Mourning celebrities 14:08 Outro #vastava #2020vision #internethoax
Épisode 21 - Wandavision, Doom Patrol and the new trend of surreal superhero television | a data analysis
1 avril 2021
In which I point out an emerging trend in superhero television of surreal stylistic shows that center around mental health, trauma etc. Minor spoilers for Wandavision, Doom Patrol, Legion and Umbrella Academy! Link to code/write-up: coming soon 0:00 Intro 1:46 The new trend in superhero shows 3:04 The parallel histories of X-Men, Doom Patrol and Umbrella Academy 5:38 But why do these shows LOOK the same? 6:15 There’s a lot of crossover between these shows’ crewmembers 8:50 a very long diatribe about an obscure web series that I really should have just cut from the video 9:22 But why are these shows all getting greenlit at the same time? 10:42 The current landscape of superhero tv shows 13:36 Superhero fatigue is taking its toll (The Arrowverse, a case study) 15:35 The same can be said for Netflix Marvel shows 17:40 The growing pains of a genre 18:15 Let’s look at how the Western genre evolved 19:43 A similar pattern emerges with police procedurals 21:56 Outro Follow me on twitter! https://twitter.com/vastava_ #vastava #wandavision #doompatrol
Épisode 22 - The TERRIBLE idea of "Human Stock Markets" and reputation as currency | BitClout explained
18 avril 2021
In which I explain why BitClout is a horrible idea, seems like a scam and has many disturbing implications for creators and their fans! Bad stuff let's discuss Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 1:11 What is Bitclout? 3:41 What are the "pros" of Bitclout? 4:47 The platform seems scammy (no withdraw option, minimum payment required) 7:01 Using celebrities' likeness without their permission (+ the inherent hypocrisy of that) 8:16 Market manipulation 9:16 Longevity of the currency 10:51 The value proposition is… nothing 13:38 Monetizing a personality … is bad 15:47 Outro #vastava #bitclout #stonks
Épisode 23 - Timeline of COVID-19 in India (data visualization of new confirmed cases by country)
28 avril 2021
Ways you can help COVID relief campaigns in India: https://docs.google.com/document/u/0/d/1eiobgyrl8iz-R1Dz7c4R5pzzzkuZLBj99vaC7T_UeVo/mobilebasic Follow me on twitter! https://twitter.com/vastava_ 0:00 March 2020 - Jan 2021 6:00 Recent case growth Music used: Savfk - Ultra https://www.youtube.com/watch?v=8A4Jak73Lao Ross Bugden - Something Wicked https://www.youtube.com/watch?v=Zuw_O5MU5CE #vastava
Épisode 24 - From Diss Tracks to BYE SISTER: the anatomy of Youtube drama
28 mai 2021
In which I analyze the evolution of Youtuber takedown (from diss tracks to the 40 minute callout video) videos with data! Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 2:10 The Youtube Diss Track (and why they were so successful) 3:41 A timeline of diss tracks 5:38 The Paul Brothers and the era of manufactured feuds 8:09 A new Youtube? 9:12 BYE SISTER and its impact 10:23 The Tati copycats 11:52 What defines the "exposed" video? 13:04 Youtube drama as a marketing tactic 14:12 The different types of "expose" videos 15:04 Commentary used to be nasty, and it might be heading that way again 16:03 Content Nukes and Leafy 17:40 Detailed catalogs of problematic behavior (D'Angelo Wallace and Frenemies) 20:04 Also, journalists better understand how creators work 21:35 Outro #vastava
Épisode 25 - The promise (and pitfalls) of public education
15 août 2021
In which I talk about why I love UC Berkeley, and struggle with it as an institution. Follow me on twitter! https://twitter.com/vastava_ 0:00 Intro 0:50 A brief overview of Pros and Cons 4:01 The Public vs. Private financial model 5:50 The Master Plan for Higher Education 7:01 Issue #1: The Budget 9:42: Issue #2: Tuition 10:57 A brief aside on who tuition affects "the most" 12:19: Issue #3: Non-resident Enrollment 13:45 Issue #4: Demographics 16:14 Issue #5: Donations 17:54 Closing Thoughts 18:39 Where I've Been + Outro #vastava
Épisode 26 - Watch Jeopardy with me!!
17 août 2021
In which I ... Link to code/write-up: Follow me on twitter! https://twitter.com/vastava_ #vastava
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