Final Project
Eric Nesline
2023-05-05
Youtube Introduction
There are a lot of entertainment services these day. Netflix, Amazon Prime, HBO, Hulu, and Disney+ to name a few. The thing about these services is they all cost money. Well, YouTube does not cost a dime. Many people will argue that YouTube is a social media platform, but I disagree. Videos have no time limit and anyone with internet and a camera can post on the Youtube platform, leading to the ultimate variation of entertainment. In my generation, generation Z, YouTube has grown to be one of the most used apps and entertainment platforms in the world. Today, many YouTubers gain so much fame they are more renowned than famous movie actors or actresses. One of the most popular YouTubers today is Jimmy Donaldson, also known as “Mr. Beast”. Today, I will be going through multiple data sources and discovering how Mr. Beast competes in the YouTube industry and what makes him successful.
Data Dictionary
Lets start by discussing all the data sources I used to easier understand it all. The first data set I used was the top 1000 YouTubers based on subscribers:
| Column | Description |
|---|---|
| Youtuber | Name of Youtuber |
| Subscribers | Total Subscribers |
| Video Views | Total Views |
| Video Count | Number of Videos on Youtube |
| Category | Category Youtuber is in |
| Started | Year Channel was started |
| View per Video | Video Views divided by Video Count |
| Subs to Total View Ratio | Subscribers divided by total views |
| Subs to Video View Ratio | Subscribers divided by views per video |
| Channel Length | Computed by 2023 - Started |
| Updated_rank | Ranking after Data Cleanse |
Summary Statistics of Youtube Data Set
| Youtuber | subscribers | video views | video count | category | started | views per video | subs to total view ratio | subs to video view ratio | channel length | updated_rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:746 | Min. : 10900000 | Min. :1.066e+07 | Min. : 13.0 | Length:746 | Min. :2005 | Min. : 31760 | Min. :0.0005679 | Min. : 0.0393 | Min. : 2.00 | Min. : 1.0 | |
| Class :character | 1st Qu.: 12600000 | 1st Qu.:3.096e+09 | 1st Qu.: 476.5 | Class :character | 1st Qu.:2010 | 1st Qu.: 1550573 | 1st Qu.:0.0018803 | 1st Qu.: 1.6509 | 1st Qu.: 7.00 | 1st Qu.:187.2 | |
| Mode :character | Median : 15100000 | Median :5.384e+09 | Median : 1092.0 | Mode :character | Median :2013 | Median : 3929398 | Median :0.0029702 | Median : 4.0535 | Median :10.00 | Median :373.5 | |
| NA | Mean : 19406032 | Mean :8.239e+09 | Mean : 10762.7 | NA | Mean :2013 | Mean : 11205782 | Mean :0.0056978 | Mean : 22.5342 | Mean :10.21 | Mean :373.5 | |
| NA | 3rd Qu.: 20400000 | 3rd Qu.:9.328e+09 | 3rd Qu.: 3461.5 | NA | 3rd Qu.:2016 | 3rd Qu.: 10075968 | 3rd Qu.:0.0051354 | 3rd Qu.: 10.5072 | 3rd Qu.:12.75 | 3rd Qu.:559.8 | |
| NA | Max. :140000000 | Max. :1.355e+11 | Max. :329711.0 | NA | Max. :2021 | Max. :368690039 | Max. :1.2189879 | Max. :997.1950 | Max. :18.00 | Max. :746.0 |
Mr Beast data set dictionary
Secondly, I used Mr Beast data set, this included the following:
| Column | Description |
|---|---|
| ID | Youtube video ID |
| Title | Video Title |
| Description | Video Description |
| Publish Time | Date of video publish |
| Duration_Seconds | Video length in seconds |
| viewCount | Total views of video |
| likeCount | Total likes on video |
| commentCount | Total comments on video |
| money | whether or not money was in title |
| video_rank | Video rank by views |
| length | Length of video in minutes |
| like_percentage | Computed by total likes divided by total views then multiplied by 100 |
Summary Statistics of Mr Beast Data Set
| id | title | description | publishTime | duration_seconds | viewCount | likeCount | commentCount | video_rank | length | money | like_percentage | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:211 | Length:211 | Length:211 | Min. :2013-01-12 23:35:45 | Min. : 52 | Min. : 13954 | Min. : 370 | Min. : 59 | Min. : 1.0 | Min. : 0.8667 | Length:211 | Min. : 0.9824 | |
| Class :character | Class :character | Class :character | 1st Qu.:2017-11-13 22:37:29 | 1st Qu.: 470 | 1st Qu.: 15503359 | 1st Qu.: 463744 | 1st Qu.: 23359 | 1st Qu.: 53.5 | 1st Qu.: 7.8333 | Class :character | 1st Qu.: 2.2601 | |
| Mode :character | Mode :character | Mode :character | Median :2019-01-16 22:05:01 | Median : 783 | Median : 49047821 | Median : 1362926 | Median : 61803 | Median :106.0 | Median : 13.0500 | Mode :character | Median : 2.8460 | |
| NA | NA | NA | Mean :2018-08-26 13:57:04 | Mean : 2098 | Mean : 50576416 | Mean : 1602417 | Mean : 75619 | Mean :106.0 | Mean : 34.9643 | NA | Mean : 3.3140 | |
| NA | NA | NA | 3rd Qu.:2020-03-07 08:30:13 | 3rd Qu.: 921 | 3rd Qu.: 80282594 | 3rd Qu.: 2287990 | 3rd Qu.:100906 | 3rd Qu.:158.5 | 3rd Qu.: 15.3500 | NA | 3rd Qu.: 3.6340 | |
| NA | NA | NA | Max. :2021-12-18 21:00:00 | Max. :85686 | Max. :178763228 | Max. :19049431 | Max. :726861 | Max. :211.0 | Max. :1428.1000 | NA | Max. :19.7356 | |
| NA | NA | NA | NA | NA | NA | NA’s :1 | NA’s :1 | NA | NA | NA | NA’s :1 |
Mr Beast
To start, shown below is Mr. Beast demographics. This comes from a data set showing the top 1,000 YouTube channels based on subscribers. It is important to note that after data cleansing, the music category was removed because if I cared about musicians I would have analyzed through Spotify’s data. Also, “YouTubers” that have 0 or 1 video posted were removed, because they simply are not true YouTubers. Note that the data is from 8 months ago, and there have been some changes to the Mr Beast channel since.
| Youtuber | Subscribers | Video Views | Vide Count | Category | Started | Views per Video | Subs to Total View | Subs to Video Views | Channel Length | Rank |
|---|---|---|---|---|---|---|---|---|---|---|
| MrBeast | 1.02e+08 | 16832456681 | 726 | Entertainment | 2012 | 23185202 | 0.0060597 | 4.399358 | 11 | 4 |
Rankings
This visual shows the rankings and how they correlate with their subscriber amount. We can see that the higher ranked YouTubers have quite the substantial gap in subscribers compared to channels with middle to lowers ranks. The gap falls off after the top 30 and the field evens out. Mr Beast is ranked 4th, therefore he is among the top percentile of YouTube Subscribers in the world.
Top YouTuber’s Analysis
Mr Beast falls under the entertainment category. Here I wanted to show how popular this category among the top YouTubers. Here we can see that Mr.Beast falls into the most common category on popular YouTube channels, by a large margin.
Channel Age
Mr Beast channel is 11 years old. Here we can see that there is a slight trend that the older a channel is the more subscribers they will have. This could align with the saying “Rome wasn’t built overnight”. Being a YouTuber is not easy, figuring out popular video content can take years to master, therefore it makes sense longevity is a factor in success and Mr Beast’s “experience” has helped.
Average Views per Video
Going back to the entertainment category, this visual depicts the average view per video. The entertainment category seems to be right in the middle. Education and Blogs hold the highest average view per video. This could be due to higher views and less videos. Mr Beast has videos posted, and some are more or less successful than others.
Mr Beast Video Analysis
Top Mr Beast Videos
Now we will dive into Mr Beast specifically rather than top YouTubers as whole. This data is all of Mr Beasts most popular videos based on popularity. Keep in mind this data is about a year old. Here we can see a table of his top 10 most viewed videos.
| Title | Total Views |
|---|---|
| $456,000 Squid Game In Real Life! | 178763228 |
| I Spent 50 Hours In Solitary Confinement | 158424466 |
| I Spent 50 Hours Buried Alive | 154322660 |
| I Put 100 Million Orbeez In My Friend's Backyard | 151143603 |
| Press This Button To Win $100,000! | 145527042 |
| Going Through The Same Drive Thru 1,000 Times | 138440792 |
| I Ate A $70,000 Golden Pizza | 127179753 |
| Last To Leave $800,000 Island Keeps It | 119576253 |
| I Went Back To 1st Grade For A Day | 119352796 |
| Surviving 24 Hours Straight In The Bermuda Triangle | 109909726 |
Here we can see a few trends. His most popular videos tend to have some sort of money prize involved. The other popular video types are ones where he does some ridiculous task like spending 50 hours buried alive or a survival plan. It is not surprising to see the squid games video as his top video. He has stated it was his most expensive video he has ever made and released after the one of the most popular Netflix shows of all time: “Squid Games”.
Monetary Value
A box plot displays how the data is spread out. Here I analyzed all
the titles that related to money. Here we can see the median and lower
25% and upper 75% along with any outliers of views for monetary titles.
This visual tells us that Mr Beast videos have more views when there is
a money prize involves, as “Yes” indicates thus.
Video Length
Here is a visual showing the top ranked videos by length of video in minutes.
This visual shows a volatile relationship between video length and the video ranking based on views. We can see that The first ranked video was longer and the rest fall off. This could show that length is not as important as title or content. It is important to note that majority of these videos fall into the 12 to 16 minute length.
Video Likes
Views are great and all but views can a false measurement of success. Oftentimes people view the video and do not watch the entire video. Measuring likes is a better way to determine video success, as people who watched and enjoyed the video will give the video a like.
Here I made a like percentage. This was computed by dividing likes by views and multiplied into a percentage. This visual shows the video rank by views to the like percentage. We can see a massive spike around the 25 rank and then a general increase in the 100 to 200 rank.
Conclusion
After analysis we can see parameters that attribute to Mr Beast success. We can also see parameters that may have no correlation to his success. To start we can see that being in the entertainment industry is impactful. This industry is popular among top YouTubers and is sort of a broader category that allows for less redundant content. Also, his longevity on YouTube has correlated with success as longer the channel has been around the more views and subscribers. Another factor to his success is the amount of money he gives away. In his top most viewed videos we see that he titles the video using monetary value and that this correlates with success. Something we found all not to be true was views vs likes. Most people would attribute more views to more likes on the video. This is not the case with Mr Beast as higher liked videos were not typically aligned with high views. Overall it is cool to see his rise to fame and how he is a role model in todays day and age.
Comment Section Analysis
Views and Likes can only tell so much information. There is no human emotion or perspective involved. Originally, I intended to dive into a popular Mr Beast video and analyze top comments based on likes. However, I came to realize the comments were majority positive and typically biased towards Mr Beast. Therefore I grabbed comments from an older Mr Beast video, and the first Mr Beast video I ever watched, titled “Tipping Uber Drivers $10,000”. This relates to the strategy he as of putting money in his titles and using it generously.
This is a visual showing a sentimental analysis of the video’s comment section. I used all 8,000 comments from the video then filtered for comments that had over 10 likes. Then I eliminated all stop words such as “a” or “the” and applied sentiments to each word. We can conclude that most comments were positive or negative. We can also see that other negatively related sentiments are not in the higher usage of words, indicating Mr Beast video aligns with positive feedback.