In this blog I will be explaining how the media platform YouTube works and help explicate how this company operates with algorithms, collecting user data, reaching multiple ethnography’s and how ethnography’s help form fandom.
I wanted to see how the algorithm works and if certain videos only get recommended to people because of their interest. I want to investigate three genres of content and see if any of them overlap and show viewers different genres of video.
These genres will include content creators who categorize themselves as the following;
- Gaming – (For example: Faze clan, lets players, highlights)
- Lifestyle/vlog – (For example: David Dobrik, casey neistat, vlogs)
- Comedy – (For example: NakeyJakey, Gus Johnson, Internet Historian)
There are a lot of YouTuber’s who make these types of content which will make it easy to analyze and understand the ethnography audience of these genres of videos. The way this study will be conducted is by making three separate YouTube channels, dedicating each one to its video genre. By the end of the study I would like to record if there is any overlapping between videos being recommended and getting a better understanding of the YouTube algorithm as this is a system that is implemented on many social media sites we visit every day.
I personally think these three genres will work well together as many YouTuber’s touch on these subjects and I think it will be a topic that can occur between channels and be shown in the algorithm.
So, I made three YouTube accounts which are called;
I went out and subscribed to as many channels I could which followed the themes of each channel. Once I was subscribed to over 30 channels on each account, I will now start watching videos and see how the YouTube channels start to change the content that gets recommended to the channel.
While I do this in my own time, I would now like to talk about the YouTube algorithm. Users on the YouTube platform admit that they are more likely to click on recommended content, this was a study done by Pew Research Center which investigated audience interactions with content. When viewers click on recommended videos, the algorithm will favor content that has more views and more watch time, as their main intent as a business is to keep people on their platform for long periods of time, so that more advertisements can be forced onto viewers, as advertisers is one of the main income for the YouTube brand and its content creators, the more people making content, the more views and clicks the platform will receive.
Reaching different ethnography’s is a big part of YouTube, as there is so many different niches and interests that can be presented on the platform. Looking at different ethnography’s was pointed out to me by a study from Christopher M. Bingham, who talked about the ‘ethnnographies of Twitch Streamers’, as this is a website that is relevant to YouTube as they operate almost the same way with content. He explores the ethnography of content creation in the new age of technology and the different approaches of income from ‘working from home’ (Bingham, 2017) this guided me to understand more of ethnography’s and audiences, which helped me understand YouTube as a business that wants to connect to different ethnography’s and appeal to bigger audiences.
How the algorithm works
The algorithm has been made by a team of engineers that focused on collecting data from users, remembering their clicks and interests to know for what they would click on in the future. Keywords and relevance is a big factor with the algorithm, when users use the search bar, YouTube will use the keywords in the search and recommend videos that have the most view time and relevance for the user, as they want to show the best results first. Keywords are so important when uploading content. Content creators get the option when uploading videos to use tags, these tags allow up to 500 characters of words that people can use to look up content.
Okay, so I started my research by making three YouTube accounts, dedicated to each roll, once every account was made and logged in, I made a queue of videos for each genre, 30 in each video. I’d let these videos play for a few hours so the algorithm can go into full effect as watch time on videos is the ones that would be more likely to recommended to you as that is how algorithms work on YouTube. Once the videos were finished playing, I could now analyses each channels homepage and how the videos were changed to these interests, as it will now be directed at this ethnographic audience.
From what I can see, gaming has had the biggest influence on the algorithm out of these three channels. Gaming is one of the biggest markets on YouTube as the watch time on these videos are way more phenomenal then other genres of videos. The reasoning for this how easily the content can be accessed and uploaded, these videos can be in the 10’s, 20’s, 30’s of minutes and be a large quantity of content, especially if someone is doing a lets play on a story mode game. So, by watching games like Valorant, Counter-strike, Among us, Dark souls and so on, YouTube has collected this user data and diverted its sole purpose to recommending gaming videos as this is what will keep the user on the platform longer as this is his/hers interest. Even advertisement has changed on the beginning of videos to suit the users interests, as I’m watching a video about Among Us, I’m getting recommended a video about the game Valorant as this was a video game I watched in previous videos.
- The Ethnography’s for the gaming channel consist of people who play video games casually and as their main hobby. They all relate to gaming and watch these YouTuber’s as they have a common interest.
With watching comedy videos has had the same effect as the gaming channel, every video on the YouTube homepage is either comedy skits, stand up or stories with comedic tones. Compilations of short funny videos like meme compilations or ‘vine’ like Tiktoks are also being recommended. This side of YouTube focuses on the personalities of content creators. The YouTube homepage for this channel is also shared with A list comedians like Ricky Gervais and Kevin hart as they are sharing the platform with YouTubers who have approached comedy in a different way.
- The audience for comedy YouTube videos appeal to a mass audience of people who enjoy clever and witty homemade comedy, these YouTubers connect with there fans by uploading content to give their audience a laugh.
For this genre of YouTube videos I noticed that the algorithm was a bit different to the other channels, in my research I discovered that these genres of videos were more likely to recommend you ‘real world videos’ like news articles and interviews, along with the vlogs in the algorithm, I think this is because vlogs mainly focus on what’s happening in day to day life and are actually connected to life situations, whereas skits and gaming are only connected to what’s happening in those virtual or fantasy worlds. As I only queued vlogs, I found this interesting as all these videos were recommended to this channel.
- The Ethnographies of people who watch vlogs and lifestyles seem like they are more interested in how other people are living and experiencing life, seeing things from there perspective from royalty and travelling or just hanging out with friends. This type of content appeals to the general audience of YouTube as you do not need to have to have a big interest in this topic to enjoy these videos.
What I have noticed in this study focusing on the YouTube Algorithm, is that these specific genres of videos have the possibility to overlap and share audiences which leads to the potential of getting a bigger following. This is because they are reaching out to multiple ethnographies. Content creators like NakeyJakey (A YouTuber who makes video essays mainly focusing on video game culture and design) has branched into different genres of video making like video essays about life advice or skits. These videos than branch into the comedy genre of videos and can lead to collaborating with other content creators. If you are a fan of Gus Johnson (who makes comedy skits) than more than likely NakeyJakey will show up in your recommended because they have made videos together, meaning the algorithm will recommended both content to each other’s audiences.
Collaboration is key when becoming a Content Creator, this is pushed by the YouTube Creator Academy as well as this is a way to expand on audiences and show new content to users, the more users watching content, the more YouTube works as a business, its kind of a Help me Help you situation.
The ‘Vlogging’ channels are a perfect example of sharing ethnographies, a great example of this is the YouTube group ‘Vlog Squad’ these are a group of Vloggers (with the main star being David Dobrik with 18 Million subscribers) all share the same themes of making vlogs and making them as ‘crazy’ as they could be to keep up viewer attention. Each member has their own YouTube channel which upload vlogs from different perspectives of an event which will lead the viewers to have the option to subscribe to the individual members of vlog squad which leads to a circle of consistent content between the group.
Just watch Davids perspective from 0-16 seconds in this video:
Now watch the unedited perspective which hasn’t been cut:
I think David Dobrik is a great example to research when it comes to YouTube ethnographies as his audience are very opinionated with the one-sided lens, he shows his audience. I say this as his videos are very short, and only shows the audience skits and highlights of his everyday which gives his audience a false ideology of his persona. David’s past relationships are always talked about in the comment section, which begins a discussion thread on who David should really be with.
This type of fandom is discussed by Lucy Bennett in her report ‘Between Ethics, Privacy, Fandom, and Social Media: New Trajectories that Challenge Media Producer/Fan Relations’. This report talks about discussions of ethics and fandoms and how boundaries are stepped over when it comes to the person they are fanning about uncomfortable. Other fans will step in with their opinion which is also known as Fan Policing, which can turn into arguments in the comment section “Fan policing is not limited to fans policing other fans, however. Another ethically challenging aspect of social media use is exposure to outpourings of hate online, and the question of how to negotiate and respond to these challenging messages.” (Bennett, 2016)
Fandom is a key factor when it comes to YouTube, and I think YouTube as a platform has changed the direction of how creators can interact with their fans and vice versa. As content creators rely heavily on their audience, it can shape the way they release content as fans now have the power to discuss about creators among themselves if they like/dislike what the creator is uploading. A study by International study of sport communications explains, “Online platforms such as YouTube enable the creation of open spaces for conversation where fans can comment and express their opinions about previously published contents, as well as develop conversation threads with other fans.”(Gil-Lopez, 2017).
I personally think YouTube is great, but I do think there’s an unfair advantage among content creators of who is being favoured by the Algorithm. There are many channels with millions of subscribers which are not getting the views and watchtime as their content has now technically pushed to the back of the line, as the algorithm favours Content Creators who are now trending and racking in the views.
I think these two channels are a good comparrison as they are nearly at the same subscriber count, but one is being favoured and the other isn’t.
I hope this blog was informative and you learned a few things about the platform YouTube that is not exactly on the surface for the general audience to see. There are a lot of ways social media learn and manipulate your data to suit your preferences in hope to keep you on these platforms for long periods of time and I think it’s a good thing to learn about these techniques these companies use as a way to inform yourself of what is actually happening and it not being a coincidence. But, then again it is a good thing as good content is being recommended to you which allows fans to keep up to date on their interests.