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Nowadays, anyone with a smartphone and an Internet connection can become a YouTuber. Many beginners dream of success on the platform, with videos acclaimed by millions of subscribers. However, reaching fame is not as easy as it seems: indeed, with how much the number of creators has grown in the past years, it has become increasingly difficult to make an impact. The difference between famous and unknown YouTubers may lie in more than the quality of their videos. Are there any metrics in famous YouTubers’ most viral videos - title, length, category, upload date - that set them apart from the rest? Could these metrics be used to create a guide for YouTubers who are starting out, and allow them to focus on the actual content with the certainty that other objective parameters are helping their success?


The story of Ada - Chapter 1: First video and starting criteria


My name is Ada, I heard you were doing research on how small YouTubers can become successful, so that’s why I reached out.

So here’s the whole story: I’m a uni student, I like to make small vlogs about my daily life and share them with my friends. Some time ago, they suggested I put them on YouTube, which I thought was an exciting idea! Can you imagine, so many people around the world seeing my videos! So I’ve been uploading videos, but I’ve barely been getting any views… I’m so confused, what am I doing wrong? Vlogs are popular, plus I film with top-notch material and I work a lot on the editing too… Is there anything else which could help me grow my channel?

Thanks, Ada Westerlain


Evaluating relevant criteria for the success of a YouTube video

Dear Ada,

Thanks for reaching out to us! We’ve been hard at work on our analysis, and we’d love to use it to help you. :) We believe it’s important to understand which “objective” parameters you can tune, as a content producer on YouTube, to hopefully have a better performance in the long run.

But first, let’s explain to you what kind of data we used for our research.

What data will we analyse, and how?

To answer our questions, we will use a large dataset of YouTube channels and videos: YouNiverse. The dataset is divided into three main types of data: