If you want more views on YouTube, get more DISLIKES. Getting a lot of dislikes, contrary to popular opinion, is not a bad thing. It may even be an indicator that you have set off the YouTube search and discovery algorithm. Dr. Linus Wilson discusses his new research on the YouTube algorithm that shows that getting more dislikes or a lower like percentage is associated with getting MORE views and a higher click-through rate (CTR). CTR is the most important factor in triggering the YouTube algorithm’s search and discovery recommendations. Video creators who try to manipulate the like to dislike ratios or percentages are ultimately misguided. There are much better calls to action that they could make to their views like subscribing or recommending another video. Controversial or newsy topics may be more clickable and inspire higher CTR and higher watch time.
This is based on Dr. Wilson’s study “Clickbait Works! The secret to getting views with the YouTube algorithm”
Wilson, Linus, Clickbait Works! The secret to getting views with the YouTube algorithm (April 9, 2019). Available at SSRN:
Click the link. Hit the download button. Click the link to download without registering below the dude’s picture. Prove you are not a computer, and you have the algorithm study that can blow up your channel for FREE.
In 2018, YouTube began releasing click-through rates (CTR) data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. This is the first academic study employing that data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube? This paper finds no to limited evidence that higher percent audience retention or and total average watch time per view are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views as did videos on trending or newsworthy topics. The marginal benefit in terms of views scaled by subscribers of increased CTR is between 71 and 318 times larger than the marginal benefits of increased watch time per view.
Keywords: YouTube, algorithm, search, discovery, video, CTR, click-through rates, clickbait, watch time, audience retention, neural networks, recommendation systems