The dislike count will be private across YouTube, but the dislike button will remain
YouTube says they strive to be a place where creators of all sizes and backgrounds can find and share their voice. To ensure that YouTube promotes respectful interactions between viewers and creators. the Google owned platform had introduced several features and policies to improve their experience. And earlier this year, YouTube experimented with the dislike button to see whether or not changes could help better protect creators from harassment, and reduce dislike attacks — where people work to drive up the number of dislikes on a creator’s videos.
We’re making the dislike counts private across YouTube, but the dislike button is not going away. This change will start gradually rolling out today.
As part of this experiment, viewers could still see and use the dislike button. But because the count was not visible to them, YouTube found that they were less likely to target a video’s dislike button to drive up the count. In short, the experiment data showed a reduction in dislike attacking behavior1. The leading video platform also heard directly from smaller creators and those just getting started that they are unfairly targeted by this behavior — and their own experiment confirmed that this does occur at a higher proportion on smaller channels.
Based on what their team learned from experimenting with this numerously complained issue, they are officially making the dislike counts private across YouTube, but the dislike button is not going away. This change will start gradually rolling out today.
What’s changing for creators and viewers starting today
Creators will still be able to find their exact dislike counts in YouTube Studio, along with other existing metrics, if they would like to understand how their content is performing.
We want to create an inclusive and respectful environment where creators have the opportunity to succeed and feel safe to express themselves.
Viewers can still dislike videos to tune their recommendations and privately share feedback with creators.
YouTube expressed that they also heard during the experiment that some of the users have used the public dislike count to help decide whether or not to watch a video.
KinerkTube Members and Visitors.. Please Note: Any YouTube embed on KinerkTube will still have the KinerkTube like and dislike button as well as the like and dislike count. Eventually changes may be adopted in the same ways as YouTube had decided if our dislike button becomes an abusive tool rather than a useful one but as of now you can like and dislike content on KinerkTube AND get like and dislike counts (public)… however we already give you the option to hide your likes and dislikes on merch and may roll out the ability to hide it on your other content (music, podcasts, beats, etc.) but only if the requests are in high demand and/or if it makes feasible sense to do so. For now we feel it gives visitors of our platform a sense of real engagement and pro choice to express a public rating on content they consume. With that said, we will work out and/or adapt to what makes feasible sense around result type algorithms the same as YouTube will do when searching and discovering based off things you liked and disliked. This could be based now off other CTAs and/or perhaps view time on certain topics and video types. Ultimately it would make more sense to show recommendations based off of user activity rather than a user action of such things like a dislike button.
It’s like “so what if you tell us you don’t like it… the real question is… are you really engaging long views or just cutting videos short by skipping or closing them out. The activity will prove the necessary algorithm. This is how we believe YouTube will improve their algorithm and hide negative stats known as the dislike count. It’s also possible they will continue to base the majority of their algorithm off the dislike count but now keep it behind the scenes for the platform to ultimately decide what to show it’s users. This is also how we see it working well for algorithmic suggested results.