Avoid Fake Likes and Views At Youtube Video :Views Stuck At view count 301?

3 replies
Hi Warriors,

Have you ever noticed that few of your videos got stuck at 301, 302 or any number between 290 to 350??
If yes, here is why?

Because You have tried to get fake likes and views to your videos and now youtube has freezed your view counts, its not mean that your video is not at youtube...it is still there but view counter is freeze.

So please waste your time in creating and promoting your videos, NOT in "How to get fake likes or views"

How to fix if your views count was freezed?

There is only one way you can do it, drive more quality traffic to that video and if YouTube realize that your video is doing fine....your view counts will work again.

I am not sure about this one, but someone told me when its view count reopen "All the previous visits " will be added to views...I hope it is true.

Anyway Thanks for your time.

Have a nice day

Vaibhav is here
#301 #avoid #count #fake #likes #promotion #stuck #video #view #views #youtube #youtube video
  • Profile picture of the author Efficacious
    Hi, in my opinion there can be other reason for that. It can be caused by a massive traffic to your video. I am sure you have noticed how videos posted by popular authors (like musicians) can be stuck on exact 301 views for some time. And it is not caused by fake traffic it's just because too much people view your video too quickly after its upload. In this case your views is not freezed or something, it's normal.
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  • Profile picture of the author jamescanz
    301 isn't the consequence of buying fake views

    This video explains: https://www.youtube.com/watch?v=oIkhgagvrjI
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  • Profile picture of the author Moneytalist
    After a video reaches a certain number of views, YouTube tells the database to freeze the view count until YouTube can manually verify the correct count to protect against botting attempts—
    i.e. using automated computer processes to artificially inflate the number of views. YouTube view counts are initially tracked by servers near the end user. By looking at reports from these individual servers, YouTube engineers can detect suspicious patterns in the data.
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