You go on twitter and Instagram, and usually what you see is a huge list of hashtags, posted by others. But when you go on blogs or other websites, you won’t really see too many of them? In this article we will explain why:
First, lets try to understand what is happening when you click a hashtag: if you see the hashtag “#good” on twitter and you click it, you will be redirected to a twitter page that shows all the recent twits which used the hashtag “#good”. We can conclude that hashtags are performing like yellowpages for ideas: you click it and you get an index page.
So why you won’t see so many hashtags on, for example, cnn.com? this is because news websites would usually use “tags” or “categories” rather than hashtags. The reason is simple: such websites do not “index” the information by hashtags, but rather they prefer to index them by categories. So Hashtags there would exist, but more rarely. And because they are rare on such sites, the editors didn’t bother to create any hashtags indexing page. So how could you, as a social media expert, take into account those “shadow hashtags” – which are on the internet, but no one really relating to them in a high level look?
The quick answer is, to use tools which do it for you. One of those tools for example is TagPredict, which can group for you all the hashtags on a certain domain, but also which one of them is trending in global overlook.
Installation of TagPredict is very simple: just enter the Google Webstore page, and add it to your Google chrome. The setup takes 2 minutes, and once you do it, click “global trends”, you will then see the list of hashtags on ALL THE INTERNET, not just on twitter.
TagPredict also got a professional version, which gives you hashtags analytics, and hashtags search, on the fly. To get TegPredict Pro you need to login to the TagPredict website and click “login with facebook”:
Do the Facebook login thing, and then click “hashtags search” to search for hashtags. Once you click a hashtag, you get full analytics of it. It looks like that:
Which is a brilliant way to analyse hashtags. Some other examples may be found on the blog article that explains how hashtags analytics works.