Major Issues With Hashtags And How Tagpredict App Is Solving Them

Download the free TagPredict Chrome Extension from the Google Webstore.

One of the biggest minuses of hashtags is that they specific to the site they are used on. It means that hashtags that are used in Instagram – for example, do not connect to hashtags used in Facebook, Twitter and other sites: each website has its own hashtags system. For example, when you click a hashtag on Facebook, you get a page which groups all the Facebook posts that use this hashtags. You won’t get Twitter twits which use that same hashtags or Instagram pages which posted that hashtags. This is how hashtags are designed and there is an obvious reason for that: each website would want to keep their users for themselves. Facebook wouldn’t link to twitter pages (you could post twitter pages in Facebook though) and twitter wouldn’t link (by design, not by posts) to Facebook pages. Otherwise, sites would “lose” traffic which they want to keep in. Imagine that you clicked a hashtag in Facebook, and got linked to Twitter… Facebook would lose users if it was designed like that. And this is true for all sites.

This specific way that hashtags are designed creates a problem to social media networkers as they basically need to manage separate marketing streams for each social media channel. Some online tools like Buffer and Hotsuite developed a technology for scheduled posts to all social media sites in one post, but the hashtags problem remained unsolved. In most cases, channel managers just do not manage hashtags: as it is too complicated to deal with so many separated channels.

Another issue is that you can’t really rely on market trends when you look at an app which analyse hashtags in a specific system. For example, if the Twitter trends pages tell you that “#McDonalds” is the most trending hashtag at the moment, it is obviously taking only Twitter into account. What about Facebook, Instagram and other sites?   And more than that, what about all the rest of hundreds of thousands websites which discuss which burger is the best? If “#EatMcDonalds” appears 100,000 times in Facebook, and “#eatBurgerKing” appears only 30,000 times in Facebook, but “#EatBurgerKing” appears 500,000 more times in other small websites – it is trending higher than #EatMcDonalds. Ignoring “the long tail” is a big mistake, and basically misleading the way you read trends. It is like ignoring “all the rest of audience” of your website traffic – sometimes it adds up to more than the “tall neck” of it!


A good example, in comparison, is the concept of a “search engine”. In the early ages, search worked on a specific website, not on all websites. You entered the university site, and you could search for papers. You couldn’t search for papers on ALL universities sites. Then the search engines (Webcrawler,Yahoo, Lycos…) came to light and offered a search that is searching on everything: it combines the search results of many websites, in one page of results.

TagPredict is basically announcing a revolution in the trends intelligence technology which is similar to what search engines improved when they emerged in the late 1990’s: instead of looking at just ONE website when you look at the market trends, TagPredict is crawling to ALL websites, including small websites, blogs, news websites, personal websites, talkbacks, forums, etc – and grouping the hashtags trends into one central platform.

Download the free TagPredict Chrome Extension here

This article is inspired by the TagPredict Blog.

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