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The top 6 influencers for #tag (beta)
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Search for an hashtag and you will see it displayed inside a red cirle. The more popular the hashtag is, the bigger the circle. You can also read a popularity rating, from 0 to 100: The most used hashtag on Twitter would get 100. The different typing variants will also be shown, and a sample of tweets using that hashtag will be displayed on the right.
Around the hashtag you searched you will also see its top related hashtags (up to 10) diplayed inside blue circles. The correlation is measured as the percentage of tweets using the searched hashtag which also use the related one. The stronger the correlation, the bigger the grey line that links them. You can also read the actual percentage by moving your mouse pointer over the related hashtag.
By clicking on a related hashtag (blue circle), it will become the selected one and its own related hashtags will be displayed. This way you can easily explore all the hashtag related to your interests, and find which ones are the most and least popular and most and least specific.
If you want to visualize if two related hashtags share some correlations, you can check the "Advanced mode" option on the lower right corner of the graph. With this option, when you click a related hashtag its own top correlations will be displayed, with pink links, along with those which were already displayed; on the right sample tweets using both hashtags will be shown. If some hashtags are among the top ten correlations for both the selected (red) and the focused (green) hashtag, they will have both grey and pink links. If you then want to explore the focused hashtag, click it a second time.
Below the hashtags graph you can find a list of up to 6 of the top influencers on Twitter for the selected hashtag. The list is created based on the activity of each user for that hashtag, on the engagement that activity creates and on the reach of their activity. This service is still in beta and the data may not be complete or accurate yet.
On the right of the list you can also see a graph where each of the top influencers is represented by a bubble (FluGraph). The Y position of the bubble shows the relative influence of each of the top users for this hashtag: The higher the position, the greater the influence. The X position shows the specialization of each user for this hashtag: A user who only tweets about this hashtag will be on the far right of the graph, while a user who tweets a lot about other hashtags will be on the left.
The size of the bubble shows the number of followers for each user. If you place your mouse pointer over the bubble, or over the name of the user on the list, you will be able to read the exact number of followers. To open the Twitter page of one of the influencers, you can click on his bubble or on her name in the list.
The biggest problem of the internet is finding what you are looking for among all the noise. Tags/hashtags are a very useful help to bring some order to the content you want to find, but as free as they are finding the most relevant tags for you can by a hurdle in itself.
My goal with hashtagify.me is to use the best analysis and visualization technologies I know to create a useful tool to find your way among Twitter hashtags and, in the near future, to the content related to them. Keep yourself updated following @hashtagify on Twitter.
I started this project using Ruby on Rails, my favorite web development tool of many years, but on my cheap VPS I wasn't able to keep the pace of the Twitter stream (not even the public XXX stream) using RoR and MySQL. I then tried using MongoDB as an alternative DB, but this didn't help. After that I decided to try node.js and redis to do all the data collection and analysis, with excellent results.
I then decided to try and use the same tools for the application server, moving as much of the application logic as possible to the client. So far, it works well enough. I still think that development on RoR is much easier for complicated projects, but for hashtagify as it is now my current solution works perfectly well. I even deploy my only static page to redis; I'm very curious to see how well this will scale as the number of visitors will ramp up.
The visual representation of the hashtags and their correlations is done using the excellent arborjs javascript library; everything else is done with jQuery.
Time permitting, I'm going to give more details about some very interesting things I discovered working on hashtagify on blog.hashtagify.me. This won't be very soon though, as for now I have too much work to do... at work, but if you're interested, you can save the bookmark and check from time to time (or follow me on Twitter).
I started developing in Basic at age 10 and went on to Assembly at age 11, on a Commodore 64. Now I'm 37 and passed through C, C++, PHP, Javascript, SQL, Python, Ruby and whatnot, but I still love the freedom that programming gives to your creativity. My day job as a project manager steals most of my time but it doesn't give me the latitude I like, so I'm always working on some side project.
Among my latest personal projects:
More about me:
If you want to contact me, tweet @danmaz74 or email me: daniele.mazzini@gmail.com
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The top 6 influencers for #tag (beta)
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