Suppose you’re just starting out on Tumblr. How to build your audience in the most efficient way? Obviously, you would want to reach the people who belong to the communities that would be more likely to repost the kind of content. It’s even better if you can also find the main hubs. Using a bit of network data mining and Gephi visualization tool all this can be done in a few minutes and we’ll show you how.
Just like other social platforms Tumblr doesn’t give you enough tools to have an overview of their network. However, they have a great “Notes” feature, which lists all the reposts that the post have gotten. For example, if you find a popular image you can always see who posted it first and who reposted it afterwards. Our task is to build a social graph from this data. We can then trace how the piece of content “traveled” through the network. If we repeat the same operation for several posts and find some regular patterns, we would be able to identify the most influential posters (hubs) and communities (groups of people that tend to repost each other’s content).
Let’s use a real example. We have just set up a new Tumblr blog for our client, Way to Russia Guide – a popular internet resource about Russia. How do we quickly build the audience that is relevant for the content they are going to post? First, let’s look for the posts tagged “Russia” by opening this link: http://www.tumblr.com/tagged/russia
. Let’s open the ones that have the most “notes” or feedback on them. These are by users millionsmillions, fuckyeahabandonedplaces, iheartmoscow (a few posts can be analysed), passionaterussian, and touchrussia. Many more blogs could be added, but we’ll stop here to keep this example simple.
The next step is to copy and paste all the repost data into a text file. It will look something like this:
Each “A reblogged this from B” statement is a link between two profiles (A and B). That’s the information we need. The next step is to remove the stuff we don’t need (Liked posts and comments) and replace ” reblogged this from ” with a comma “,” so that we have a nice and neat CSV file that just lists the links between the profiles. This can easily be done by search / replace function of any editor. In the end we get something like this:
The final step is to open this .csv file in Gephi and visualize the network.
Then we should apply forced atlas layout…
Surprisingly, even though we used different blogs found through “russia” tag, it’s a connected graph, meaning that the data we extract from this analysis will be quite relevant for us. If we “follow” the main hubs in this network and thus make them notice our content, we’ll have access to the whole network.
Then we can delete the “orphan” nodes (the ones that were just “liking” the content – there are about 50% of them in this graph. We also make the nodes with the higher betweenness centrality bigger (those profiles that appear more often on the shortest path between any two randomly chosen nodes in the network – a measure of the node’s influence in the network).
And finally turn on the captions to see what these profiles are…
The most influential ones in the network are iheartmoscow, fuckyeahabandonedplaces, khrushchev-is-my-homeboy (interesting, because we didn’t notice this one before, but this blog was often reblogged by others, although it did not originate many posts by itself – a sign of an important hub for proliferating content across the network), russophilia. Also, the two very important ones are integral87, tvoya-krasatina (both were not detected before, because they often link different hubs together, but they didn’t originate the content we found) and vogueofrussia, as they connect different communities together. Touchrussia and iloveeurope are also important hubs.
The final step is to “Follow” all these blogs on Tumblr and to re-blog or like some of their posts. This is usually enough to get noticed and followed back, making it much more likely that they will see and interact with your own content.
Once you accumulate quite a few Tumblr friends and reblog some of their posts, just look for the ones that have the most “notes” and repeat the procedure described above. This will allow you to build a strong and interconnected network. It’s also important to sometimes go outside of one’s immediate “filter bubble” and continue to search for the relevant “tagged” content that doesn’t belong to your immediate circle of Tumblr friends. This will enable you to always expand your reach and find the new and interesting information to share with your readers that is also unique to the blog circle that you belong to.
Let us know if you’d like us to do such analysis for your blog or if you have any questions.