Above is a network model I made using Gephi, which is a network analysis model that helps to demonstrate the relationship of the networks by using social media information. The network model here represents the interconnected relationship between users and a hashtag, as I wanted to see where the source of the hashtag derived from. Firstly I decided to use the Twitter streaming importer, which enables me to analyse hashtags in twitter and gather twitter users information. For the hashtag, I decided to pick #Shadowbysuga, as I saw it was worldwide trending and I could find interesting data information. Within the twitter streaming importer, I decided to apply the user network logic rather than a full network. This implies that I’ll find users who are currently tweeting the hashtag and who are also following each other. To an extent it can also pinpoint different communities of people who partake in that hashtag.
The streaming importer found 166 nodes, implying that there are 166 users currently tweeting with the hashtag, The display of the nodes however are quite cluttered, so I decided to change the display to Yifan-Hu, which now you can see the nodes are visibly clear and the network relationship are more apparent.
For further depth of the network relationship, I wanted to group the nodes to their communities So I decided to run the modularity class and network diameter to detect any community structure from the hashtag.
To see the different communities of network and their sizes, I change the color of the nodes based on their modularity class and ranking the size by Betweenenss centrality. And as you can see from the diagram, some nodes have become larger, meaning that it may imply that the user may have a bigger following/influence or official accounts,whilst the different colors can imply the different set of communities the user/node belong to.
In this final network diagram, it shows the user names and how the hashtag actually derived from one particular node/account, which was @bts_twt, as majority of the nodes are pointing to that user. And it is interesting to see, how there’s another different set of community nodes branching off from the center and expanding the hashtag to other users.
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