I used NodeXL (can only be used in Windows OS) and Gephi to analyze a Twitter Search Network: #justdoit. I am not a super fan of Nike but my roomie is 🙂
In the dataset, each twitter account that mentions the hashtag is a node. If the tweet is a reply to another tweet or a mention of another tweet, an edge is added between these two accounts. Here is how the data laboratory looks like in Gephi (the original datas was generated automatically in NodeXL):
Here is the first network showing up once I open the data file. This network is meaningless for us, so it needs modifications:
Then, we need to calculate in statistics. (Details found on Gephi wikipedia http://wiki.gephi.org/index.php/Category:Measure)
- Avg. weighted degree: Average of sum of weights of the edges of nodes. (differences: Average Degree: Simply the sum of edges of a node.) We always use avg. weighted degree, I think.
- Modularity: Measures how well a network decomposes into modular communities.
- Eigenvector centrality: A measure of node importance in a network based on a node’s connections.
Then, we need to adjust the nodes min and max size; apply modularity class;
The next step is very important, because we have many communities in the network which are not important for us, so we only show the first three communities:
Then partition the edge relationship: tweet, reply and mention; Run a layout algorithm – force atlas to get a better layout; show the labels for nodes and run a label adjust algorithm.
Here is my result for the first three communities using #justdoit:
(important steps for creating a beautiful network: statistics,node,modularity,edge,labels,layout)
From the analysis, we can see that nikejapan, nike, and nikewomen_jp are the first three #justdoit communities. Why do Japanese enjoy running this much? What comes to my mind firstly is the book – What I Talk About When I Talk About Running written by a famous Japanese writer Haruki Murakami. It seems that his book has made a hit!