Social Network Analysis of the Social Media Club - Kuala Lumpur
SMCKL is a group that meets occasionally to explore matters relevant to social media and industry. The most recent one was about social media monitoring tools, and featured three presentations by comScore, Brandtology and JamiQ. They were interesting, but I was surprised that nobody was talking about social network analysis - so I thought I'd do a little demonstration here.
There was much tweeting going on before and after the evening, which was also an occasion for people to meet and network. Using NodeXL, I gathered all the tweets with the hashtag #smckl: in all there were 71 tweeters, and 757 'edges' (i.e. links in the form of 'Followed' relationships, 'Mentions', or 'Replies to'). The following examples only take into account the Followed relationship - i.e. I am only showing a link between tweeters when one follows the other.
A question for social media monitoring has to be: how influential is any particular tweeter? Here I'll look at two ways of visualising that.
Followers
A common measure is how many followers a tweeter has.
In these images, the size of the profile picture is proportionate to the number of followers - the bigger the profile picture, the more followers. Also, the more central the tweeter is, the more ties s/he has with the other tweeters. The person in the middle is the most embedded in the network - with the most ties to other people, directly or indirectly; on the other hand, as you can see, there are some really on the edge - with only a couple of lines attached them to the denser cluster in the middle. They are outliers, less likely to be influential within this group.
The first picture was very dense, so I have filtered out all tweeters with less than 500 followers
and with less than 1000 followers.
Again, a pattern emerges of a denser cluster in the middle with a few outliers. What this suggests is that most people at the SMCKL evening already know each other. But not all: I said above that outliers are less likely to be influential within that group - it's important to note here that the person with the most followers (@victorliew) is an 'outlier'. This suggests that he could be an important 'bridge' for this group to connect to another group. The question would be - who is he? And why are so many people following him?
Continue reading "Social Network Analysis of the Social Media Club - Kuala Lumpur"
There was much tweeting going on before and after the evening, which was also an occasion for people to meet and network. Using NodeXL, I gathered all the tweets with the hashtag #smckl: in all there were 71 tweeters, and 757 'edges' (i.e. links in the form of 'Followed' relationships, 'Mentions', or 'Replies to'). The following examples only take into account the Followed relationship - i.e. I am only showing a link between tweeters when one follows the other.
A question for social media monitoring has to be: how influential is any particular tweeter? Here I'll look at two ways of visualising that.
Followers
A common measure is how many followers a tweeter has.
In these images, the size of the profile picture is proportionate to the number of followers - the bigger the profile picture, the more followers. Also, the more central the tweeter is, the more ties s/he has with the other tweeters. The person in the middle is the most embedded in the network - with the most ties to other people, directly or indirectly; on the other hand, as you can see, there are some really on the edge - with only a couple of lines attached them to the denser cluster in the middle. They are outliers, less likely to be influential within this group.
The first picture was very dense, so I have filtered out all tweeters with less than 500 followers
and with less than 1000 followers.
Again, a pattern emerges of a denser cluster in the middle with a few outliers. What this suggests is that most people at the SMCKL evening already know each other. But not all: I said above that outliers are less likely to be influential within that group - it's important to note here that the person with the most followers (@victorliew) is an 'outlier'. This suggests that he could be an important 'bridge' for this group to connect to another group. The question would be - who is he? And why are so many people following him?
Continue reading "Social Network Analysis of the Social Media Club - Kuala Lumpur"