Mapping your Personal Learning Network (PLN)

I thought this was a very interesting activity because it parallels something we recently did in MKT1005. In that example, students researched the hashtag #UnitedBreaksGuitars and wrote an essay on what the tweets around the subject, actors, or other factors meant. This is an important point as meaning, or why things are grouped as they are, is an important considering when mapping social networks. If nodes cluster, we really want to know how and why, and understand the relationship they have. To do this particular assignment, the students used a twitter mapping website called SocioViz which I will use to map my twitter network. We can then explore who is in it, and why. Since my twitter is only used for professional purposes, and most of my tweets are about my field of data analytics, it should be easy to make sense of.

Note that there are limitations, the free version of the site will only retrieve 100 tweets from within the past seven days. While not providing a historical or longitudinal perspective, this can offer a good cross sectional or snapshot view of a particular issue, depending on its size and complexity. Also note that as we are looking at the data from a particular user account, in this case @data_professor, individual hashtags are able to be explored as search parameters as well.

@data_professor twitter account
The most active (based on the number of tweets sent) and the most influential (based on the number of retweets or mentions)

In this first image, we have the most active members of the network on the left, in this list, four of the top five are colleagues and one is a conversation I had about experiencing Canada through Tim Horton’s double doubles and Quebec maple syrup covered pancakes.  On the right are the most influential people based on the number of mentions or retweets on my account. It is interesting that I am at the top of this list, but then again, I often retweet things I find interesting from elsewhere on twitter. You will note that while many of the same people appear in this list, they are in a different order, which is indicative of how we interact. Interesting that @tourismquebec is on the list following their retweet regarding the aforementioned pancakes.

2
@data_professor twitter network

From alessandrozonin https://alessandrozonin.wordpress.com/2015/02/20/socioviz-a-free-social-network-analysis-tool-for-twitter/

“Each user is represented with a circle (a node) and is connected to another user when there are interactions between them (RT or mentions). The nodes size are proportional to the number of RT and mention received, so is a good indicator of the influence of a particular person in a network of conversations; different colors represents different community where the dynamic of interactions are more frequent.”

It is interesting to see how tweets about a topic, for example, a recent homework project involving Netflix and dimensional modeling, connects to the map. It seems that the blue nodes are my school network, notice the direction of the arrows connecting the nodes inside this cluster. Now compare this to the yellow cluster, which was a short conversation about Canadian coffee and pancakes, very random, and outside of the normal network I communicate with. The red nodes seem to be the Hub, and while a part of the college, has a unique pattern of communication with the blue group, at least from my vantage point in the network.

3
Hashtag and subject comparison

Since I am not that great at remembering to use #hashtags, I often send tweets without them. This is probably why, when looking at the graphic to the right about the topics used in tweets, bsn4416 (a course taught by a colleague) and maplesyrup (an essential part of a real Canadian flapjack breakfast) are the only subjects mentioned. I suppose that if I am more aware of it, I can start to expand this list and integrate my tweets into larger themes. This can be quite useful when I am trying to connect to audiences with similar interests and content that I would find beneficial.

I think as time goes on I will keep expanding my network, both by people following me, and continuing to follow others. As my interactions expand, for example at an upcoming conference, I am curious to see how this will impact my metrics and what it will mean for my PLN.

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