In our book, The Networked Nonprofit, co-authored with Allison Fine, we provide an overview of mapping your social network in Twitter and other sites using some of the social network analysis tools available.
As someone who loves to play with analytics, visuals, maps, and other geekery, I've been wanting to explore in more depth the how-to and the techniques. To take my learning deeper on social network analysis and mapping techniques and how they can be applied them to a social media strategy, I took a workshop with Marc Smith. He is a self-described "Internet Sociologist" and developer of NodeXL. The workshop was organized by colleague, Tatyana Kanzavelli
This FREE software works as an add-on template in Excel, allows you import data from Twitter, Flickr, YouTube, and Email and create social network analysis maps. It doesn't require that you know a programming language, although you need to understand the basic vocabularly of social network analysis and how to translate this to your social media strategy. After all, data is only as good as it is actionable! Otherwise, you waste a lot of time creating meaningless, but cool maps.
What I liked best about this workshop and the instructor is that it wasn't on the software features. He gave context of social network analysis, explained the value proposition for using it to help you make social media strategy improvements, and walked us through some examples in the software. I learned a lot but need to spend more time with the tool and terminology - and apply it to a real world situation.
What I'd love to see is a cheat sheet that maps out the technical social analysis terms with strategy tactics so you knew which analysis run to make what decisions. Or, a guide to before/after maps so you could see how visualizing your network helped you improve strategy.I loved getting context in workshops and Marc didn't disappoint. For example, I learned that the first ever social networking analysis map was created by Jacob Moreno. It looked at the relationships between players on a football team. Who liked each other, who didn't. Apparently this team chemistry is important to winning.
My notes:
- People are using social media for social interaction and leaving what Eugene Eric Kim called "ant trails." Social networking analysis can track those interactions (replies, commenting, like, etc) and help you make decisions with your strategy.
- Without social networking analysis, it's like a weatherman trying to predict a snowstorm without seeing a whole weather map. It gives you a 10,000 view of your ecosystem. Without this visualization, it's like three blind men touching the elephant. There's too much unstructured data (comments, replies, likes, etc) - you need to see a picture or map.
- When you map your network, it tells you a story. Who is connected to whom? How are they interacting? Where are the clusters? Who are the influencers? Who are the bridge builders between clusters? Who is in the edges? Who isn't connected? Who should I spend my time responding to and cultivating? The analysis looks at frequency of interaction, relationship structure (two-way, one-way), and helps reveal structural similarities.
- An individual's online social behavior and connections can be looked at against social networking analysis characteristics and different profiles or roles are then created. He showed an example of this in an online community SNA and pointed out the lurkers, conversation starters, trolls, based on particular patterns in the map.
- He covered social network analysis 101 and key metrics including centrality, cohesion, density, and betweeness. That last one, "betweeness" describes how long the path is between people. There is great power and responsibility in the people have high betweeness. To illustrate this, he showed a social network analysis based on voting record of senators. Not surprisingly, it clusters into two groups, plus there a few senators who ranked high in "betweeness." Can you guess who they are?
- Social network analysis help you figure out who, where, how much, and how people in your network are connected. Seeing these relationships in aggregate helps you make decisions.