Madalyn Sklar asked how the various network graphs I make work. They’re based on a few different components and ideas, but the big idea is using a centrality algorithm to determine influence.
What is Centrality?
Centrality is a way to calculate how one node – like a Twitter handle – interacts with other nodes. The more a node is referenced, the more important it is.
Why Does Centrality Matter?
Centrality is, for me, the best way to judge influence in social media. Why? Because it’s easy to be loud and relatively easy to build a large audience (especially if you have dollars to spend). It’s less easy to organically convince a lot of people to talk about you (bots aside). The more other people talk about you, the more they reference you (whether or not you talk a lot), the more important you are in an influence network graph.
As mentioned above, there are certainly ways to game centrality mapping, such as the use of bots; however, the average social media marketing practitioner is unlikely to deal with them in any substantial way on a day-to-day basis.
How Do We Build Influence Network Graphs?
I walk through the process from beginning to end in this video. The only caveat is that my data-processing middleware is confidential. The data source used in the video is by Brand24, and the network graphing software package is an open-source package named Gephi.
Don’t see anything? Watch the video on YouTube.
What Do We Do With Network Graph Outputs?
Once we’ve built our graph, we use the data to:
- identify influencers
- identify influencer precursors
- perform outreach to connecting nodes
- identify trends
- find unexpected connections among groups
- build influence key topics
- build advertising lists
Properly done, influence network graphing provides us with nearly unlimited uses to turn our analysis into action.
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