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Mapping connections among twitter users who tweet CRM, SCRM, and Social Graph

CRM, SCRM, and the social graph are all hot topics. This is a map (a social graph) of connections among recent twitter users who tweeted the term “social graph”

2010 - May - 18 - NodeXL - twitter social graph

This is a list of the top “betweeness” contributors in that map: these are the people who most “bridge” different parts of the network.

2010 - May - 18 - NodeXL - twitter top betweenness social graph list

This is a map of connections among recent twitter users who tweeted the term “crm”: the color indicates how “between” of “bridge-like” that person is – blue is low betweenness and red is high.

2010 - April - 25 - NodeXL - Twitter - crm betweenness color

This is a map of connections among recent twitter users who tweeted the term “social crm”:

NodeXL Twitter Network Graphs: Social CRM

These social graphs are present in a wide range of social media and can be extracted and analyzed using the techniques of social network analysis and visualization.

In each graph image, the general size and structure of the population related to that term is made visible.  These images can also be represented as lists in a spreadsheet  that can rank participants by their various network attributes (like centrality, degree or the density of the participant’s local network).

The result is a snapshot of a several days of activity that reveals key participants in hub and bridge positions.  These people and their tweets may be more “influential” than other, more peripheral, contributors.

The X/Y space in these images is not where the meaning is encoded: rather it is the structure of the collections of connections that tell the story. In these images the size and density of different topics can be inferred from comparisons between images: “social graph” is far less dense than “social crm” (which is a bit of a surprise). The spreadsheet of metrics is an alternate view of the network properties of each participant, allowing rank ordering by degree or various centralities. Even in the dense network visualizations it is often possible to identify hubs as well as sub-clusters that indicate separate populations engaged in discussion around the same topics. In other images (see: these clusters are very visible. The absence of them indicates a more uniform population.

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