Social network analysis is a powerful tool for marketing, planning and strategy, and for planning and evaluating public policy and programs where the goal is to disseminate information and technology, create collaborative partnerships and strengthen communities.
SNA lets you see who connects to you, and who connects to them. You can understand social media traffic: who is talking to you, about you, and about what; and you can mine your data on sales contacts, co-attendees at meetings and conferences or any other way that other people or organizations are connected or refer to you.
With this technology, you can identify the connectors in your organization’s networks and to whom your research products and publications reach. You can identify influencers who occupy important central positions in your network of customers or stakeholders. When combined with state of the art visual data presentation, network maps can tell a clear and compelling story about your organization’s reach and influence.
Mapping Member and Organization Connections with Communications, Social Media and other Relationship Data
Here’s a great example of social network analysis in action. I mapped this Twitter network for the Canadian Evaluation Society 2016 annual conference, and later did the same for the American Evaluation Association conference in Atlanta.
This network mapping was done with a several social network analysis software packages on data downloaded directly from Twitter. It is presented here using D3.js to replicate its structure.The image presented here is interactive: double-clicking on any node allows you to see that person’s immediate connections. More interactive views of this Twitter traffic can be seen in the D3.js demo pages attached to the Portfolio page of this site.
The following image is from the five-minute presentations that I did during the conference, to encourage use of the conference Twitter hashtag and create conversations and cross-talk on what was happing. It presents the overall Twitter network and several component clusters of more tightly-connected colleagues. See my blog post on mapping conference Twitter traffic on this site.
Another example, involving two connected events is our mapping of the Twitter traffic generated by the American Evaluation Association’s Impact Convergence 2016 and Annual Conference 2016, held back to back in Atlanta, last October.
The Impact Convergence was for people involved or interested in the idea of measuring social value and supporting or financing initiatives on the basis of having measurable social value). The evaluation conference involved people involved in evaluating the results of policies, programs and investments in mainly social, economic and health/education initiatives. Clearly there would be an overlap in interests, and AEA wanted to promote interchange between these communities. AEA naturally also hoped to be able to gauge their success in fostering this “convergence”. Capturing and mapping the Twitter traffic generated by these events as a social network was one way to do it.
If there was no overlap between the Impact Convergence and Evaluation 2016 Twitter traffic, then we would have seen something like the first image: two entirely separate communities.
Instead of two non-overlapping groups, what we did see was the second: intersecting communities, several of which are clearly either primarily about Impact Converge or Evaluation 2106, but also clearly overlapping, and particularly where a number of key network members are concerned. A set of metrics generated while mapping the network identifies individuals and groups that are important to the structure and potential for information flow of the network. The key “both-world” players are grouped between, rather than immediately surrounding, the centres of @Impact_Converge and @aeaweb (the official Twitter addresses for these events).
Understanding this observation and its implications is assisted by thinking of this as a matter of gravity. In this “force-directed” network layout, all nodes are repelled to a degree, but the repulsion is countered by gravitation to other nodes that is stronger the more often the nodes are in contact (via Twitter). Proximity in the mapping, therefore, is a function of connection. Nodes that share many mutual connections will tend to be proximal in the mapping.
Reasonable proxies for the centre of the IMPCON and Eval 2016 networks are the locations of the @Impact_Converge and @aeaweb nodes. Not all tweets included these handles, but many did, and those not referencing them and using the conference hostages of #IMPCON16 and #Eval16 would still tend to be connected to others who did, for one event or the other. Gravity would thus bring them together into the neighbourhoods surrounding these nodes, as is clear in the mapping.
The first degree (immediate) connections to these nodes extend into these neighbourhoods, overlapping in the space between them. The significance of this is further explained next.
Selecting (approximately) the members of the group connected directly to both of the main event nodes, and selecting their first degree neighbours, gives us a “sub-graph” of participants who are very close to either or both events in terms of the sources and destinations of their own communications or those with whom they are directly connected.
You could use this group to represent the main overlap of the events.
Another way of looking at this is by differentiating between clusters,“pulling them apart” as in the final. layout. This makes the clusters, identified in the first mapping by the same colours as in this mapping, more apparent.
The gravity of mutual connection pulls the people in G1 in particular away from both of the event centres, while some of the central overlapping group are still located within the IMPCON-centred cluster.
The same is true of the other smaller clusters as well: while many of their members may be connected to the central event nodes, they are still more strongly connected to the others with whom they are clustered.
Most of the central overlapping group are found where circled in two large and highly interconnected clusters. Being able to zoom in on subgroups like this is handy strategically as part of a communications strategy.
Communications during and after the event:
With these visual mappings and the analytical data generated, along with the raw contents of the tweets, a conference organizer can see what is working (we did this while the conference was in progress), what is creating comment and “buzz”, and convey this sense of immediacy and excitement to participants.
Going forward, having mapped the Twitter, and if available, Facebook data related to your event, you will know who the major players are in your audience for future communications. You will have guidance on who will pass on messages, who will connect others that you might otherwise miss, and what groups exist for whom you need to craft particular ideas and messages.
We can work together to obtain periodic Twitter/Facebook “snapshots” so that you can track the size and health of the ongoing communications network. Email and organization-to-organization hyperlink networks (where one organization publishes another’s website address) can also be mapped. Alternatively, I can provide training so that you can do this yourself moving forward.
Further analysis can also be done using the attendance records for sessions occurring during the event. Again, this will help you identify communities of interest for whom you can shape your agenda and messaging. All you need for this is session registration or signup/attendance data.
For more information on event mapping, please see the Blog page of this site on conference social media mapping and on understanding networks of interest and collaboration.
Other organizational and collaborative/community networks
There are obviously many applications of social network analysis for organizations with members, clients, customers or collaborators. A key application is mapping networks of researchers and the spread of research and information, as I did with the National Research Council Canada and Cathexis Consulting for the Canadian HIV Vaccine Initiative. This image shows the potential network of connections between international AIDS researchers based on efforts to bring together a collaborative community.
The following image presents the hyperlink connections (meaning one organization have another’s web address on their own website) between am major charitable organization and a network of others. I can’t help calling this “data fish”.
Network mapping can greatly assist organizations in better understanding their memberships.The next image is from a mapping that an AEA colleague and I and did using data from the American Evaluation Association on the cross-membership of those registered in the Social Network Analysis Topical Interest Group (TIG) and all other AEA TIGs. Note that this analysis used only existing data.
The AEA network mapping presented here, and the data behind it, reveals a great deal of information about the SNA TIG members’ interests and affiliations in other areas of practice.
Social network mapping and analysis is very useful for capturing and understanding the nature of many kinds of networks – of organizations, people, citations, ideas and any other set of
interrelated entities. Mapping the web of organizations and connectivity of citizens among each other and the community is a powerful way to understand and guide efforts at community development. Understanding the potential for and actual observation of diffusion of research and information from government/academia to industrial innovators and collaborative networks is a natural application of the technique.
Again, please see this site’s Blog page for more discussions on applications of social network mapping and analysis.
Capabilities for mapping complex systems become equally powerful for understanding webs of organizations, functions and results, such as, for example, my recent mapping of Ontario’s immunization system. This approach allowed the client to focus on key connections in the midst of a large and complex set of factors. This is discussed at more length in the section on “Evaluation Support”, on this website.
My Social Network Analysis clients include Doctors of B.C., Circum Network (for the Rick Hanson Foundation/International Collaboration on Repair Discoveries), Cathexis Consulting Inc (for the Canadian HIV Vaccine Initiative), Tactix Government Relations and Public Affairs and the National Research Council of Canada and, for Twitter traffic-mapping, the organizers of the 2106 Canadian Evaluation Society Annual Conference and the recent 2016 Impact Convergence / American Evaluation Association Annual Conference events in Atlanta. Complex system/environment mapping clients include the Ontario Ministry of Health and Long Term Care, the United Way of Calgary and Human Services, Alberta, as discussed under “Evaluation Support”.