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Common Ground – Finding How Two Conferences Connected Using Social Media

This post presents the mapping of Twitter traffic related to the recent Impact Convergence and Evaluation 2016 conferences of the American Evaluation Association (AEA). It demonstrates how we could measure and ascertain that AEA’s efforts to reach out to another audience, the social impact measurement and investment “community”, was successful. Success would mean that participants in both events were frequently in contact with each other on any number of issues, whether they attended both events or just one.

The next post will look at how the analysis of the network could guide a strategy to build on it and maximize the potential for cross-sectoral communications. A third post will demonstrate how the creation and ongoing maintenance or growth of an online communications network can be monitored and evaluated.

The American Evaluation Association held two related events in the last week of October 2016. These were the Impact Convergence 2016 and AEA Annual Conference 2016, one happening immediately after the other and involving many of the same people. 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.
unconnected components

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. We will discuss what makes them “key” in the next post. 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).
entire network

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.

AEA first deg neighboursIMPCON first deg neighboursCombined neighbours

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.


creating subgraph


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.

boxed clusters layout

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 we will discuss in the next post.

Note also that many of these central players also appear to be special in some way, as indicated by the larger size of their nodes. This will be explained in more detail later, but for now let’s say that these are well-regarded members of the network who are important to the spread of information.

Next time we’ll look at how the mapping and its associated metrics and features could help to guide a communications strategy both building and using the network. Following that, we’ll see how the success of such a strategy could be evaluated.