A Social Network Visualisation Graph for a Local Business

Thanks to a couple of weeks of trying, and some major digging into APIs and other coding options... I have gathered enough data to put together a rudimentary network of FB relationships of users interacting with Lawrence Companies (LC). I've got some feedback on what that looks like, and some decisions that can be taken now that the data can be put in one place for review. I offer up a couple of options of what can be done with these types of data... after all, you never offer up just one option to a client... or potential client.

To recap, I gathered all the reaction data to LC's FB page since 2011, as well as comments, counts of shares, and mentions. I chose, for the purpose of this graph, to focus on the reactions. There were 3 times as many reactions as shares, mentions, and comments combined, and I've yet to find a way to track shares to individual accounts.

The resulting pull of friends data resulted in 18,940 data points. I forced reciprocal relationships in the data, as FB doesn't make a clear distinction between following and friends. For the purpose of this graph, the assumption is that if you show up on someone's friends list, that you have a reciprocal relationship. Additionally, this graph represents only relationships, and not conversations. I've chosen to identify relationships among those who reacted to posts from LC's FB page.

On to the data. I chose to filter based on a degree range of 6, and removed outliers (of which there were 6) with no connections to the central group. I've anonimised the graph data, because nobody wants to see their friends on FB made public. You will note there are 35 data points, but that the number 7 shows up twice. The repetition is deliberate and not an error. Employees are noted with green nodes, and non-employees with red.

The key question I had was how robust the networks of those reacting were. As stated in the previous post on this topic, approximately 20% of the volume of reactions came from employees. It's also no surprise to me that the most robust networks are employees (1,5,6,7,8,12, and 16). The selection bias of the reactions is most likely the cause... but please note 1,5,7, and 8 have very central positions in this graph... hence a lot of strong connections. The duplication of 7 as an employee and a non-employee results because of that node having multiple FB accounts, and identifying with multiple employers. Had the reactions come from larger non-employee populations, we might have seen other robust networks represented.

So... that's a lot of gobbledy-gook, telling me virtually nothing. What do I do with this thing?

You ask yourself what the objectives of your social outreach is.

I mentioned in a previous post that there are articles discussing truck driver shortages. If increasing driver applications, or increasing incoming owner-operator applications is a key measure, then perhaps working with node 8, a maintenance worker with several truck driver friends may be a good target for beginning outreach. By leveraging experience of node 16, an IT employee, LC should be able to create podcast content that can be gathered from that node's driver friends and shared through LC's main content sites. And good old node 7... that driver has a robust list of other truck driver friends that should have content directed to them. Not on this graph, but also of importance, are the three interns from LC who have reacted to posts and have a group of friends including drivers.

How that content is distributed is another conversation for another post. Let's hold off until the next post for that.

Coordinated Social Engagement by a Traditional Media Giant

I review data... I visualise data... it's my job.

To that end, I keep an eye on who is publishing what and how. So, when I see a weird trend, I stop and ask to see if it's as weird as I think it is.

The Wall Street Journal recently published an online article from Jeanne Whalen, "Why the U.S. Pays More Than Other Countries for Drugs", and it kinda went ballistic. If you look at the page, you see 5,523 shares from Facebook.

OMG!

Not really.

This is a huge topic, right now. Of course an article on this will get a lot of shares.

What struck me as odd, then?

I want to know who shared this article.

Part of what my agency does is track social profiles for a lot of different categories. It's kind of our thing. It's proprietary, and it's awesome. It's called MDigital Life. We started with physicians, and have expanded into other areas, such as media outlets and journalists.

Which is why I can tell you with authority that there were at least 25 of our identified journalist influencers who shared this article.

"Yawn..."

But wait, there's more.

Of those 25, 17 of them were from WSJ.

17 of 25.

Who organises that well? Apparently, WSJ.

At first, I'm thinking, "Okay, it's mostly their health desk."

Nope.

To name a few, I've got a page one editor, an EMEA markets editor, a relationship columnist, and a housing and mortgage market reporter. I also have the Asia, Canada, and Infographics desks sharing the article.

Colour me impressed if WSJ got their teams together to put in a concerted effort for blowing this article up.

If not, colour me wicked surprised that such a huge, unlikely coincidence occured and gave them an unexpected windfall.