Brand Conversations and Stock Performance

About a year ago, we comparatively visualized conversations between two competitive brands of major sport apparel companies.  The network of communications of Brand A showed better potential characteristics for healthy and robust interaction.

One year later, and more than 1,000,000 people talking about each brand, what do we see?

Brand A (one year later)
Brand A

Several million conversations later, we still see a deeply interconnected pattern of communication in Brand A.  The large number of clusters are still visible, but the interconnections are less clearly visible.  Let’s compare with Brand B:

Brand B
Brand B (one year later)

Brand B began with fewer, more centralized, clusters of conversation, and less “cross talk” between them.

Again, several million conversations later, it has evolved to a larger version of what we saw before.  While there are more distinct clusters one year later in Brand B than Brand A, each of those clusters receives less input from others.  Visually, we see this distinction by the are of fewer “clear” clusters toward center of the later graph of Brand A. In other words, should something great happen, the network of communications in Brand A would foster faster and more reinforcing communication.  If you’re a marketer, that’s what you want.

So, what’s the difference in stock performance over the past year?  Brand A outperformed B by about 30%.

Palin’s Email Network

Gov Palin's Email Network (click for larger version)

Lots of cleanup left to do in the code parsing/cleaning up the emails, but here’s a first pass.  Seems like at least two connected networks, and surprisingly both the yahoo and the Gov’t email addresses are both in the larger one.  I wonder what the smaller one comprises of?

A very big thanks to the folks over at Sunlight Foundation for the data.

Comparing Online Brand Conversations (Sports Apparel)

Over a long enough period of time, maps of who is talking with whom mostly look the same.  Many conversations start to overlap with each other, and eventually you see a large central core and any number of outliers.

However if you look over short enough periods, you can see patterns of how those conversations start to merge.  And, you can tell a lot.  Easily.

Brand A
Brand B

Here we have a mapping of conversation partners mentioning two of the top sports apparel manufacturers, measured over the same period. Compare the shapes of Brand A (on left) to Brand B.

Brand B (on right) has a few larger clusters of conversation, lightly linked together by a few individuals participating in a number of the conversations.

Brand A has a lot of smaller conversations, interspersed with a handful of larger dense clusters; all webbed together in wide mesh.

So which is better?

Continue reading “Comparing Online Brand Conversations (Sports Apparel)”

Health Care Leans Republican

3.6-times as many former congressional staffers turned health care lobbyists and their immediate connections have network ties closer to former President Bush, than to current President Obama.

distances

The connections in the network map shown below, and used for the analysis above, include people and organizations (e.g. corporate, not-for-profit, public, etc.) the people have been identified with.

Other trivia: Continue reading “Health Care Leans Republican”

Mathematicians Do It Randomly

What it look like if you took all of the Mathematics articles from JSTOR, the digital journal archive, and mapped co-authorship of the papers? It would look something like this.  Interesting to note, that while the distribution does hold to the small world network distribution exponent, there’s some “peakiness” about it that may suggest it’s not really one network, but the merging of several.  Given the role of mathematics on so many other subjects, that would not be a surprise.

JSTOR Mathematics Authors
Largest cluster of co-authorship

Zoomable image with names, after the jump.

Continue reading “Mathematicians Do It Randomly”

NY Senate and Transparency

Congrats to the NY Senate for beginning to open more data at http://www.nysenate.gov/opendata!

Here is the network of Senator Allocations of Funding to Community Projects (CPFs): 2009-2010 by Senator or group and zipcode.  Line width is proportional to funding allocation.

communityAllocations

[click for full-size image]

Related: how do we define what’s public data? Some transit agencies are claiming copyright over transit performance.

Senator Allocations of Funding to Community Projects (CPFs): 2009-2010

Foreign Lobbying of NY Congressmen

Thanks to ProPublica and Sunlight Foundation:

…for the first time digitized one year’s worth of FARA records, making them accessible in a searchable database that allows users to easily follow the money and connect the dots. With the Foreign Lobbying Influence Tracker , anyone can quickly learn what governments are lobbying whom, how often and about what. [source @ ProPublica]

Here are the firms the Congressmen and -women from my home state have been meeting with:

Foreign Lobbying of NY Congress
Foreign Lobbying of NY Congress

and the countries of the governmental department or foreign firm paying the lobbyists:

NY Congress Lobbying by Country
NY Congress Lobbying by Country