Darren Krape

There is an old adage that a group typically won’t vote to reduce it’s own power. It isn’t a far jump to extend this to tax increases: few people are likely to vote an increase in their own taxes. This got me wondering about the new Congressional “super committee” appointed to propose a solution to the debt crisis. Most particularly, which members have the most to lose if taxes are increased on the rich?

Here is a table of the members’ net worth, sorted from richest to poorest. Since the data provides only range of net worth, I’ve sorted by the lowest estimated amount (sorting by the high estimate has only a marginal impact on the order, with Senator Kyl dropping several places).

Name	Chamber	Representation	Net Worth (low)	Net Worth (high)
John Kerry	Senate	D-MA	$182,755,534	$294,869,059
Fred Upton	House	R-MI	$7,010,173	$25,651,000
Rob Portman	Senate	R-OH	$6,469,080	$14,965,000
Dave Camp	House	R-MI	$2,966,100	$10,515,000
Pat Toomey	Senate	R-PA	$1,127,038	$2,905,000
Jeb Hensarling	House	R-TX	$928,025	$2,270,000
Jon Kyl	Senate	R-AZ	$519,090	$746,082
Patty Murray	Senate	D-WA	$449,017	$1,185,000
James Clyburn	House	D-SC	$212,010	$582,000
Chris Van Hollen	House	D-MD	$148,007	$445,000
Xavier Becerra	House	D-CA	$100,054	$1,424,999
Max Baucus	Senate	D-MT	$13,013	$204,000

Data from OpenSecrets.org

I should note that this is a rather blunt analysis. Due to different forms of taxation someone that has a higher net worth may or may not have to pay more taxes if there is a change to the tax regime. Additionally, this is net worth, which is not the same as annual income and is taxed differently.

As an aside, I plan to start a non-profit to help raise Senator Baucus out of his relative poverty.

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HTC recently posted to their Facebook wall a simple question: “How many mobile phones have you owned?” Within a day they received about 2000 answers. Using the Facebook’s Graph API I wondered how hard it would be to automate the analysis to find out the average number. Here is the result:

Note: I removed about a dozen responses that stated ownership of more than 500 mobile phones. While this is probably feasible, it was a small minority and skewed my results too much.

Previous Manufacturer In most cases, the owner upgraded from a phone by one of these makers to an HTC. As such, these are the “losers”, with Nokia coming out worst.

Current Model Commenters more readily their shared current model. As expected, most of these are HTCs, though some commenters left HTC for a new maker.

Comments by User Gender While expecting a male bias, I didn’t expect it to be so one-sided. I’m interested to know if the page demographics are similarly skewed.	Comments by Locale Facebook doesn’t provide location for un-authenticated requests. Fortunately, locale is a useful proxy. (EN includes US and GB)
Comments by Sentiment This measures the sentiment toward HTC and their products, not the sentiment of the comment. The sentiment was reviewed manually, so the accuracy is quite high. However, the small sample-size should discourage reading too much into this set of results.

The process for this was actually quite straight-forward. First I used the Facebook Graph API to download all the comments into a database. Because I didn’t want to manually review 2000 comments, my first pass automatically checked for comments containing only numbers. This took only a few minutes to code and knocked out 400 comments.

I then selected very short messages with the assumption that they were just numerical answers with punctuation. This proved prescient since many people noted the number of phones they’d owned and appended smiley faces. This took care of another 250 or so messages. To review the final set, I coded a small web application to manually step through each comment. Through this application I could also note the comment sentiment, past and current phones, and other facets. After some refinement to the app, I was able to review about a dozen messages a minute and complete the entire review in less than two hours.

I looked at several other Facebook posts with a large number of comments, but choose this one because many of the answers would be uniquely easy to automatically parse. If the question required a prose response, the analysis would need to be done more manually, which would be quite time-consuming. Crowd sourcing could provide a solution, perhaps through something like Amazon’s Mechanical Turk.

Because I coded the web application solely for this topic, I was able to very specific in my search parameters, which allowed for some interesting insights. For example, it was fascinating to see people’s ownership history (especially how their brand-loyalty has evolved over time). Similar analysis using an off-the-shelf tool (Sysomos, Radian 6, etc) would not have provided this level of customization and granularity.

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After mapping the best-networked DC Twitter technorati, I figured I’d try it out on an environment I’m not familiar with: Seattle.

The results

Here are the results of the best-networked Seattle tech Twitter users:

Rank	Handle	Name	Relationships	Followers
1	ShaunaCausey	Shauna Causey	867	25530
2	moniguzman	Monica Guzman	602	13516
3	BillGates	Bill Gates	564	2999410
4	jennihogan	Jenni Hogan	529	33243
5	TheNewsChick	Linda Thomas	527	15648
6	KevinUrie	Kevin Urie	524	3837
7	briancrouch	Brian Crouch	520	11354
8	ChrisPirillo	Chris Pirillo	492	96065
9	jshuey	Jeff Shuey	485	16870
10	LilyJang	Lily Jang	483	15718
11	seattlewinegal	Barbara Evans	467	12300
12	Shih_Wei	Veronica Wei Sopher	455	3670
13	Ryanintheus	Ryan Hodgson	440	2777
14	JessEstrada	Jess Estrada	433	4543
15	BMW	Brian M. Westbrook	429	7269
16	ColinAC	Colin Christianson	415	5505
17	JenniferCabala	Jennifer Cabala	404	3196
18	johnhcook	John Cook	403	5740
19	thinkmaya	thinkmaya	397	6218

Shauna Causey, who manages social media at Nordstrom, ranks the highest, by quite a margin over second-placed Monica Guzman, a tech journalist. Unsurprisingly, Bill Gates, perhaps the world’s foremost geek (and I use that approvingly), ranks highly in third. Two broadcast journalists, Jenni Hogan and Linda Thomas, follow-up in forth and fifth. Kevin Urie, founder of Social Media Club Seattle, ranks sixth.

Rounding out the top ten are: Brian Crouch (7th), social media manager for the Music Group; Chris Pirillo (8th), a social media strategist; Jeff Shuey (9th), board member of the Seattle Social Media Club; and, finally, Lily Jang (10th), a local TV anchor.

For the methodology and additional information, check out the post I did on my DC analysis.

A few notes

• This analysis mapped 1910 individual Twitter accounts, resulting in 4.2 1.4 million relationships to crunch. Interestingly, this is fewer accounts than the DC analysis (2700 accounts), but more than twice as many relationships (1.9 million) Check out the update below.
• There were more local journalists in my Seattle results, likely due to the wider network of relationships indexed as compared to the DC analysis. I debated removing these accounts to focus just on people self-identified as involved in the tech scene, but I didn’t want to adjust the results too much.
• There were a good number of “institutional” Twitter accounts that I filtered out. Check out the complete list of results to see how they scored.
• It’s interesting to see how well Kevin Urie was ranked. Despite having a relatively small following, he was very well connected within this network, no doubt through his work with the Seattle SMC.
• Oh, and why Seattle? My girlfriend is working there and can help validate the results.

Know the Seattle tech scene well? How do my results look to you? Anyone I missed?

Seattle skyline photograph by Bala

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When it comes to social media for business, there is one question on everyone’s mind: Who are the influential people in my area? Unfortunately answering this is easier said than done. Take Twitter for example. You could look at a user’s total followers or the number of lists they are on, but those are blunt instruments at best. When you’re focused on a specific topic, those numbers can be downright misleading.

After mulling this over, I figured a good measure of potential influence would be how well networked a person is in a particular topical environment. To test this hypothesis I decided to look at an area I know pretty well: the Washington DC tech scene. Since I already have a good sense of this community, I could verify the analytical results from my own knowledge.

The Results

After doing my analysis, here is my ranking of the top ten most networked individuals:

Rank	Handle	Name	Relationships	Followers
1	corbett3000	Peter Corbett	671	7,980
2	dcconcierge	Shana Glickfield	644	5,979
3	FrankGruber	Frank Gruber	571	27,172
4	cheeky_geeky	Mark Drapeau	539	19,652
5	DCeventjunkie	Lisa Byrne	492	5,755
6	shashib	Shashi Bellamkonda	482	14,287
7	digiphile	Alex Howard	462	78,433
8	alexpriest	Alex Priest	427	4,940
9	SteveCase	Steve Case	414	416,114
10	digitalsista	Shireen Mitchell	408	7,562

Overall, this squares pretty well with my knowledge of this community. Number one, @corbett3000, belongs to none other than Peter Corbett, the CEO of iStrategyLabs, a leading DC technology firm and organizer of many DC tech conferences (including the upcoming 10-day DC tech festival). Coming in second is Shana Glickfield, who goes by @dcconcierge, and is DC’s consummate networker and FOMO sufferer. Rounding out the top three is Frank Gruber, @frankgruber, CEO of TechCocktail.com. Number four is every one’s favorite Microsoft staffer, Mark Drapeau who tweets at @cheeky_geeky.

It is particularly interesting to see how a large Twitter following does not necessary translate into a significant number of relationships within this particular network.

For kicks, I threw the top forty accounts into NodeXL to see what the network looks like:

Network of the top forty DC Twitter technorati. Each line represents a single follower-followed relationship. It is interesting to see which accounts are more central within this small network and which are more peripheral.

Methodology

And the big question: how did I arrive at these results? Here is the process I used:

1. My starting point was trying to figure out how to measure the number of connections within a particular geographic region and subject area. For this, I needed a good index of who was active in the DC tech sphere on Twitter. Fortunately, this part of the job has been done by the community in the form of Twitter lists. I trolled through a large selection of Twitter lists looking for ones that had “DC” and either “social media” or “technology” and entered those info my database.
2. I then went through each list and saved all the individual accounts on that list.
3. From the users on these lists, I ended up with a database of about 2700 Twitter accounts that, through the Twitter lists, were related to the DC tech scene.
4. In the most time-consuming part of the analysis, I set-up a system to download all the people these Twitter accounts followed. Since a follow is an expression of interest in the followed account, I counted that as a “vote”. This is much the same way Google considers a link a vote of confidence in the linked page.
5. After a few days of downloading data from Twitter, I had a database of nearly 2 million follower-to-followed relationships. Using this index, I checked which accounts were most frequently followed and ranked them according.
6. I was able to automate nearly every set after selecting the Twitter lists, but the final step requires a good deal of “eyes on screen”. The final list included a number of very widely followed accounts, but not ones I was interested in. Since I was only focusing on individuals in DC, I removed a lot of institutions and people outside of the city. For example, the top three @mashable, @barackobama, and @techcrunch are all widely followed, but not members of the DC tech scene. As such, I excluded these from the final results. (It is interesting that Mashable has a much wider following in DC than Techcrunch. I imagine if I was looking at Silicon Valley the ranking would be inverted.)

I was able to automate most of the above steps using a small app I coded, so much of the data collection took place while I was fast asleep in bed. Due to Twitter API data call limits, most of this was done using automated CRON jobs. Steps 1 and 6 though were not automated and required diligently going through the data.

Final Thoughts

So that’s it? Certainly not, this only measures who is widely followed within this topical subset, not who is actually influential. You’d need to combine this with other measures (frequency of retweets, ability to drive conversations, and so on) to get real sense of influence.

Fortunately the results do square pretty well with my understanding of the DC tech scene, which helps validate the approach. Most likely I’ll do some more playing around with this technique, so stay tuned.

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