Posted 7 March 2012
Tagged to HTC, Photography
Since I joined HTC back in October, I’ve gotten to play with a lot of our phones and I’ve been quite impressed, especially with photos from the camera. What I love most is how people are surprised that they were taken with a cellphone — even people in HTC!
Here are a few of my favorites:
Frosty morning in Seattle
Check out a few more after the jump.
Posted 14 August 2011
Tagged to Politics
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).
||Net Worth (low)
||Net Worth (high)
|Chris Van Hollen
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.
Posted 7 August 2011
Tagged to Facebook, Measurement, Social Media
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.
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.
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.
Posted 18 July 2011
Tagged to Measurement, Social Media, Twitter
After mapping the best-networked DC Twitter technorati, I figured I’d try it out on an environment I’m not familiar with: Seattle.
Here are the results of the best-networked Seattle tech Twitter users:
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?
For some reason the app created multiple entries in the relationships database table, which quite inflated the numbers above. Since a “relationship” means someone within the network analyzed is following that account, you cannot have more followers than there were accounts analyzed. I should have noticed this when writing up the results (alas, late night hacking). This doesn’t change the ranking order much, though JenniferCabala and johnhcook did swap places in 17 and 18 (sorry John). Monica Guzman‘s follow-up questions brought the error to my attention!
I should also say that this is pretty experimental and involves hacking on nights and weekends. Nonetheless, it seemed to have worked well on the DC network.
Seattle skyline photograph by Bala