Following the example of tech blogger Louis Gray, who was inspired by the TechMeme leaderboard, we are starting a new experiment, the 1001 Noisy Leaderboard.
This ranks the feed items that we shared, starred or read in the last 30 days via Google Reader. We are using a variation of the Louis Gray approach, by considering not only "shared", but also "read" and "starred" items. It's a noisy formula, which we will fine-tune later on.
|1||Imaging Insider||Latest Photography News|
|2||Serious Compacts||All about Serious but Compact cameras|
|3||Digital Pro Talk||Professional Photographer shares expertise|
|4||Photoshop Insider||Scott Kelby's digital photography blog|
|5||TWIP Photo||All things Photography|
|6||PhotoWalk Pro||PhotoWalking and lots more|
|7||Pro Photo Life||Professional Photographer shares expertise|
|8||NikonRumors.com||The breakaway hit of the season!|
|9||Epic Edits blog||Resource for the aspiring photographer|
|10||Image Sensors World||Image Sensors and image sensors too!|
So how are blogs considered for the Leaderboard? We start with all the blogs that we are subscribed to with Google Reader. On a regular basis, we read, share and star individual posts from various blog/website feeds using Google Reader. Google Reader keeps track of the action, and summarizes the results, which allows us to come up with the leaderboard by heuristically combining the three sets of data (read+shared+starred).
A number of posts from the blogs mentioned above were shown in the "Situation Room". Speaking of the Situation Room, it is long overdue for a remodeling!
It's easy to do
This is a really fun thing you can all do if you are using Google Reader or any other news reader (or social bookmarking site) that keeps track of what you are reading or sharing or tagging. For Google Reader, just click on the "Trends" link which is located in the top right corner box at the Google Reader page.
You can assign different weights to each category, eg "starred" or "shared" may have a much higher weight than "read". This of course depends on how you utilize Google Reader and the different tags.
This dataset is volume-based, so if a blog publishes 100 posts in a month, it will likely have more shares/stars/reads than a blog that publishes 10 posts a month. If you are a number-crunching person, you can normalize the data - divide by the number of actual posts in the last 30 days, and come up with a more a subjectively-qualitative ranking. I haven't done this in this edition.
PS> We did not include any of the "noisy blogs" (eg this blog or RAWsumer or DSLR Map or Camera Charts or flickr pool) in the list, thus making more room for other blogs/feeds!