How To Evolve Blog Commenting In Three Easy Steps
I’ve been thinking about social media a lot lately and how people interact with Web 2.0 technologies and services themselves as well the other people who use those services.
In particular, I’ve been thinking about the comment function of blogs. And how to improve that function.
As a football nut and a die-hard Vikings fan who still reads everything about the team despite their abysmal record this year, I spend a lot of time on Vikings blogs, especially the Star Tribune‘s superb Access Vikings blog by their beat writers Judd Zulgad and Kevin Seifert.
I’m sure it’s one, if not the, most popular Strib blog. There is certainly a lot of participation by its readers. A post from yesterday about Daunte Culpepper coming back to town boasts 162 comments.
I often read the comments because there’s some very good stuff in there, but wading through the crap and the banter from one reader to another is usually a waste of time. Separating the conversational wheat from the chaff is a problem for all blogs with a lot of reader participation, not just Access Vikings.
I’d love to see some smart programmers at Six Apart or Blogger or some open source guru create a plugin for WordPress that would do this: Allow me (as a registered user of the blog in question) to annotate the blog post and apply highlighting and/or my own comments in the form of, say, a sticky note(s) that I can place anywhere on the page.
It would also have a function like the fantastic ClickTale service that gives you a "recording" of each site visitor’s behavior on the page by following mouse movements. I often, for instance, click and drag my mouse over text to highlight interesting text. I don’t know why I do it, but I do. That behavior could be captured.
Combining ClickTale recordings with the sticky notes data should give you enough aggregate data to create a heat map for that page.
Apply heat maps to the blog comments and, voila, as a blog reader I’ve got a quick visual representation of the relative "interestingness" of blog comments. And as a blogger, I’d get extremely valuable data on how people were interacting with my blog.
Perhaps someone could come up with a semantic solution that analyzes the text of blog comments and the frequency with which blog comments refer to other comments or commenters to determine "interestingness."
However it’s arrived at, we sorely need a solution and it seems to me that the technology is probably already in place to create it.