The top-secret algorithmic mojo 

The one thing I mentioned that I haven’t talked about anywhere is how the top 100 algorithm works.  It’s pretty simple, actually: it’s PageRank.  I implemented it in the same way it’s described in the original paper. (I’m no computer scientist, but I’m pretty sure I’ve got it right. Also, I’m hoping that I don’t get a cease and desist for patent infringement.)

The interesting thing for me was applying a solution that works well in one domain (web search) to a completely different one (Twitter conversations).  And I was happy to see that it confirmed my intuitions about influence within Twitter–basically that @ replies are the most useful metric so far for understanding who is influential.

Anyway, listen to Connie’s podcast for more details.

Hashtags

I also started tracking hashtags. Hashtags are a simple way of tagging in your tweets. You place the hash mark (#) in front the word or words you want to tag (e.g. #sxsw, #microformats, #yahoo), and then sites like Terraminds and Hashtags.org and eventually Tweeterboard can aggregate them. [*]

Right now Tweeterboard is capturing any alphanumeric tags. If you want to use multi-word tags, you can separate the words with a plus sign (+) or underscore(_). All tags are normalized, which means they’re converted to lowercase with pluses and underscores removed, before they’re stored. So #san_francisco ends up being the same as #sanfrancisco, and #new+york+city becomes #newyorkcity.

This is a preliminary implementation so I can see what the data looks like. I’ll eventually show your tags, and let you find people and URLs by tags. But for now I have a couple of minor technical details to figure out.

Just for fun, here are a few of the most popular tags since I’ve been tracking:

• ipan – 226 mentions
• 8217 – 142
• 1 – 114
• ip2 – 79
• cparty – 78
• 2 – 63
• lift08 – 50
• 8230 – 40
• socon08 – 38
• barcampmd – 35
• 3 – 34
• 4 – 30
• twitter – 28
• supertuesday – 28
• music – 28
• caprimary – 27
• myspace – 27

You can probably guess what those technical details are. Anyway, if you know your way around PHP and regular expressions and you have some advice, please add a comment.

* In hindsight, I probably should’ve written about hashtags in my book.

A case-sensitive app? I like to think I’m smarter than that, but I had to hunt through the database and code to understand what was happening. The good news is that Tweeterboard doesn’t treat “MaxWeb” and “maxweb” as different users. However, it does store that user name in a couple of different places. In the users table, it’s “MaxWeb” which is how it’s spelled on Twitter. In the table that records conversations between users it’s “maxweb” because the function that extracts user names from tweets converts everything to lowercase.

On yndygo’s profile, you see “MaxWeb” under the “Gets Love” tab because that name is pulled from the users table. Under the “Gives Love” it pulls the name from the conversations table so it’s “maxweb.” If Tweeterboard were truly case sensitive, “MaxWeb” and “maxweb” would be treated as separate user names and would appear in both places. But they don’t–a fact I confirmed with a bunch of manual database inspection so, while I wouldn’t bet my life on it, I’m pretty sure that’s the case. Like 99%.

In the next day or so I’m going to tidy things up so all user names appear in lowercase. (I’ll leave it up for now so you can see the differences I point out above.)