Yesterday, I bumped into PostRank. This system is collecting data from various social systems like Twitter and provides a service where you can type in an url of a blog post or a entire blog. You get a PostRank depending on the popularity of the URL.

I wrote a plugin for Atomisator and ran it on my own blog. Here's the result: http://ziade.org/afpy/

And the Atomisator configuration for this is :
atomisator

sources =

    rss http://tarekziade.wordpress.com/feed/atom/



database = sqlite:///carpet.db



outputs =

    rss  public/rss.xml "http://tarekziade.wordpress.com/feed/atom/" "Carpet Python with PR" "Powered by Atomisator"



enhancers =

    postrank

How PostRank works

PostRank works with urls you provide, on their web interface or through their web services.

As long as these url are present in their big cloud-computing based system, they provide a rank that is calculated with the number of comments related to the blog, the number of tweet messages that refers to it, and so on. The complete algorithm they used is secret but this is not the point. I have secret algorithms too ;).

The point is that they are trying to categorize blog entries using social networks as indicators, and that they have a huge database.

Social indicators in Atomisator

This is one of the approach I have with Atomisator, when it is used to build a planet. For instance I have a Digg plugin that will inject in each entry the comments found on Digg if the entry was digged. It also present the number of Digg. Of course this is done live because I don't have a cloud-computing based system where I store data. I use Digg webservice on the fly. (On the fly here doesn't mean Atomisator make the calls to Digg from the Planet application of course. It means Atomisator calls them when it creates the merged feed on the system)

The benefit of this approach is that I can provide a social indicator on a post immediatly. Systems like PostRank will not work on entries that are too recent because their spiders have a lag of one week or so.

The pitfall of my approach is that I am unable to calculate trends because I don't store the indicators as they vary.

But if someone wanted to build a BtoC application using Atomisator, they could implement a set of plugins based on Amazon tools to make them store data in a more scalable way and in time.

Next steps

So I have this new PostRank plugin, and this is awesome because I have added a treshold parameter in it. Basically if a post has a high PostRank value, it will appear in the Planet. If it's low, it can be automatically removed. The fact that PostRanks are lagging for new entries is not a problem: interesting posts will eventually pop after a few days in the Planet.

This is perfect to reduce the number of entries in an aggregator.

But I do want to write my own PostRank that works live, with no storage at all. Because the whole point of Atomisator is to provide a framework where anyone can try out various filtering combinations.

So to be able to provide this power, it needs to work just by collecting data directly from the social services, like the PostRank plugin does with this PostRank "meta-service". The next step is therefore to see if I can query services like Twitter to list the twits related to an url, without having to store the twitter feed myself.

In any case, if my talk on Atomisator at Pycon 2009 is selected, the PostRank plugin will be shown besides the Digg plugin.