In experimenting with news aggregation and mining on the d8taplex site, I've come up with the following questions:
- Why are some news articles picked up and others not? News sources such as Reuters create articles that are either directly consumed or which are picked up by other publications and passed along.
- Who are these people writing these articles? What are their interests, areas of expertise and personalities?
- What is the role of the editor and how do they influence the selection and form of the content produced by the news machine?
The next round of experimentation with news aggregation has resulted in the current new site. It has the following features.
Firstly, it presents two lists of articles. On the left, are those articles which are currently getting a reasonable amount of buzz (as determined by bit.ly clicks). The second column, on the right, presents those articles which are not yet receiving much attention. As you read the left you will probably realise that you are already familiar with these stories.
The amount of bit.ly juice is indicated by the number prefixed by 'B:' The article block consists of the title, the time stamp of the article, the link and the list of contributors.
Secondly, by providing the dynamic filtering one can very quickly get a view of which unloved articles are present relating to those stories that are already getting attention. For example, in the screenshot below we can see that one article about Gingrich is getting buzz while four others that mention him are languishing on the right.
Thirdly, it connects the user with the personality of the author and editor by providing, where possible, various facets of information such as job title, twitter account, email address, phone number, role, languages spoken, etc. Some of these are gleaned from the Reuters site while others are mined from various social network sites and so on.
Please take a look at the site and let me know what you think. There are many things that I'd like to do with it, such as surfacing social metrics for the authors, predicting the potential upside for an article based on the historical bit.ly scores of the authors, etc.
If you click through to an article from the site, you will be contributing to its bit.ly score!
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