Info 202 Fall 2011

Whenever I go near Sproul Hall, I grab a copy of The Daily
Californian. I like reading papers I mean “physical” papers when compared to
digital papers. However this post not about my likes or dislikes it is about
the way information is organized on the digital medium. An e-paper gives an
enormous flexibility to organize information. If you visit the website you will
find the “News, Sports, A&E, Opinion, Multimedia and Blogs” categories.
Well Sports news is a subset of News so why not place it in under News?  I believe it has been classified as separate
category due to the popularity of sports. Sports create has lot of following
and every aspect related to sports is news.

I came across the following metadata in Daily Californian, “Author,
Date, Time and Tags”. Well these metadata are also used for organizing news in
news category. When you visit the website you will find “Most Related Posts” I
was wondering how the related posts are determined. This reminds me how I used
to determine related blogs posts in an NGO’s blog site which I used to develop
and maintain. I used to determine analyze the tags associated with each blog post,
in a logical way more number of similar tags I used to categorized them as
related posts. This is very simple way of categorizing because the website was
moderated and the tags used were controlled. However my logic will render
different results if the vocabulary used for tagging is uncontrolled. Categorizing
posts with tags in an uncontrolled vocabulary requires complex text analytics
to make sense of tags. There are new kinds of metadata like Tweets, Facebook
Likes which are increasingly being used. Also the number of clicks on a post
can be another form of metadata. All these new metadata are used to “Most Popular”,
“Most Tweeted” or “Most Commented” using user generated information to organize
things.