I just added a Knol (new google “wiki-like” project) on information overload. Thought this might be a nice first post for this blog.
Summary:
The amount of information we are now presented on the web is overwhelming. There is more information that we can process. This phenomenon is called “information overload”. Most of the effort on the web to deal with this problem falls under the term “information filtering”
More than a decade into the birth of internet directories and search, the hyperlinked web has provided us with a near instant access to all the information we might need, and more. With the recent exponential growth of user generated content –both general as well as personal information – build on social networks, blogs and micro blogging platforms, the amount of information we are now presented on the web is overwhelming. There is more information that we can process.
The effects of information overload are tremendous. It lowers one’s ability to focus and greatly deteriorates productivity. Time consuming information gathering has been replaced with time consuming information processing.
The response has been an effort to provide information filtering mechanisms to better digest all the information that we are exposed to on a day to day basis.
There are both algorithms as well as social filters to deal with information overload.
One possible non exhaustive classification for social filtering could be:
1. Content ranked by users to assess popularity.
2. Views or comments to assess popularity.
3. Both systems on top of a social graph allows filtering by users you trust or users with similar tastes.
I know very little to nothing about algorithm filtering (looking for more collaborators on this) but it seems to be that most of them rely on establishing correlations on your information consumption patterns. Another widely and current popular use is to leverage the social graph to introduce you to users you might want to “add as friends” and follow.
