Publication Date

Summer 2011

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Soon Tee Teoh

Third Advisor

Snehal Patel

Keywords

Social Network Filtering

Abstract

Social networks are at an all time high, nowadays. They make the world a smaller place to live in. People can stay in touch with friends and can make new friends on these social networks which traditionally were not possible without internet service. The possibilities provided by social networks enable vast and immediate contact. People tend to spend lot of time on the social networks like Facebook, LinkedIn and Twitter peeping into their friend‟s accounts and trying to stay connected with the world.
However, recently people have started closing their accounts on these famous social networks after having been irritated with the large amount of data that floods these networks. Although there are many problems associated with these social networks like: privacy issues, identity fraud, information overload, etc.; the problem that bothers people the most is that of information overload.
This project provides a solution to the information overload problem by filtering all the user‟s friend‟s posts on the basis of user‟s likes without explicitly asking the user to specify their likes. The project analyzes the user's posts to find out their likes, and then returns the filtered posts to them from their friends on Facebook, Twitter and LinkedIn.
Thus, this project attempts to remove noise from the huge amount of data on these social networks.

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