Analyzing Social Media Data
Morgan Madeira
Junior, Computer Science
Social media has increasingly become an outlet for expression for a large part of our society. Literature suggests that analyzing data from these sites can lead to improvements in areas such as health-care and search-ad targeting. Users of these sites often associate with many other users described as “friends,” even if they do not have a strong connection, or what would be described as friendship in daily life. It is valuable to determine the strength of relationships between users and to identify communities within social networks. These communities represent people with similar characteristics, which are used by applications to solve many real-world problems. For instance, it is useful to identify groups that are interested in a specific movie genre. Information about these groups can be used to target movie advertisements towards the people most interested in that genre. These types of problems have similar characteristics to identifying close friends. We have created a system to collect and analyze the data about user characteristics, while being respectful of privacy concerns. The system is composed of a front end Facebook application and a back end machine-learning based tool. The front end component gathers data about a user and their friends. The back end uses the collected data and machine-learning techniques to determine relationships between users.
To learn more about the project, check out an interview with Morgan.
Catch Morgan's poster presentation at URCAD this Wednesday, April 25 in the University Center Ballroom from 10:00 a.m. to 12:30 p.m.