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Identifying Interesting Posts on Social Media Sites

Seethakkagari, Swathi, M.S.

Abstract Details

2012, MS, University of Cincinnati, Engineering and Applied Science: Computer Engineering.
This thesis work considers the classification of messages posted on social networking sites as a step towards identifying interesting/uninteresting messages. As first approximation, a message is represented by a few attributes including the message length (number of words), posting frequency (time difference between consecutive messages) for the same sender, comments and likes (received for the previous posts). Keywords in the posts can also be considered as a parameter. A classifier, trained according to the user's perception of whether a message is interesting or not, is used to label each message. In this thesis we consider the two different classifiers, k- Nearest Neighbor Classifier and Naive Bayes Classifier. Facebook is considered for illustration purposes.
Anca Ralescu, PhD (Committee Chair)
Fred Annexstein, PhD (Committee Member)
Kenneth Berman, PhD (Committee Member)
41 p.

Recommended Citations

Citations

  • Seethakkagari, S. (2012). Identifying Interesting Posts on Social Media Sites [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337888634

    APA Style (7th edition)

  • Seethakkagari, Swathi. Identifying Interesting Posts on Social Media Sites. 2012. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337888634.

    MLA Style (8th edition)

  • Seethakkagari, Swathi. "Identifying Interesting Posts on Social Media Sites." Master's thesis, University of Cincinnati, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337888634

    Chicago Manual of Style (17th edition)