Publication Date
2014
Document Type
Thesis
Committee Members
Keke Chen, Ph.D. (Advisor); Guozhu Dong, Ph.D. (Committee Member); T.K. Prasad, Ph.D. (Committee Member)
Degree Name
Master of Science in Computer Engineering (MSCE)
Abstract
Inappropriate tweets may cause severe damages on the authors' reputation or privacy. However, many users do not realize until publishing them after a while. Once published, such tweets have lasting effects that may not be completely eliminated by simple deletion, because other users may have read them or third-party tweet analysis platforms have cached them. In this paper, we study the problem of identifying regret tweets for normal individual users, with the ultimate goal to reduce the occurrences of regret tweets. We develop a machine learning approach to extract a large collection of regret tweets from noisy deleted tweets. We show that with only the content-based analysis we can effectively find a significant portion of regret tweets with identifiable regret reasons. Furthermore, with the proposed approach we can also effectively distinguish regret tweets from normal kept tweets.
Page Count
41
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2014
Copyright
Copyright 2014, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
