Mining and Understanding Regret Tweets
Lu Zhou · OhioLink ETD Center (Ohio Library and Information Network) · 2014
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.