Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones

TitleTweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones
Publication TypeConference Proceedings
Year of Publication2016
AuthorsLu Zhou, Wenbo Wang, Keke Chen
Conference Name25th International World Wide Web Conference (WWW 2016)
Pagination603-612
Date Published04/2016
PublisherACM
Conference LocationMontreal, Canada
ISBN978-1-4503-4143-1
Abstract

Inappropriate tweets can cause severe damages on authors’ reputation or privacy. However, many users do not realize the negative consequences until they publish these tweets. Published tweets have lasting effects that may not be eliminated by simple deletion because other users may have read them or third-party tweet analysis platforms have cached them. Regrettable tweets, i.e., tweets with identifiable regrettable contents, cause the most damage on their authors because other users can easily notice them. In this paper, we study how to identify the regrettable tweets published by normal individual users via the contents and users’ historical deletion patterns. We identify normal individual users based on their publishing, deleting, followers and friends statistics. We manually examine a set of randomly sampled deleted tweets from these users to identify regrettable tweets and understand the corresponding regrettable reasons. By applying content-based features and personalized history-based features, we develop

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Citation:
Zhou, L., Wang, W., & Chen, K. Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones. Paper presented at 25th International World Wide Web Conference (WWW 2016), Montreal, Canada (pp. 603-612). New York, NY: ACM.

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