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

Lu Zhou, Wenbo Wang, Keke Chen
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引用次数: 52

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 \emph{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 classifiers that can effectively predict regrettable tweets.
正确推文:分析已删除的推文,以理解和识别令人遗憾的推文
不恰当的推文会对作者的名誉或隐私造成严重损害。然而,许多用户直到发布了这些推文才意识到负面后果。发布的推文具有持久的影响,可能不会通过简单的删除来消除,因为其他用户可能已经阅读了它们,或者第三方推文分析平台已经缓存了它们。遗憾的推文,即具有可识别的遗憾内容的推文,对其作者造成的损害最大,因为其他用户很容易注意到它们。本文研究了如何通过内容和用户的历史删除模式来识别\emph{正常个人用户}发布的遗憾推文。我们根据他们的发布、删除、关注者和好友统计数据来识别正常的个人用户。我们手动检查一组随机抽样的从这些用户删除的推文,以识别遗憾的推文,并了解相应的遗憾原因。通过应用基于内容的特征和个性化的基于历史的特征,我们开发了可以有效预测遗憾推文的分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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