I Wish I Didn't Say That! Analyzing and Predicting Deleted Messages in Twitter
Sasa Petrovic, Miles Osborne, Victor Lavrenko

TL;DR
This paper investigates the phenomenon of tweet deletions on Twitter, proposing methods to predict which tweets are likely to be deleted and analyzing the underlying reasons for such deletions.
Contribution
It introduces a novel approach to automatically predict tweet deletions and provides insights into the factors influencing deletion behavior.
Findings
Deletions can be predicted with significant accuracy
Certain tweet features correlate strongly with deletion likelihood
Analysis reveals common reasons behind tweet deletions
Abstract
Twitter has become a major source of data for social media researchers. One important aspect of Twitter not previously considered are {\em deletions} -- removal of tweets from the stream. Deletions can be due to a multitude of reasons such as privacy concerns, rashness or attempts to undo public statements. We show how deletions can be automatically predicted ahead of time and analyse which tweets are likely to be deleted and how.
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Taxonomy
TopicsSpam and Phishing Detection · Authorship Attribution and Profiling · Sentiment Analysis and Opinion Mining
