Analyzing Regrettable Communications on Twitter: Characterizing Deleted Tweets and Their Authors

TL;DR

This study characterizes deleted tweets and their authors on Twitter, revealing personality traits linked to deletion and developing a classifier with 78% F1-score to predict tweet deletion based on content and user features.

Contribution

It provides a detailed analysis of the characteristics of deleted tweets and authors, and introduces a predictive model for tweet deletion.

Findings

Deleted tweets are less informational and conversational.

Users who delete tweets tend to be more extroverted and neurotic.

The classifier achieves an F1-score of 0.78 in predicting deletions.

Abstract

Over 500 million tweets are posted in Twitter each day, out of which about 11% tweets are deleted by the users posting them. This phenomenon of widespread deletion of tweets leads to a number of questions: what kind of content posted by users makes them want to delete them later? %Are all users equally active in deleting their tweets or Are users of certain predispositions more likely to post regrettable tweets, deleting them later? In this paper we provide a detailed characterization of tweets posted and then later deleted by their authors. We collected tweets from over 200 thousand Twitter users during a period of four weeks. Our characterization shows significant personality differences between users who delete their tweets and those who do not. We find that users who delete their tweets are more likely to be extroverted and neurotic while being less conscientious. Also, we find that deleted tweets while containing less information and being less conversational, contain significant indications of regrettable content. Since users of online communication do not have instant social cues (like listener's body language) to gauge the impact of their words, they are often delayed in employing repair strategies. Finally, we build a classifier which takes textual, contextual, as well as user features to predict if a tweet will be deleted or not. The classifier achieves a F1-score of 0.78 and the precision increases when we consider response features of the tweets.