Prediction of Twitter Message Deletion

Abstract

Social media are a way for people to build their reputation or to promote an idea. Twitter, in contrast with other social media sources, is a generator of real-time textual information, and it is mainly used to share ideas, opinions and breaking news. It is meant for short, quick, compelling statements that reach out millions of users around the world. Posting something inappropriate may affect the public image, privacy of celebrities, politicians as well as ordinary Twitter users. If we could in advance alarm the user of the potential vulnerability in the message to be posted we could protect his/her identity from being compromised. So, automatic identification of the message with the content causing it to be deleted in the future is a promising area of research. In this paper, we are analyzing Twitter messages in English language with the objective to build a classifier to predict whether a particular post will be deleted by the user or not. We apply the Recurrent Neural Networks (RNN) model that relies on the context-based information of tweets while doing the classification. An additional contribution of the work is the construction of a rich set of features including twitter metadata, user information and tweets’ text to train classical machine learning algorithms on Twitter data.