Classifying tweets using user account information

Abstract

Twitter is a short-text message system developed 6 years ago. It now has more than 100 million users generating over 300 million tweets every day. Twitter accounts are used for diverse purposes, such as social, advertising, political, religious, benevolent or vicious ideologies, among other activities. These activities can be communicated by humans, a machine or a robot. The purpose of this paper is to build predictive models, such as Logistic Regression, K Nearest Neighbors and Neural Network in order to identify the best variables that help predict, based on the contents, whether the tweets are coming from a human or a machine with the least possible error.