This paper presents a method to classify social media users based on their socioeconomic status. Our experiments are conducted on a curated set of Twitter profiles, where each user is represented by the posted text, topics of discussion, interactive behaviour and estimated impact on the microblogging platform. Initially, we formulate a 3-way classification task, where users are classified as having an upper, middle or lower socioeconomic status. A nonlinear, generative learning approach using a composite Gaussian Process kernel provides significantly better classification accuracy (75 %) than a competitive linear alternative. By turning this task into a binary classification - upper vs. medium and lower class - the proposed classifier reaches an accuracy of 82 %.
['Vasileios Lampos', 'Nikolaos Aletras', 'Jk Geyti', 'Bin Zou', 'Ingemar J. Cox']
Inferring the Socioeconomic Status of Social Media Users Based on Behaviour and Language