Gender identification in social media using transfer learning

Abstract

Social networks have modified the way we communicate. It is now possible to talk to a large number of people we have never met. Knowing the traits of a person from what he/she writes has become a new area of computational linguistics called Author Profiling. In this paper, we introduce a method for applying transfer learning to address the gender identification problem, which is a subtask of Author Profiling. Systems that use transfer learning are trained in a large number of tasks and then tested in their ability to learn new tasks. An example is to classify a new image into different possible classes, giving an example of each class. This differs from the traditional approach of standard machine learning techniques, which are trained in a single task and are evaluated in new examples of that task. The aim is to train a gender identification model on Twitter users using only their text samples in Spanish. The difference with other related works consists in the evaluation of different preprocessing techniques so that the transfer learning-based fine-tuning is more efficient.