Drake or Hen? Machine Learning for Gender Identification on Twitter

Abstract

Social media offers an invaluable wealth of data to understand what is taking place in our society. However, the use of social media data to understand phenomena occurring in populations is difficult because the data we obtain is not representative and the tools which we use to analyze this data introduce hidden biases on characteristics such as gender or age. For instance, in France in 2021 women represent 51.6% of the population [1] whereas on Twitter they represent only 33.5% of the french users [2]. With such a difference between social networks user demographics and real population, detecting the gender or the age before going into a deeper analysis becomes a priority. In this paper we provide the results of an ongoing work on a comparative study between three different methods to estimate gender. Based on the results of the comparative study, we evaluate future work avenues.