This paper describes the participation of the Web Science and Social Computing Research Group from the Universitat Pompeu Fabra, Barcelona (UPF) at CLEF 2018 eRisk Lab1. Its main goal, di- vided in two different tasks, is to detect, with enough anticipation, cases of depression (T1) and anorexia (T2) given a labeled dataset with texts written by social media users. Identifying depressed and anorexic indi- viduals by using automatic early detection methods, can provide experts a tool to do further research regarding these conditions, and help people living with them. Our proposal presents several machine learning models that rely on features based on linguistic information, domain-specific vo- cabulary and psychological processes. The results, regarding the F-Score, place our best models among the top 5 approaches for both tasks.