The academic world increasingly faces challenges related to publication misconduct. Many ethically questionable practices are related to predatory jour- nals and predatory publishers, as these do not implementand enforce sound quality control measures. A list of these ”bad players”, known as Beall’s List [Bea], can help researchers to avoid publishing in outlets of questionable quality. The list is not free from criticism,though, and it is curated by humans. This list may also not be complete. This project aims to use statistics and machine learning techniques to automatically separate the good from the bad journals. Theobjective of this project are to devise and implement algorithms that will identify and separate ”good” and ”bad” academic journals.