In the last decade, many scholars have worked on content moderation from different perspectives. Many have focused on the issues related to censorship and freedom of expression from a law perspective (focusing on the regulation framework i.e.) ; others have tried to work with digital methods either to work with great corpus of data, either to try to understand how algorithmic content moderation actually works on each platform; etc. This study aims at comparing how content moderation works on different social media. Working with data from the DSA Transparency Database, we aim at comparing how platforms like Facebook, Instagram, Twitter/X, TikTok (etc.) moderate their content online. Therefore, the study explores different questions: What type of content is more likely to be deleted? What is the proportional difference between content being deleted by humans and machines? How do the proportions of content being deleted differ across different social media platforms? How do the proportions of content deleted by humans and machines differ across social media platforms? In which category do we find the highest partial/temporary suspension incidence?