Although according to surveys related to internet user ac- tivity it is considered one of the most popular aspects, few studies are actually concerned with internet pornography. This paper is aimed at rectifying that overlook. In particu- lar, we study user activity related to internet pornography by looking at two main behaviors: (i) watching pornogra- phy, and (ii) providing feedback on pornography items in the form of ratings and comments. By using appropriate datasets that we collect, we make contributions related to the study of both behaviors pointed out above. With regards to viewing, we observe that views are highly dependent on pornography category and video size. By studying the feedback system of pornography video websites, we observe differences in the way users rate items across websites popular in different parts of the world. Fi- nally, we employ sentiment analysis to study the comments that users leave on pornography websites and we find sur- prising similarities across the analyzed websites. Our results pave the way to understanding more about human behav- ior related to internet pornography and can impact, among others, fields such as content personalization, video content delivery, recommender systems.