Social media trends are increasingly taking a significant role for the understanding of modern social dynamics. Platforms like Twitter enable democratic interaction within users, thus, providing an opportunity to inject influential ideas within formed communities. In this work, we take a look at how the Twitter landscape gets constantly shaped by automatically generated content.Twitter bot activity can bet raced via network abstractions which, we hypothesize, can be learned through state-of-the-art graph neural network techniques. We employ a large bot database,continuously updated by Twitter, to learn how likely is that a user is mentioned by a bot, as well as, for a hashtag. Furthermore, we model this likelihood as a link prediction task between the set of users and hashtags. Moreover, we contrast our results by performing similar experiments on a crawled data set of real users.