The rise of Online Social Networks in the last decade has changed society by irreversibly mingling the bounds between tangible reality and the online and, consequently, has shifted the paradigms of popularity relationships between famous entities and the public. It has been observed that Online Social Network (OSN) content popularity can be forecasted thanks to prediction algorithms applied to early metrics and that, more in general, predicting metrics can be both endogenous and exogenous to the OSN. This thesis introduces a novel approach to popularity forecasting based on historical information as well as attributes in control of the content creator instead of early popularity metrics and content quality attributes.