Modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. In this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the social-network and profile-based blogging-behavior model. The models are based on two regression techniques: Extreme Learning Machine (ELM), and Modified General Regression Neural Network (MGRNN). We choose one of the largest blogs, a political blog, DailyKos, for our empirical evaluation. Experiments show that the social network and profile-based blogging behavior model with ELM regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.