An agent-based model is developed to understand the behaviors and rule-sets that generate social media networks. Simple rules are used to generate a network that resembles a backcloth (friend/follow) network collected at an earlier date us-ing the Twitter API. Model parameter adjustments were made to reproduce the collected network’s summary statistics and stylized specifics such as average de-gree, clustering, community size and distribution, as well as general structural met-rics. An approximate network was produced in line with the general properties of our collected data. In this paper, we focus on and discuss the properties of the de-gree distribution of our generated network and compare using recently developed methods for analyzing power law and heavy tailed distributions. It is proposed that a power law distribution forms a more appropriate fit for both our collected and generated networks then an exponential distribution, that a power law distri-bution is a more appropriate fit for smaller, more clustered networks than larger networks with multiple active local social forces. Finally, that seldom used models of fit such as a lognormal or a truncated power law distribution outperform both the exponential and power law distributions for either network.