Over the years, myriad people and publications have attempted to list who the top Twitter feeds in the field of Astronomy might be. These lists have been haphazardly biased by personal preferences, unconscious bias, search engine choices, and fickle characteristics such as fame. In this presentation, we use network analysis techniques to identify twitter users who follow large numbers of astronomy feeds, and use their following habits to identify the primary voices being followed. For ease of analysis, we restrict our analysis to personal accounts of PhD astronomers who are active in science and science communications. Through this kind of a network analysis, we have identified clusters in the larger scale twitter structure, as well as biases, over-sensitivities, and twitter-usage behaviors that influence who is followed by what kind of audience on Twitter.