This paper considers the extraction and analysis of Social Networks for the identification of trends. Our methodology focuses on the utilization of semantics for determination of relevant networks within unstructured data. The Social Networks are examined from the perspective of structure and considered as a time series. Our metrics focus on the identification of influence and power among key players. This method is applied against a collection of Twitter messages and compared to historical market share trends of technologically-related topics. Through this work we demonstrate that structural qualities reflecting community dynamics can provide insight to the prediction of long-term trends. The goal of this work is to lend insight to the characterization of consumer behavior, particularly in the area of technology forecasting.