The advances in social media and social computing technologies have dramatically changed the way through which people interact, organize, and collaborate. The use of social media also makes the large-scale data revealing human behavior accessible to researchers and practitioners. The analysis and modeling of social networks formed from relatively stable online communities have been extensively studied. The research on the structural and dynamical patterns of large-scale crowds motivated by accomplishing common goals, named the cyber movement organizations (CMO) or cyber-enabled social movement organizations (CeSMO), however, is still limited to anecdotal case studies. This research is one of the first steps towards the understanding of the CMO/CeSMO based on real data collected from online social media.The focus of my research is on the study of an important type of CMO/CeSMO, the crowd-powered search behavior (also known as human flesh search, HFS), in which a large number of Web users voluntarily gathered together to find out the truth of an event or the information of a person that could not be identified by one single person or simple online searches. In this research, I have collected a comprehensive data-set of HFS. I first introduce the phenomenon of HFS and reviewed the study of online social groups/communities. Then, I present the empirical studies of both individual HFS episodes and aggregated HFS communities, and unveiled their unique topological properties. Based on the empirical findings, I propose two models to simulate evolution and topology of individual HFS networks. I conclude the dissertation with discussions of future research of CMO/CeSMO.