Crowdsourcing represents a powerful approach for organizations to engage in distant search and mobilize knowledge distributed amongst a diverse network of people. While organizations generally succeed in generating large amounts of knowledge, they frequently fail to identify useful ideas that have the potential to solve problems or serve as innovation. We combine text mining and network analysis to examine how such contributions emerge on crowdsourcing platforms and how organizations may identify them. We find that useful ideas typically originate from members in a crowd with only few network ties and that these contributions become especially useful when they are enriched with local knowledge provided by experienced members on the platform. We extend existing research by examining the effects of network relationships and knowledge (re)combination in crowdsourcing. We also discuss the potential of network analysis and text mining to support organizations in tracking the origin of contributions and analyzing their content.