Although the extraction of facts and aggregated information from individual Online Social Networks (OSNs) has been extensively studied in the last few years, cross-social media-content examination has received limited attention. Such content examination involving multiple OSNs gains significance as a way to either help us verify unconfirmed-thus-far evidence or expand our understanding about occurring events. Driven by the emerging requirement that future applications shall engage multiple sources, we present the architecture of a distributed crawler which harnesses information from multiple OSNs. We demonstrate that contemporary OSNs feature similar, if not identical, baseline structures. To this end, we propose an extensible model termed SocWeb that articulates the essential structural elements of OSNs in wide use today. To accurately capture features required for cross-social media analyses, SocWeb exploits intra-connections and forms an "amalgamated" OSN. We introduce a flexible API that enables applications to effectively communicate with designated OSN providers and discuss key design choices for our distributed crawler. Our approach helps attain diverse qualitative and quantitative performance criteria including freshness of facts, scalability, quality of fetched data and robustness. We report on a cross-social media analysis compiled using our extensible SocWeb-based crawler in the presence of Facebook and Youtube.