By including "credible data" extracted from the Twitter social networking service, the study of earthquakes and tsunamis is being systematically transformed into a big data analytics problem. The challenge of establishing geophysically credible tweets is considered through a combination of deep learning and semantics (that is, knowledge representation). More specifically, tweet classification via GPU-based platforms for deep learning is compared and contrasted with previous work based on the use of in-memory computing via Apache Spark. Although there remains cause for optimism in augmenting traditional scientific data with that derived from social networking, ongoing research is aimed at providing utility in practice. The motivation for success remains strong, as establishing a causal relationship between earthquakes and tsunamis remains problematical, and this in turn complicates any ability to deliver timely, accurate messaging that could prove life-critical. Finally, we'll consider the applicability of this approach to other disaster scenarios (for example, the Deepwater Horizon oil spill).