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Social media analysis of disaster events is a critical task in crisisinformatics research. It involves analyzing social media data generated duringnatural disasters, crisis events, or other mass convergence events. Due to thelarge data sets generated during these events, large scale softwareinfrastructures need to be designed to analyze the data in a timely manner.Creating such infrastructures bring the need to maintain them and this becomesmore difficult as these infrastructures grow larger and older. Maintenancecosts are high since there is a need for queries to be handled quickly whichrequire large amounts of computational resources to be available on demand 24hours a day, seven days a week. In this thesis, I describe an alternativeapproach to designing a software infrastructure for analyzing unstructured dataon the cloud while providing fast queries and with the reliability needed forcrisis informatics research. Additionally, I discuss a new approach for a morereliable Twitter stream collection using container orchestrated systems. Ifinally compare this new infrastructure with existing crisis informaticssoftware infrastructures and compare their reliability, scalability andextensibility with my approach and my prototype.

Social Media Analysis for Crisis Informatics in the Cloud