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

翻译：灾害事件中的社交媒体分析是危机信息学研究中的关键任务，涉及对自然灾害、危机事件或其他大规模聚集事件期间产生的社交媒体数据进行解析。由于此类事件生成的数据集规模庞大，需要设计大规模的软件基础设施以实时分析数据。构建此类基础设施带来了维护需求，且随着基础设施规模扩大与使用时间增长，维护难度日益增加。维护成本高昂，因为查询需快速响应，这要求大量计算资源能够全天候按需供应。本论文提出一种替代方案，设计用于在云平台上分析非结构化数据的软件基础设施，同时提供快速查询能力，并满足危机信息学研究所需的可靠性。此外，本文探讨了一种基于容器编排系统的、更可靠的Twitter流数据采集新方法。最后，我将这一新型基础设施与现有危机信息学软件架构进行比较，从可靠性、可扩展性和可拓展性三个维度，对比现有方案与我所提出的方法及原型系统。