Understanding resilience attributes for children, youth, and communities in the wake of the Deepwater Horizon oil spill study, social media component

Twitter data was acquired from a service provider to investigate the role of social media during and after the Deepwater Horizon Oil Spill. In particular, historical Twitter data was accessed using a geospatial query tool. The query rules were built with a set of keywords and filtered by date. Using a combination of human coding and machine-learning processes, the twitter datasets were examined to get insights into online communications related to the oil spill. This dataset contains the description about the data acquisition process, the search strategy and keywords, and the dates of the historical Twitter datasets related to the Deepwater Horizon oil spill, along with a detailed summary of the methodology being used for data analysis.本数据集通过服务提供商获取推特（Twitter）数据，旨在探究深水地平线漏油事件（Deepwater Horizon Oil Spill）发生期间及事后社交媒体所扮演的角色。具体而言，研究人员借助空间查询工具获取历史推特数据，查询规则由一组关键词构建，并按日期进行筛选。本研究结合人工编码与机器学习流程，对推特数据集展开分析，以挖掘与此次漏油事件相关的线上传播洞察。本数据集涵盖了相关历史推特数据的获取流程、搜索策略与关键词、对应时间节点，以及用于数据分析的详细方法论摘要。