Streaming social media provides a real-time glimpse of extreme weather impacts. However, the volume of streaming data makes mining information a challenge for emergency managers, policy makers, and disciplinary scientists. Here we explore the effectiveness of data learned approaches to mine and filter information from streaming social media data from Hurricane Irma's landfall in Florida, USA. We use 54,383 Twitter messages (out of 784 K geolocated messages) from 16,598 users from Sept. 10–12, 2017 to develop 4 independent models to filter data for relevance: 1) a geospatial model based on forcing conditions at the place and time of each tweet, 2) an image classification model for tweets that include images, 3) a user model to predict the reliability of the tweeter, and 4) a text model to determine if the text is related to Hurricane Irma. All four models are independently tested, and can be combined to quickly filter and visualize tweets based on user-defined thresholds for each submodel. We envision that this type of filtering and visualization routine can be useful as a base model for data capture from noisy sources such as Twitter. The data can then be subsequently used by policy makers, environmental managers, emergency managers, and domain scientists interested in finding tweets with specific attributes to use during different stages of the disaster (e.g., preparedness, response, and recovery), or for detailed research.

流媒体社交媒体提供了极端天气影响的实时概览。然而，大量的流数据使挖掘信息成为应急管理人员、政策制定者和学科科学家面临的挑战。在这里，我们探讨了从飓风艾尔玛在美国佛罗里达州登陆的流媒体社交媒体数据中挖掘和过滤信息的数据学习方法的有效性。我们使用 2017 年 9 月 10 日至 12 日期间来自 16,598 位用户的 54,383 条 Twitter 消息（共 784 K 条地理定位消息）开发了 4 个独立模型来过滤数据的相关性：1）基于时间和地点的强制条件的地理空间模型每条推文，2) 包含图像的推文的图像分类模型，3) 用于预测推文可靠性的用户模型，以及 4) 用于确定文本是否与飓风艾尔玛相关的文本模型。所有四个模型都经过独立测试，并且可以组合起来，根据用户为每个子模型定义的阈值快速过滤和可视化推文。我们设想这种类型的过滤和可视化例程可以用作从 Twitter 等噪声源捕获数据的基本模型。随后，政策制定者、环境管理者、应急管理者和有兴趣查找具有特定属性的推文以在灾难的不同阶段（例如，准备、响应和恢复）使用或用于详细研究的领域科学家可以使用这些数据。