  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 784K 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.
