TY  - JOUR
PY  - 2021//
TI  - A multi-modal approach towards mining social media data during natural disasters - a case study of Hurricane Irma
JO  - International journal of disaster risk reduction
A1  - Mohanty, Somya D.
A1  - Biggers, Brown
A1  - Sayedahmed, Saed
A1  - Pourebrahim, Nastaran
A1  - Goldstein, Evan B.
A1  - Bunch, Rick
A1  - Chi, Guangqing
A1  - Sadri, Fereidoon
A1  - McCoy, Tom P.
A1  - Cosby, Arthur
SP  - e32
EP  - e32
VL  - 54
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2212-4209
UR  - http://dx.doi.org/10.1016/j.ijdrr.2020.102032
ID  - ref1
ER  -