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The increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during natural disasters, but high levels of noise (i.e., non-relevant content) present challenges in both understanding social experiences of a disaster and in facilitating disaster recovery. This study builds on current research by uniquely using social media data, collected in the field through qualitative interviews, to create a supervised machine learning model. Collected data represents rescuers and rescuees during the 2017 Hurricane Harvey. Preliminary findings indicate a 99% accuracy in classifying data between signal and noise for signal-to-noise ratios (SNR) of 1:1, 1:2, 1:4, and 1:8. We also find 99% accuracy in classification between respondent types (volunteer rescuer, official rescuer, and rescuee). We furthermore compare human and machine coded attributes, finding that Google Vision API is a more reliable source of detecting attributes for the training set.
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On the need for prospective disaster survey panels
Disasters are typically unforeseen, causing most social and behavioral studies about disasters to be reactive. Occasionally, predisaster data are available, for example, when disasters happen while a study is already in progress or where data collected for other purposes already exist, but planned pre-post designs are all but nonexistent. This gap fundamentally limits the quantification of disasters’ human toll. Anticipating, responding to, and managing public reactions require a means of tracking and understanding those reactions, collected using rigorous scientific methods. Oftentimes, self-reports from the public are the best or only source of information, such as perceived risk, behavioral intentions, and social learning. Significant advancement in disaster research, to best inform practice and policy, requires well-designed surveys with large probability-based samples and longitudinal assessment of individuals across the life-cycle of a disaster and across multiple disasters.
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- Award ID(s):
- 1760484
- PAR ID:
- 10126512
- Date Published:
- Journal Name:
- Disaster medicine and public health preparedness
- ISSN:
- 1938-744X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/dmp.2019.94
