Social Media-Informed Urban Crisis Detection

Samuels, Rachel; Mohammadi, Neda; Taylor, John E; Georgia Institute of Technology. School of Civil and Environmental Engineering

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Social Media-Informed Urban Crisis Detection

2020-07-01
By Samuels, Rachel ; Mohammadi, Neda ; Taylor, John E

[PDF-3.62 MB]

English

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Details:

Creators:

Samuels, Rachel ; Mohammadi, Neda ; Taylor, John E

Samuels, Rachel ; Mohammadi, Neda ; Taylor, John E Less -
Corporate Creators:

Georgia Institute of Technology. School of Civil and Environmental Engineering
Corporate Contributors:

Georgia. Department of Transportation. Office of Performance-Based Management & Research ; United States. Department of Transportation. Federal Highway Administration

Georgia. Department of Transportation. Office of Performance-Based Management & Research ; United States. Department of Transportation. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Case Studies Crisis Detection Crisis Management Crisis Response Crowdsourcing Detection And Identification Technologies Disasters And Emergency Operations Information Management Social Media
Publication/ Report Number:

FHWA-GA-20-1834
Resource Type:

Tech Report
Geographical Coverage:

Georgia ; United States

Georgia ; United States Less -
Edition:

Final Report(May 2019 – July 2020)
Corporate Publisher:

Georgia. Department of Transportation. Office of Performance-Based Management & Research
Abstract:

This research project examines the potential utility of a social media-informed crisis detection system to aid GDOT in the identification of hazards across the state. To assess that utility, the research team first analyzed two case studies with the goal of identifying the relevant information contained within posted social media data. Case study data were drawn from hundreds of thousands of social media postings in Georgia, and include: 1) the winter storm that impacted north Georgia January 16-17, 2018 (N = 436 after processing) and 2)the flooding that occurred across the state of Georgia from Tropical Storm Irma from September 10-17, 2017 (N = 910 after processing).Nearest Neighbor Ratio analysis identified that postings were geographically clustered in areas of higher populations, but not statistically significantly clustered in terms of either sentiment or primary topic. The research team then analyzed sentiment as a method of ranking the social media data relevance and determined that neutral sentiment can function as a secondary filtration method for relevance. Finally, the team specified an approach to integrating this data visually that aligns with GDOT processes and with existing Waze data streams currently utilized by GDOT. The findings from this research and, in particular, the specification of a social media-informed crisis detection system, provide GDOT and other state agencies with a deeper understanding of the role social media can serve in crisis detection, tracking, and visualization, as well as providing a set of specifications to design and implement such a system.
Format:

PDF
Funding:

Task Order No. 2014-86
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:eb60165952e721d80c87c9ccb364e47473d0fdc130b10ba080bcd4b222b9421b
Download URL:

https://rosap.ntl.bts.gov/view/dot/53905/dot_53905_DS1.pdf
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Social Media-Informed Urban Crisis Detection

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