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Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado

Utah Climate Center, Department of Plants, Soil & Climate, Utah State University, Logan, UT 84322, USA
Department of Landscape Architecture & Environmental Planning, Utah State University, Logan, UT 84322, USA
NOAA/National Integrated Drought Information System, and Cooperative Institute for Research in the Environmental Sciences, University of Colorado Boulder, Boulder, CO 80305, USA
Author to whom correspondence should be addressed.
Academic Editors: Zheng Duan and Babak Mohammadi
Water 2022, 14(18), 2773;
Received: 2 June 2022 / Revised: 23 August 2022 / Accepted: 24 August 2022 / Published: 6 September 2022
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S. View Full-Text
Keywords: drought; Twitter; machine learning; Colorado drought; Twitter; machine learning; Colorado
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MDPI and ACS Style

Mukherjee, S.; Wang, S.; Hirschfeld, D.; Lisonbee, J.; Gillies, R. Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado. Water 2022, 14, 2773.

AMA Style

Mukherjee S, Wang S, Hirschfeld D, Lisonbee J, Gillies R. Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado. Water. 2022; 14(18):2773.

Chicago/Turabian Style

Mukherjee, Sarbajit, Simon Wang, Daniella Hirschfeld, Joel Lisonbee, and Robert Gillies. 2022. "Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado" Water 14, no. 18: 2773.

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