Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza

Allen, Christopher; Tsou, Ming‐Hsiang; Aslam, Anoshé A; Nagel, Anna; Gawron, Jean Mark

doi:10.1371/journal.pone.0157734

Cited by 113 publications

(99 citation statements)

References 13 publications

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“…AI is likely to become useful in public health and disease surveillance. ML applied to twitter found that Tweets could be a useful supplementary influenza surveillance tool and correlate well with official statistics . ML models have also been developed to classify suicide‐related communication on twitter .…”

Section: Population and Social Media Analysismentioning

confidence: 98%

Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

113

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Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

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Section: Population and Social Media Analysismentioning

confidence: 98%

Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

113

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“…These studies have explored the progression of natural disasters such as wildfires [29] and earthquakes [30]. The spatiotemporal analysis of Twitter content has also been used to track disease outbreaks and distribution [31,32].…”

Section: Related Workmentioning

confidence: 99%

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

Zhou

Chen

2017

IJGI

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Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation) and a social event (Pope Francis' visit to the US) in the New York City-Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information), this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.

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“…They were also able to demonstrate the coastal effects, where being located on the east or west coast of the US predicts more geotag users. Allen et al (2016) in their work on using Twitter data for surveillance of influenza used census data to normalize tweet count for individual cities. Li et al (2013) used socio-economic and demographic data from American Community Survey (ACS) to compare Twitter and Flickr usage patterns across contiguous United States.…”

Section: Bias In Social Media Datamentioning

confidence: 99%

Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

Ajayakumar

Shook

Turner

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the "North American storm complex" in

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Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza

Cited by 113 publications

References 13 publications

Artificial intelligence and machine learning in emergency medicine

Artificial intelligence and machine learning in emergency medicine

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

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