Social media analytics and research testbed (SMART): Exploring spatiotemporal           patterns of human dynamics with geo-targeted social media messages

Yang, Jiue-An; Tsou, Ming‐Hsiang; Jung, Chin‐Te; Allen, Christopher; Spitzberg, Brian H.; Gawron, Jean Mark; Han, Sang-Seop

doi:10.1177/2053951716652914

Cited by 27 publications

(10 citation statements)

References 54 publications

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“…Paul and Dredze [ 16 , 17 ] developed a topic model for Twitter to discover associated symptoms, treatments, and general words with diseases. Althouse et al [ 18 ] investigated population health concerns during the United States' Great Recession and Yang et al [ 19 ] used Twitter to track the diffusion of information with regard to disease outbreaks in and across different cities and geographic regions. Furthermore, mental health has been investigated in Twitter and screening instruments for population mood or emotions have been usefully implemented in several studies [ 20 – 36 ].…”

Section: Introductionmentioning

confidence: 99%

A novel surveillance approach for disaster mental health

et al. 2017

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BackgroundDisasters have substantial consequences for population mental health. Social media data present an opportunity for mental health surveillance after disasters to help identify areas of mental health needs. We aimed to 1) identify specific basic emotions from Twitter for the greater New York City area during Hurricane Sandy, which made landfall on October 29, 2012, and to 2) detect and map spatial temporal clusters representing excess risk of these emotions.MethodsWe applied an advanced sentiment analysis on 344,957 Twitter tweets in the study area over eleven days, from October 22 to November 1, 2012, to extract basic emotions, a space-time scan statistic (SaTScan) and a geographic information system (QGIS) to detect and map excess risk of these emotions.ResultsSadness and disgust were among the most prominent emotions identified. Furthermore, we noted 24 spatial clusters of excess risk of basic emotions over time: Four for anger, one for confusion, three for disgust, five for fear, five for sadness, and six for surprise. Of these, anger, confusion, disgust and fear clusters appeared pre disaster, a cluster of surprise was found peri disaster, and a cluster of sadness emerged post disaster.ConclusionsWe proposed a novel syndromic surveillance approach for mental health based on social media data that may support conventional approaches by providing useful additional information in the context of disaster. We showed that excess risk of multiple basic emotions could be mapped in space and time as a step towards anticipating acute stress in the population and identifying community mental health need rapidly and efficiently in the aftermath of disaster. More studies are needed to better control for bias, identify associations with reliable and valid instruments measuring mental health, and to explore computational methods for continued model-fitting, causal relationships, and ongoing evaluation. Our study may be a starting point also for more fully elaborated models that can either prospectively detect mental health risk using real-time social media data or detect excess risk of emotional reactions in areas that lack efficient infrastructure during and after disasters. As such, social media data may be used for mental health surveillance after large scale disasters to help identify areas of mental health needs and to guide us in our knowledge where we may most effectively intervene to reduce the mental health consequences of disasters.

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Section: Introductionmentioning

confidence: 99%

A novel surveillance approach for disaster mental health

et al. 2017

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“…The possibility of forecasting crisis events based on social media data is not considered in current systems. Even though there are several existing tools that may assist EMAs in crisis management by analyzing social media data [32,33], currently, there is no holistic tool focusing on the early detection of disasters. Rather different domain-specific approaches do exist [9].…”

Section: Problem Identificationmentioning

confidence: 99%

The Usage of Twitter Data for Early Crisis Detection

Mirbabaie¹,

Stieglitz²,

Lambertz³

2020

WI2020 Community Tracks

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The establishment of novel information and communications technologies, such as social media, has changed how individuals and organizations behave. Particularly during uncertain situations caused by natural or man-made disasters, people communicate and share information via social media. Emergency services could use published content of social media participants for an early detection of crisis situations and thus enhance crisis management. However, exploiting these information bears several challenges. To gain insights from large amounts of unstructured data in time-critical situations, emergency services are in the need of tools that enable them to gather, process, and analyze relevant information. In this research article, we follow the design science paradigm and propose a concept (artifact) of an early crisis detection tool. Based on expert interviews, design implications for the concept tool are derived. In addition, we conducted a design thinking workshop to evaluate the artifact, highlighting its practical relevance and usability.

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“…However, using the Twitter API requires technical insights that many emergency management agencies are unlikely to have. And while a number of scholars have developed dashboards or other systems for collecting and storing large amounts of this data for social research (Stefanidis et al, 2013;Felt, 2016;Yang et al, 2016;Poorthuis and Zook, 2017), the Twitter Terms of Service prevents these data from being shared. These limitations create a divide between data producers and those who want to access and analyze data.…”

Section: Data Intensive Infrastructure For Rapid Dissemination Of Infmentioning

confidence: 99%

Collaborative Science to Enhance Coastal Resilience and Adaptation

Nichols¹,

Wright

Bainbridge

et al. 2019

Front. Mar. Sci.

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Impacts from natural and anthropogenic coastal hazards are substantial and increasing significantly with climate change. Coasts and coastal communities are increasingly at risk. In addition to short-term events, long-term changes, including rising sea levels, increasing storm intensity, and consequent severe compound flooding events are degrading coastal ecosystems and threatening coastal dwellers. Consequently, people living near the coast require environmental intelligence in the form of reliable shortterm and long-term predictions in order to anticipate, prepare for, adapt to, resist, and recover from hazards. Risk-informed decision making is crucial, but for the resulting information to be actionable, it must be effectively and promptly communicated to planners, decision makers and emergency managers in readily understood terms and formats. The information, critical to forecasts of extreme weather and flooding, as well as long-term projections of future risks, must involve synergistic interplay between observations and models. In addition to serving data for assimilation into models, the observations are also essential for objective validation of models via hind casts. Linked observing and modeling programs that involve stakeholder input and integrate engineering, environmental, and community vulnerability are needed to evaluate conditions prior to and following severe storm events, to update baselines, and to plan for future changes over the long term. In contrast to most deep-sea phenomena, coastal vulnerabilities are locally and regionally specific and prioritization of the most important observational data and model predictions must rely heavily on input from local and regional communities and decision makers. Innovative technologies and nature-based solutions are already helping to reduce vulnerability from coastal hazards in some localities but more focus on local circumstances, as opposed to global solutions, is needed. Agile and spatially distributed response capabilities will assist operational organizations in predicting, preparing for and mitigating potential community-wide disasters. This white paper outlines the rationale, synthesizes recent literature and summarizes some data-driven approaches to coastal resilience.

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Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages

Cited by 27 publications

References 54 publications

A novel surveillance approach for disaster mental health

A novel surveillance approach for disaster mental health

The Usage of Twitter Data for Early Crisis Detection

Collaborative Science to Enhance Coastal Resilience and Adaptation

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