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1. Analyzing the Emotions of Crowd for Improving the Emergency Response Services

   https://doi.org/https://doi.org/10.1016/j.pmcj.2019.04.009  

   Neha Singh, Nirmalya Roy (August 2019, Pervasive and mobile computing)

   Twitter is an extremely popular micro-blogging social platform with millions of users, generating thousands of tweets per second. The huge amount of Twitter data inspire the researchers to explore the trending topics, event detection and event tracking which help to postulate the fine-grained details and situation awareness. Obtaining situational awareness of any event is crucial in various application domains such as natural calamities, man-made disaster and emergency responses. In this paper, we advocate that data analytics on Twitter feeds can help improve the planning and rescue operations and services as provided by the emergency personnel in the event of unusual circumstances. We take an emotional change detection approach and focus on the users’ emotions, concerns and feelings expressed in tweets during the emergency situations, and analyze those feelings and perceptions in the community involved during the events to provide appropriate feedback to emergency responders and local authorities. We employ improved emotion analysis and change point detection techniques to process, discover and infer the spatiotemporal sentiments of the users. We analyze the tweets from recent Las Vegas shooting (Oct. 2017) and note that the changes in the polarity of the sentiments and articulation of the emotional expressions, if captured successfully can be employed as an informative tool for providing feedback to EMS. 

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2. Building a Real-Time Geo-Targeted Event Observation (Geo) Viewer for Disaster Management and Situation Awareness

   https://doi.org/10.1007/978-3-319-57336-6_7  

   Tsou, Ming-Hsiang; Jung, Chin-Te Jung; Allen, Christopher; Yang, Jiue-An; Han, Su Yeon; Sptizberg, Brian H.; Dozier, Jessica (May 2017, Progress in Location Based Services 2018, Lecture Notes in Geoinformation and Cartography)

   Situation awareness plays an important role in disaster response and emergency management. Displaying real-time location-based social media messages along with videos, pictures, and hashtags during a disaster event could help first responders improve their situation awareness. A geo-targeted event observation (Geo) Viewer was developed for monitoring real-time social media messages in target areas with four major functions: (1) real-time display of geo-tagged tweets within the target area; (2) interactive mapping functions; (3) spatial, text, and temporal search functions using keywords, spatial boundaries, or dates; and (4) manual labeling and text-tagging of messages. Different from traditional web GIS maps, the user interface design of GeoViewer provides the interactive display of multimedia content and maps. The front-end user interface to visualize and query tweets is built with open source programming libraries using server-side MongoDB. GeoViewer is built for assisting emergency responses and disaster management tasks by tracking disaster event impacts, recovery activities, and residents’ needs in the target region. 

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3. Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

   https://doi.org/10.2196/24432  

   Li, Zhenlong; Li, Xiaoming; Porter, Dwayne; Zhang, Jiajia; Jiang, Yuqin; Olatosi, Bankole; Weissman, Sharon (January 2020, JMIR Research Protocols)

   null (Ed.)

   Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432 

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4. Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and COVID-19

   https://doi.org/10.1177/03611981231168120  

   Momin, Khondhaker Al; Kays, H M; Sadri, Arif Mohaimin (May 2023, Transportation Research Record: Journal of the Transportation Research Board)

   Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during Hurricane Laura compounded by the COVID-19 pandemic. Hurricane Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana, U.S. Using an application programming interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Hurricane Laura. Online social networks were based on Twitter’s user influence feature (i.e., mentions or tags) that allows notification of other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into 21 components of various size, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those which heavily involved and closely interacted with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters. 

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5. A comparison of people’s use of Twitter in Puerto Rico Earthquake and Hurricane Maria

   Thackaberry, Taylor; Bogemann, Kayley; Burchard, Shane; Butler, Jessie; Spencer, Austin; Li, Liuqing; Wernstedt, Kris; Murray-Tuite, Pamela; Fox, Edward A. (May 2020, International Conference on Information Systems for Crisis Response and Management)

   The purpose of the Twitter Disaster Behavior project is to identify patterns in online behavior during natural disasters by analyzing Twitter data. The main goal is to better understand the needs of a community during and after a disaster, to aid in recovery. The datasets analyzed were collections of tweets about Hurricane Maria, and recent earthquake events, in Puerto Rico. All tweets pertaining to Hurricane Maria are from the timeframe of September 15 through October 14, 2017. Similarly, tweets pertaining to the Puerto Rico earthquake from January 7 through February 6, 2020 were collected. These tweets were then analyzed for their content, number of retweets, and the geotag associated with the author of the tweet. We counted the occurrence of key words in topics relating to preparation, response, impact, and recovery. This data was then graphed using Python and Matplotlib. Additionally, using a Twitter crawler, we extracted a large dataset of tweets by users that used geotags. These geotags are used to examine location changes among the users before, during, and after each natural disaster. Finally, after performing these analyses, we developed easy to understand visuals and compiled these figures into a poster. Using these figures and graphs, we compared the two datasets in order to identify any significant differences in behavior and response. The main differences we noticed stemmed from two key reasons: hurricanes can be predicted whereas earthquakes cannot, and hurricanes are usually an isolated event whereas earthquakes are followed by aftershocks. Thus, the Hurricane Maria dataset experienced the highest amount of tweet activity at the beginning of the event and the Puerto Rico earthquake dataset experienced peaks in tweet activity throughout the entire period, usually corresponding to aftershock occurrences. We studied these differences, as well as other important trends we identified. 

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