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Author Antone Evans Jr.; Yingyuan Yang; Sunshin Lee pdf  openurl
  Title Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses Type Conference Article
  Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021  
  Volume Issue Pages 792-807  
  Keywords Big Data Analysis, Machine Learning, COVID-19, Twitter  
  Abstract During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases.  
  Address University of Illinois Springfield; University of Illinois Springfield; University of Illinois Springfield  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-61-5 ISBN (up) Medium  
  Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management  
  Notes aevan7@uis.edu Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2374  
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Author Seungwon Yang; Haeyong Chung; Xiao Lin; Sunshin Lee; Liangzhe Chen; Andrew Wood; Andrea Kavanaugh; Steven D. Sheetz; Donald J. Shoemaker; Edward A. Fox pdf  isbn
openurl 
  Title PhaseVis1: What, when, where, and who in visualizing the four phases of emergency management through the lens of social media Type Conference Article
  Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013  
  Volume Issue Pages 912-917  
  Keywords Civil defense; Classification (of information); Data visualization; Information systems; Risk management; 10-fold cross-validation; Classification algorithm; Classification evaluation; Emergency management; Potential utility; ThemeRiver; Through the lens; Twitter; Disasters  
  Abstract The Four Phase Model of Emergency Management has been widely used in developing emergency/disaster response plans. However, the model has received criticism contrasting the clear phase distinctions in the model with the complex and overlapping nature of phases indicated by empirical evidence. To investigate how phases actually occur, we designed PhaseVis based on visualization principles, and applied it to Hurricane Isaac tweet data. We trained three classification algorithms using the four phases as categories. The 10-fold cross-validation showed that Multi-class SVM performed the best in Precision (0.8) and Naïve Bayes Multinomial performed the best in F-1 score (0.782). The tweet volume in each category was visualized as a ThemeRiver[TM], which shows the 'What' aspect. Other aspects – 'When', 'Where', and 'Who' – Are also integrated. The classification evaluation and a sample use case indicate that PhaseVis has potential utility in disasters, aiding those investigating a large disaster tweet dataset.  
  Address Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States; Department of Accounting and Information Systems, Virginia Tech, Blacksburg, VA 24061, United States; Department of Sociology, Virginia Tech, Blacksburg, VA 24061, United States  
  Corporate Author Thesis  
  Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN (up) 9783923704804 Medium  
  Track Social Media Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 1122  
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