toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links
Author (up) Andrea Kavanaugh; Steven D. Sheetz; Riham Hassan; Seungwon Yang; Hicham G. Elmongui; Edward A. Fox; Mohamed Magdy; Donald J. Shoemaker pdf  isbn
openurl 
  Title Between a rock and a cell phone: Communication and information technology use during the 2011 Egyptian uprising Type Conference Article
  Year 2012 Publication ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2012  
  Volume Issue Pages  
  Keywords Cellular telephones; Information systems; Mobile phones; Contextual factors; Information technology use; Innovation diffusion; Innovation diffusion theory; Middle East; Opinion leaders; Social media; Social media datum; Social networking (online)  
  Abstract Many observers heralded the use of social media during recent political uprisings in the Middle East even dubbing Iran's post election protests a “Twitter Revolution”. We seek to put into perspective the use of social media in Egypt during the mass political demonstrations in 2011. We draw on innovation diffusion theory to argue that these media could have had an impact beyond their low adoption rates due to other factors related to demographics and social networks. We supplement our social media data analysis with survey data we collected in June 2011 from an opportunity sample of Egyptian youth. We conclude that in addition to the contextual factors noted above, the individuals within Egypt who used Twitter during the uprising have the characteristics of opinion leaders. These findings contribute to knowledge regarding the role of opinion leaders and social media, especially Twitter, during violent political demonstrations. © 2012 ISCRAM.  
  Address Virginia Tech, Blacksburg, VA 24061, United States; Arab Academy for Science and Technology, Cairo, Egypt; Alexandria University, Alexandria, Egypt  
  Corporate Author Thesis  
  Publisher Simon Fraser University Place of Publication Vancouver, BC Editor L. Rothkrantz, J. Ristvej, Z.Franco  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780864913326 Medium  
  Track Social Media and Collaborative Systems Expedition Conference 9th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 138  
Share this record to Facebook
 

 
Author (up) Liuqing Li; Edward A. Fox pdf  isbn
openurl 
  Title Disaster Response Patterns across Different User Groups on Twitter: A Case Study during Hurricane Dorian Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 838-848  
  Keywords Hurricane, Response, Pattern, User Classification, Twitter  
  Abstract We conducted a case study analysis of disaster response patterns across different user groups during Hurricane Dorian in 2019. We built a tweet collection about the hurricane, covering a two week period. We divided Twitter users into two groups: brand/organization or individual. We found a significant difference in response patterns between the groups. Brand users increasingly participated as the disaster unfolded, and they posted more tweets than individual users on average. Regarding emotions, brand users posted more tweets with joy and surprise, while individual users posted more tweets with sadness. Fear was a common emotion between the two groups. Further, both groups used different types of hashtags and words in their tweets. Some distinct patterns were also discovered in their concerns on specific topics. These results suggest the value of further exploration with more tweet collections, considering the behavior of different user groups during disasters.  
  Address Department of Computer Science, Virginia Tech; Department of Computer Science, Virginia Tech;  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-74 ISBN 2411-3460 Medium  
  Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes liuqing@vt.edu Approved no  
  Call Number Serial 2275  
Share this record to Facebook
 

 
Author (up) Liuqing Li; Edward A. Fox pdf  isbn
openurl 
  Title Understanding patterns and mood changes through tweets about disasters Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages  
  Keywords Disaster, Pattern, User Classification, Mood Detection, Twitter  
  Abstract We analyzed a sample of large tweet collections gathered since 2011, to expand understanding about tweeting

patterns and emotional responses of different types of tweeters regarding disasters. We selected three examples for

each of four disaster types: school shooting, bombing, earthquake, and hurricane. For each collection, we deployed

our novel model TwiRole for user classification, and an existing deep learning model for mood detection. We

found differences in the daily tweet count patterns, between the different types of events. Likewise, there were

different average scores and patterns of moods (fear, sadness, surprise), both between types of events, and between

events of the same type. Further, regarding surprise and fear, there were differences among roles of tweeters. These

results suggest the value of further exploration as well as hypothesis testing with our hundreds of event and trend

related tweet collections, considering indications in those that reflect emotional responses to disasters.
 
  Address Virginia Tech, United States of America  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes Approved no  
  Call Number Serial 1863  
Share this record to Facebook
 

 
Author (up) 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 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  
Share this record to Facebook
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: