Automating Relevance and Trust Detection in Social Media Data for Emergency Response
Much has been written concerning the value of using messaging and microblogged data from crowds of non-professional participants during disasters. Often referred to as microblogging, the practice of average citizens reporting on activities “on-the-ground” during a disaster is seen as increasingly valuable. Data produced through microblogging is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. Microblogging is seen to have intrinsic value across responder organizations and victims because of its growing ubiquity, communications rapidity, and cross-platform accessibility.
However, despite the evidence of strong value to those experiencing the disaster and those seeking information concerning the disaster, there has been very little uptake of message data by large-scale, disaster response organizations. Real-time message data being contributed by those affected by a disaster has not been incorporated into established mechanisms for organizational decision- making. Through this research, we seek to find mechanisms to automatically classify information within a microblogged data stream to be relevant (i.e., disaster related) as well as verifiable and actionable.
Publications
HongMin Li, Doina Caragea, and Cornelia Caragea. "Towards Practical Usage of Domain Adaptation Algorithms in Classifying Disaster Related Tweets." In: Proceedings of the 14th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2017), Albi, France, 2017. [ISCRAM-17 link] [pdf]
Kishore Neppalli, Cornelia Caragea, Doina Caragea, Murilo Cerqueira Medeiros, Andrea Tapia, Shane Halse. "Predicting Tweet Retweetability during Hurricane Disasters." In: International Journal of Information Systems for Crisis Response and Management (IJISCRAM 2017), 2017. [article] [pdf]
Kishore Neppalli, Cornelia Caragea, Anna Squicciarini, Andrea Tapia, and Sam Stehle. "Sentiment Analysis during Hurricane Sandy in Emergency Response." In: The International Journal of Disaster Risk Reduction (IJDRR 2017), 2017. [article] [pdf]
HongMin Li, Doina Caragea, Cornelia Caragea, and Nic Herndon (2017). "Disaster Response Aided by Tweet Classification with a Domain Adaptation Approach." In: Journal of Contingencies and Crisis Management (JCCM), Special Issue on HCI in Critical Systems, 2017. In press. [pdf]
Shane Halse, Andrea H. Tapia, Anna Squicciarini, and Cornelia Caragea. "An Emotional Step Towards Automated Trust Detection in Crisis Social Media." In: Information, Communication & Society, 2017. [article]
Cornelia Caragea, Adrian Silvescu, and Andrea Tapia. "Identifying Informative Messages in Disasters using Convolutional Neural Networks." In: Proceedings of the 13th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2016), Rio de Janeiro, Brazil, 2016. [pdf]
Kishore Neppalli, Murilo Cerqueira Medeiros, Cornelia Caragea, Doina Caragea, Andrea Tapia, Shane Halse. "Retweetability Analysis and Prediction during Hurricane Sandy." In: Proceedings of the 13th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2016), Rio de Janeiro, Brazil, 2016. [pdf]
Shane Halse, Andrea Tapia, Anna Squicciarini, Cornelia Caragea. "An Emotional Step Towards Automated Trust Detection in Crisis Social Media." In: Proceedings of the 13th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2016), Rio de Janeiro, Brazil, 2016. [pdf]
Shane Halse, Andrea Tapia, Anna Squicciarini, Cornelia Caragea. "Tweet Factors Influencing Trust and Usefulness During Both Man-Made and Natural Disasters." In: Proceedings of the 13th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2016), Rio de Janeiro, Brazil, 2016. [pdf]
Hongmin Li, Nicolais Guevara, Nic Herndon, Doina Caragea, Kishore Neppalli, Cornelia Caragea, Anna Squicciarini, Andrea H. Tapia. "Twitter Mining for Disaster Response: A Domain Adaptation Approach." In: Proceedings of the 12th International Conference on Information Systems for Crisis Response and Management, (ISCRAM 2015), Kristiansand, Norway, 2015. [pdf]
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Brandon Truong, Cornelia Caragea, Anna Squicciarini, Andrea H. Tapia. "Extracting Valuable Information from Twitter During Natural Disasters." In: The 2014 Annual Meeting of the Association for Information Science and Technology (ASIS&T 2014), Seattle, WA, USA. [pdf]
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Amanda Karavolia, Dimitrios Vogiatzis, Brandon Truong, Andrea H. Tapia, Cornelia Caragea, Anna Squicciarini and Georgios Paliouras. "Tweet Type, Location and Popularity: Case Study Hurricane Sandy." In: The 2014 KDD Workshop on Learning about Emergencies from Social Information, co-located with Knowledge Discovery and Data Mining 2014 (KDD-LESI 2014), New York City, USA. [pdf]
Cornelia Caragea, Anna Squicciarini, Sam Stehle, Kishore Neppalli, Andrea H. Tapia. "Mapping Moods: Geo-Mapped Sentiment Analysis During Hurricane Sandy." In: Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2014), University Park, Pennsylvania, USA, 2014. [pdf]
The dataset and code are available upon request by sending an email to cornelia@uic.edu.
2011
Cornelia Caragea, Nathan McNeese, Anuj Jaiswal, Greg Traylor, Hyun-Woo Kim, Prasenjit Mitra, Dinghao Wu, Andrea H. Tapia, C. Lee Giles, Bernard J. Jansen, John Yen. "Classifying Text Messages for the Haiti Earthquake." In: Proceedings of the 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2011), Lisbon, Portugal, 2011. [pdf][slides]
Highlights of our ISCRAM 2011 paper on the iRevolution page can be found here.
2017
2016
2015
2014
People
Faculty
Cornelia CarageaAssociate Professor, Computer Science, Kansas State University
Andrea H. TapiaAssociate Professor, College of Information Sciences and Technology, The Pennsylvania State University
Anna SquicciariniAssistant Professor, College of Information Sciences and Technology, The Pennsylvania State University
Students
Sam Stehle Kishore NeppalliThis research is supported by a collaborative grant from the National Science Foundation. [NSF project website].