Social Media Emergency Analysis and Realistic Evacuation Modeling

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dc.contributor.advisorAlhajj, Reda
dc.contributor.authorSahin, Coskun
dc.contributor.committeememberAlhajj, Reda
dc.contributor.committeememberOzyer, Tansel
dc.contributor.committeememberRokne, Jon George
dc.contributor.committeememberRuhe, Guenther
dc.contributor.committeememberMouhoub, Malek
dc.date2021-11
dc.date.accessioned2021-09-14T14:43:16Z
dc.date.available2021-09-14T14:43:16Z
dc.date.issued2021-09-07
dc.description.abstractThe widespread of disasters necessitates appropriate actions to be taken to avoid casualties or at least reduce them to the minimum level possible. In this work, we propose solutions to two of the ways we can help it. The first part is using the potential of social media to detect emergencies and provide further information for the public and rescue teams. It uses a multi-layer machine learning approach to find and cluster emergency-related messages. Natural language processing and information extraction techniques are adopted for location detection, casualty and severity calculation. The effectiveness of the model is shown on Twitter data in nearly real-time using its own API. The second part of the thesis investigates the area of crowd behavior modeling in order to provide a platform for simulating various emergency evacuation scenarios. Crowd behavior modeling is an important challenge especially for games, social simulation and military training software. There are various applications focusing on specific approaches to solve social, physical, emotional and cognitive dimensions of this problem. In this work, first, we built and agent-based framework that adopts OCC emotion model and Belief-Desire-Intention (BDI) approach in a heterogeneous environment containing individuals with different knowledge of the surroundings. It uses a deep Q-learning model running on top of a neural network for creating a set of partially-trained agents. Second, we simulate group decision-making process using a mathematical ferromagnetism model and analyze how it affects the overall success of the crowd achieving a goal together. Our work contributes to the literature in two different ways. Firstly, it shows that a multi-layer classification model is effective for detecting emergencies in real-time using unstructured data. Moreover, it provides evacuation simulation scenarios on the public buildings where an emergency occurs and saves valuable time for rescue teams while planning an emergency response. Secondly, it combines state-of-the-art frameworks for crowd behavior modeling with a partial learning mechanism. Our experiments show that the system is capable of simulating common group patterns during emergencies. Moreover, it provides a generic crowd framework, where partially-trained agents can find optimal solutions via interaction and collaboration.en_US
dc.identifier.citationSahin, C. (2021). Social Media Emergency Analysis and Realistic Evacuation Modeling (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/39190
dc.identifier.urihttp://hdl.handle.net/1880/113864
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectMachine learningen_US
dc.subjectSocial media analysisen_US
dc.subjectInformation extractionen_US
dc.subjectEmergency detectionen_US
dc.subjectCrowd behavior modelingen_US
dc.subjectMulti-agent systemsen_US
dc.subjectEvacuation simulationen_US
dc.subjectDeep Q-learningen_US
dc.subject.classificationComputer Scienceen_US
dc.titleSocial Media Emergency Analysis and Realistic Evacuation Modelingen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
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