Analyzing Twitter Data for Emergency Management

Marbouti, Mahshid

Analyzing Twitter Data for Emergency Management

dc.contributor.advisor	Maurer, Frank
dc.contributor.author	Marbouti, Mahshid
dc.contributor.committeemember	Braun, John
dc.contributor.committeemember	Willett, Wesley
dc.contributor.committeemember	Far, Behrouz Homayoun
dc.contributor.committeemember	Costa Sousa, Mario
dc.date	2018-11
dc.date.accessioned	2018-05-25T20:05:38Z
dc.date.available	2018-05-25T20:05:38Z
dc.date.issued	2018-05-23
dc.description.abstract	Social media is an important part of our lives. It is hard to ignore the role of social media in our everyday lives and during disastrous events. During emergencies, emergency personnel need to make strategic decisions in a short amount of time, coordinate actions and prioritize tasks. Social media can be a powerful source of information that comes directly from the public; it can reflect public sentiment, needs, and questions. In this research, I performed an interview study to find the use cases and challenges that emergency-related organizations encounter when dealing with social media. The findings reveal the needs of practitioners for designing social media monitoring tools to help them find the information they need. One of the main challenges for practitioners is that commercial tools are not designed for emergency response, and academic approaches do not consider their requirements. This dissertation brings insight into the design of expert-informed machine learning solutions for identifying relevant information from social media by following a human-centered design approach. By actively being involved with emergency practitioners throughout three years, I designed, developed, and evaluated a social media monitoring tool for emergency response. The evaluation results show the effectiveness of bringing analysts into the classification loop to train and get feedback to machine learning classifiers. It also shows that analysts would like to combine the training tasks with their response tasks. Another aspect of this research is exploring the significance of various categories of features and machine learning algorithms and automatically identifying situational awareness information in different emergency event datasets. Results show that significant features vary across different events which indicates that training should happen during the event.	en_US
dc.identifier.citation	Marbouti, M. (2018). Analyzing Twitter Data for Emergency Management (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31944	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/31944
dc.identifier.uri	http://hdl.handle.net/1880/106677
dc.language.iso	eng
dc.publisher.faculty	Graduate Studies
dc.publisher.faculty	Science
dc.publisher.institution	University of Calgary	en
dc.publisher.place	Calgary	en
dc.rights	University 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.
dc.subject	Situational Awareness
dc.subject	Social Media
dc.subject	Machine Learning
dc.subject	User Study
dc.subject	Software Design
dc.subject	Social Media Monitoring Tools
dc.subject	Human Computer Interaction
dc.subject.classification	Information Science	en_US
dc.subject.classification	Computer Science	en_US
dc.subject.classification	Engineering	en_US
dc.title	Analyzing Twitter Data for Emergency Management
dc.type	doctoral thesis
thesis.degree.discipline	Computer Science
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.item.requestcopy	true