Analyzing Twitter Data for Emergency Management

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2018

Author dc:creator

• Marbouti, Mahshid

Advisor dc:contributor.advisor

• Maurer, Frank

Committee members dc:contributor.committeemember

• Braun, John
• Willett, Wesley
• Far, Behrouz Homayoun
• Costa Sousa, Mario

• Situational Awareness
• Social Media
• Machine Learning
• User Study
• Software Design
• Social Media Monitoring Tools
• Human Computer Interaction

Statement 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.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/31944

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/106677