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