Applications of deep learning in extracting actionable information from crisis-related social media content

Doctor of PhilosophyDepartment of Computer ScienceDoina CarageaWe have witnessed a large number of crisis situations in recent years, from natural disasters to man-made disasters and also to deadly animal and human health crises, culminating with the ongoing Covid-19 public health crisis. Disasters can have devastating health and socio-economic impacts. Emergency response and critical resource management during crises are pivotal tasks in mitigating the impacts of such events. These tasks require time-critical and reliable information for effective implementation. During emergent crises, there is a huge influx of information from various sources, which makes the task of collecting and managing reliable information harder. Identifying key information relevant for emergency responders and policy makers from huge streams of data is an infeasible task for human to attempt. There is a clear need of a pipeline of systems that can monitor, identify and collect actionable and relevant information from incomplete and noisy sources of data. Social media has evolved into a platform for people to share their concerns, report information as eyewitnesses of events, and also call for help, especially during crisis situations. However, due to the unstructured nature of data shared in these digital media, inherent noise and potential misinformation, extraction of actionable information is a challenging task. Considering the challenges associated with modern data-driven emergency response and crisis management, deep-learning is a natural choice in making use of the large volume of unstructured data. However, deep-learning models, typically, require a large amount of annotated or labelled data, which may not always be available for an emergent crisis. This dissertation aims to address some of these issues by exploring multi-task and multimodal deep-learning approaches, combined with self-supervised representation learning. From an application point of view, this dissertation tackles two specific tasks surrounding crisis information management: firstly, the time-critical task of identifying actionable information for emergent crisis, and secondly, the task of analyzing public response to crisis events and the policies surrounding the events through social-media

Note: Scalable Multiphoton Coincidence-counting Electronics

We present a multichannel coincidence-counting module for use in quantum optics experiments. The circuit takes up to four transistor–transistor logic pulse inputs and counts either twofold, threefold, or fourfold coincidences, within a user-selected coincidence-time window as short as 12 ns. The module can accurately count eight sets of multichannel coincidences, for input rates of up to 84 MHz. Due to their low cost and small size, multiple modules can easily be combined to count arbitrary M-order coincidences among N inputs