The work of Emergency Management (EM) agencies requires timely collection of relevant data to inform decision-making for operations and public communication before, during, and after a disaster. However, the limited human resources available to deploy for field data collection is a persistent problem for EM agencies. Thus, over the last decade, many of these agencies have started leveraging social media as a supplemental data source and a new venue to engage with the public. Such uses present both opportunities and challenges. While prior research has analyzed the potential benefits and attitudes of practitioners and the public when leveraging social media during disasters, a gap exists in the critical analysis of the actual practices and uses of social media among EM agencies, across both geographical regions and phases of the EM lifecycle - typically mitigation, preparedness, response, and recovery. In this paper, we conduct a mixed-method analysis to update and fill this gap on how EM practitioners in the U.S. and Europe use social media, building on a survey study of about 150 professionals and a follow-up interview study with 11 participants. The results indicate that using social media is no longer a non-traditional practice in operational and informational processes for the decision-making of EM agencies working at both the local level (e.g., county or town) and non-local level (e.g., state/province, federal/national) for emergency management. Especially, the practitioners affiliated with agencies working at the local level have a very high perceived value of social media for situational awareness (e.g., analyzing disaster extent and impact) and public communication (e.g., disseminating timely information and correcting errors in crisis coverage). Further, practitioners now engage with the public during the preparedness phase to mobilize them during the response phase. We present a model to understand the current practices of communication between agencies and the public, as well as among practitioners while leveraging social media. We also discuss novel challenges, including public fragmentation caused by the increasing use of multiple social media platforms, information integrity, and social listening expectations. We conclude with the policy, technological, and socio-technical needs to design future social media analytics systems to support the work of EM agencies in such communication.

Data annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important to help beginning annotators identify how their early-stage knowledge can lead to inaccurate answers, which in turn, helps to ensure quality annotations at scale. To investigate this issue, we conducted a formative study involving eight individuals from the field of disaster management, each possessing varying levels of expertise. The goal was to understand the prevalent factors contributing to disagreements among annotators when classifying Twitter messages related to disasters and to analyze their respective responses. Our analysis identified two primary causes of disagreement between expert and beginner annotators: 1) a lack of contextual knowledge or uncertainty about the situation, and 2) the absence of visual or supplementary cues. Based on these findings, we designed a Context interface, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet. The summative study compares Context design with two widely used designs in data annotation UI, Highlight and Reasoning-based interfaces. We found significant differences between these designs in terms of attitudinal and behavioral data. We conclude with implications for designing future interfaces aiming at closing the knowledge gap among annotators.

Formal response organizations perform rapid damage assessments after natural and human-induced disasters to measure the extent of damage to infrastructures such as roads, bridges, and buildings. This time-critical task, when performed using traditional approaches such as experts surveying the disaster areas, poses serious challenges and delays response. This paper presents an AI-based system that leverages citizen science to collect damage images reported on social media and perform rapid damage assessment in real-time. Several image processing models in the system tackle non-trivial challenges posed by social media as a data source, such as high-volume of redundant and irrelevant content. The system determines the severity of damage using a state-of-the-art computer vision model. Together with a response organization in the US, we deployed the system to identify damage reports during a major real-world disaster. We observe that almost 42% of the images are unique, 28% relevant, and more importantly, only 10% of them contain either mild or severe damage. Experts from our partner organization provided feedback on the system's mistakes, which we used to perform additional experiments to retrain the models. Consequently, the retrained models based on expert feedback on the target domain data helped us achieve significant performance improvements.

Workflow builders are designed to facilitate development of automated processes and support flexible applications that can be updated, enhanced or completely revamped. The Hewlett-Packard WorkManager is an open product data management solution with workflow management capabilities. WorkManager supports the entire product lifecycle by providing a single, logical repository for all data, and it manages and tracks enterprise-wide processes. With a strong information management platform at its core, WorkManager provides central administration capabilities, including supervision and intervention, where necessary. Because enterprise data is usually fragmented and stored in a variety of legacy systems, and different organizations have different amount of control over their data, an enterprise workflow system needs to support processes accessing data from different sites and applications. This paper describes the architecture of distributed workflow, Hewlett-Packard's solution to the enterprise workflow problem. The architecture is an extension of the existing WorkManager architecture. Its development is based on user requirements and four high-level user models. The user models and the architecture are described.>