Can public administrators use community education interventions in disaster management? We examine community education interventions as tools that raise awareness of hazards, communicate risks, and develop resilience in communities. We study a programme in Essex County, UK, in which Essex County Fire and Rescue Services used the results of proportional hazards modelling to identify localities at risk of accidental dwelling fires and to target community education interventions. We then assess the intervention's impact by comparing the incidence of accidental dwelling fires before and after the Parish Safety Volunteer programme began, as well as between treated and untreated areas, in a differencein-difference regression. We find that there are greater reductions in accidental dwelling fires in treated areas than in untreated areas, and argue that community education interventions can forge vital networks and increase safety for vulnerable people, as well as build trust and resilience important for disaster and crisis prevention.

IntroductionPolitical science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way.Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research.

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The popularity and ubiquity of social networks has enabled a new form of decentralised online collaboration: groups of users gathering around a central theme and working together to solve problems, complete tasks and develop social connections. Groups that display such `organic collaboration' have been shown to solve tasks quicker and more accurately than other methods of crowdsourcing. They can also enable community action and resilience in response to different events, from casual requests to emergency response and crisis management. However, engaging such groups through formal agencies risks disconnect and disengagement by destabilising motivational structures. This paper explores case studies of this phenomenon, reviews models of motivation that can help design systems to harness these groups and proposes a framework for lightweight engagement using existing platforms and social networks.