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Extensive research on social media usage during emergencies has shown itsvalue to provide life-saving information, if a mechanism is in place to filterand prioritize messages. Existing ranking systems can provide a baseline forselecting which updates or alerts to push to emergency responders. However,prior research has not investigated in depth how many and how often shouldthese updates be generated, considering a given bound on the workload for auser due to the limited budget of attention in this stressful work environment.This paper presents a novel problem and a model to quantify the relationshipbetween the performance metrics of ranking systems (e.g., recall, NDCG) and thebounds on the user workload. We then synthesize an alert-based ranking systemthat enforces these bounds to avoid overwhelming end-users. We propose a Paretooptimal algorithm for ranking selection that adaptively determines thepreference of top-k ranking and user workload over time. We demonstrate theapplicability of this approach for Emergency Operation Centers (EOCs) byperforming an evaluation based on real world data from six crisis events. Weanalyze the trade-off between recall and workload recommendation acrossperiodic and realtime settings. Our experiments demonstrate that the proposedranking selection approach can improve the efficiency of monitoring socialmedia requests while optimizing the need for user attention.

Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers