Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers
Hemant Purohit, Carlos Castillo, Muhammad Imran, Rahul Pandey

TL;DR
This paper introduces a new ranking system for social media alerts in emergency centers that balances alert relevance with user workload, optimizing response efficiency during crises.
Contribution
It presents a novel model linking ranking performance to workload bounds and proposes an adaptive Pareto optimal algorithm for alert selection in emergency scenarios.
Findings
The approach improves monitoring efficiency in emergency centers.
It effectively balances recall and workload in real-world crisis data.
The method adapts to periodic and real-time alerting settings.
Abstract
Extensive research on social media usage during emergencies has shown its value to provide life-saving information, if a mechanism is in place to filter and prioritize messages. Existing ranking systems can provide a baseline for selecting which updates or alerts to push to emergency responders. However, prior research has not investigated in depth how many and how often should these updates be generated, considering a given bound on the workload for a user due to the limited budget of attention in this stressful work environment. This paper presents a novel problem and a model to quantify the relationship between the performance metrics of ranking systems (e.g., recall, NDCG) and the bounds on the user workload. We then synthesize an alert-based ranking system that enforces these bounds to avoid overwhelming end-users. We propose a Pareto optimal algorithm for ranking selection that…
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Taxonomy
TopicsPublic Relations and Crisis Communication · Disaster Management and Resilience · Evacuation and Crowd Dynamics
