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

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 adaptively determines the preference of top-k ranking and user workload over time. We demonstrate the applicability of this approach for Emergency Operation Centers (EOCs) by performing an evaluation based on real world data from six crisis events. We analyze the trade-off between recall and workload recommendation across periodic and realtime settings. Our experiments demonstrate that the proposed ranking selection approach can improve the efficiency of monitoring social media requests while optimizing the need for user attention.