应急中心社交媒体警报的等级划分
Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers
摘要：
对紧急情况下社交媒体使用情况的广泛研究表明，如果建立一种机制来过滤和优先处理信息，社交媒体在提供拯救生命的信息方面具有价值。现有的排名系统可以提供用于选择向紧急响应者推送哪些更新或警报的基线。然而，先前的研究还没有深入研究这些更新应该生成多少以及多久生成一次，考虑到由于在这种紧张的工作环境中注意力预算有限而导致的用户工作量的给定界限。本文提出了一个新的问题和模型来量化排名系统的性能指标之间的关系（例如，调用，NDCG）和用户工作负载的界限。然后，我们合成一个基于警报的排名系统，强制执行这些界限，以避免压倒性的最终用户。我们提出了一个帕累托最优算法的排名选择，自适应地确定随着时间的推移的首选top-k排名和用户的工作量。我们证明了这种方法的适用性，紧急行动中心（EOCs）进行评估的基础上，从六个危机事件的真实世界的数据。我们分析了召回和工作量建议之间的权衡定期和实时设置。我们的实验表明，所提出的排名选择方法可以提高监控社交媒体请求的效率，同时优化用户注意力的需求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
