Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers
Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers
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DOI：
10.1109/wi.2018.00-88
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发表时间：
2018-09
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影响因子：
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通讯作者：
Hemant Purohit;C. Castillo;Muhammad Imran-;Rahul Pandey
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文献类型：
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作者：
Hemant Purohit;C. Castillo;Muhammad Imran-;Rahul Pandey
对紧急情况下社交媒体使用情况的广泛研究表明，如果有一种过滤和优先处理信息的机制，它可以提供拯救生命的信息。现有的排名系统可以为选择向紧急救援人员推送哪些更新或警报提供基准。然而，之前的研究并没有深入调查这些更新应该产生多少次和多久一次，考虑到在这种紧张的工作环境中，由于有限的注意力预算，用户的工作量是给定的。本文提出了一个新的问题和一个模型来量化排名系统的性能指标（例如，召回，NDCG）与用户工作量界限之间的关系。然后，我们合成了一个基于警报的排名系统，该系统强制执行这些界限，以避免压倒最终用户。我们提出了一种帕累托最优排序算法，该算法可以自适应地确定top-k排序的偏好和用户工作量随时间的变化。通过基于六个危机事件的真实世界数据进行评估，我们证明了该方法对紧急操作中心（eoc）的适用性。我们分析了定期设置和实时设置之间的召回和工作负载推荐之间的权衡。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，同时优化用户关注需求。
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.