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Social-EOC: Serviceability Model to Rank Social Media Requests for Emergency Operation Centers

机译:Social-EOC:用于对应急运营中心的社交媒体请求进行排名的可服务性模型

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The public expects a prompt response from emergency services to address requests for help posted on social media. However, the information overload of social media experienced by these organizations, coupled with their limited human resources, challenges them to timely identify and prioritize critical requests. This is particularly acute in crisis situations where any delay may have a severe impact on the effectiveness of the response. While social media has been extensively studied during crises, there is limited work on formally characterizing serviceable help requests and automatically prioritizing them for a timely response. In this paper, we present a formal model of serviceability called Social-EOC (Social Emergency Operations Center), which describes the elements of a serviceable message posted in social media that can be expressed as a request. We also describe a system for the discovery and ranking of highly serviceable requests, based on the proposed serviceability model. We validate the model for emergency services, by performing an evaluation based on real-world data from six crises, with ground truth provided by emergency management practitioners. Our experiments demonstrate that features based on the serviceability model improve the performance of discovering and ranking (nDCG up to 25%) service requests over different baselines. In the light of these experiments, the application of the serviceability model could reduce the cognitive load on emergency operation center personnel, in filtering and ranking public requests at scale.
机译:公众希望紧急服务部门能够迅速做出回应,以解决在社交媒体上发布的帮助请求。但是,这些组织所经历的社交媒体信息超载,再加上其有限的人力资源,给他们及时识别关键请求和确定优先级提出了挑战。这在危机情况下尤其严重,在这种情况下,任何延误都可能对响应的有效性产生严重影响。尽管在危机期间对社交媒体进行了广泛的研究,但是在形式上表征可使用的帮助请求并自动对它们进行优先排序以进行及时响应方面的工作还很少。在本文中,我们提出了一种称为“ Social-EOC”(社会紧急行动中心)的可服务性正式模型,该模型描述了可表达为请求的在社交媒体中发布的可服务消息的元素。我们还基于提出的可维护性模型描述了一种用于发现和排序高度可维护请求的系统。我们通过基于来自六个危机的真实数据进行评估,并由应急管理从业人员提供实地数据,来验证应急服务模型。我们的实验表明,基于可维护性模型的功能可提高在不同基准上发现和排名(nDCG高达25%)服务请求的性能。根据这些实验,可服务性模型的应用可以减轻应急操作中心人员的认知负担,从而大规模过滤和排序公共需求。

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