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首页> 外文会议>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >SAVIZ: Interactive Exploration and Visualization of Situation Labeling Classifiers over Crisis Social Media Data
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SAVIZ: Interactive Exploration and Visualization of Situation Labeling Classifiers over Crisis Social Media Data

机译：SAVIZ：危机社交媒体数据上情境标签分类器的交互式探索和可视化

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Due to climate change and the effects of geopolitical and social challenges like the refugee crisis in Europe, the world is facing an unprecedented set of humanitarian problems. According to the United Nations, there is a projected funding shortfall of more than 20 billion dollars in addressing these needs. Technology can play a vital role in mitigating this burden, especially with the advent of real-time social media and advances in areas like Natural Language Processing and machine learning. An important problem addressed by machine learning in current crisis informatics platforms is situation labeling, which can be intuitively defined as semi-automatically assigning one or more actionable labels (such as food, medicine or water) to tweets or documents from a controlled vocabulary. Despite multiple advances, current situation labeling systems are noisy and do not generalize very well to arbitrary crisis data. Consequentially, consumers of these outputs (which include humanitarian responders) are unwilling to trust these outputs without due diligence or provenance. In this paper, we demonstrate an interactive visualization platform called SAVIZ that provides non-technical first responders with such capabilities. SAVIZ is completely built using open-source technologies, can be rendered on a web browser and is backward-compatible with several pre-existing crisis intelligence platforms. We use two real-world scenarios (the 2015 earthquake in Nepal, and the unfolding Ebola crisis in Africa) to illustrate the potential of SAVIZ.
机译：由于气候变化以及欧洲难民危机等地缘政治和社会挑战的影响，世界面临着前所未有的一系列人道主义问题。据联合国称，解决这些需求的资金缺口预计将超过200亿美元。技术在减轻这种负担方面可以发挥至关重要的作用，特别是随着实时社交媒体的出现以及自然语言处理和机器学习等领域的进步。当前危机信息学平台中机器学习解决的一个重要问题是情况标签，可以直观地定义为将半自动分配一个或多个可操作标签（例如食物，药品或水）分配给推文或受控词汇表中的文件。尽管取得了多项进步，但现状标签系统仍然嘈杂，不能很好地推广到任意危机数据 a 。因此，这些产出的消费者（包括人道主义应急人员）不愿在没有尽职调查或出处的情况下信任这些产出。在本文中，我们演示了一个称为SAVIZ的交互式可视化平台，该平台可为非技术急救人员提供此类功能。 SAVIZ是完全使用开放源代码技术构建的，可以在Web浏览器上进行渲染，并且与几个预先存在的危机情报平台向后兼容。我们使用两种现实情况（2015年尼泊尔地震和非洲持续爆发的埃博拉危机）来说明SAVIZ的潜力。

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