Predicting floods with Flickr tags.
Predicting floods with Flickr tags.
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DOI：
10.1371/journal.pone.0172870
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Procter R
中科院分区：
文献类型：
--
作者：
Tkachenko N;Jarvis S;Procter R
社交媒体帖子中的用户生成内容（UGC）及其相关元数据（如时间和位置戳）越来越多地被用于在飓风、风暴和洪水等自然灾害事件期间提供有用的操作信息。这些新的数据来源的主要优点是双重的。首先，在纯粹的附加意义上，与传统的传感器网络相比，它们可以提供更密集的危险地理覆盖。其次，它们提供了物理传感器无法做到的事情：通过记录个人观察和经验，它们直接记录了危害对人类环境的影响。由于这个原因，内容的解释（例如，主题标签、图像、文本、表情符号等）和元数据（例如，关键字、标签、地理位置）已经成为对社交媒体分析的许多研究的焦点。然而，由于当前方法中的语义标签的选择通常被简化为事件的确切名称或类型（例如，标签“#桑迪”或“#桑迪”），这些方法的主要限制仍然是它们仅仅是短时预报能力。在这项研究中，我们利用最近几次洪水事件期间发布的图像的多义性标签，并演示如何使用此类自愿地理数据在事件爆发前提供事件预警。
Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.