Tkachenko Nataliya, Jarvis Stephen, Procter Rob
Warwick Institute for the Science of Cities, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
PLoS One. 2017 Feb 24;12(2):e0172870. doi: 10.1371/journal.pone.0172870. eCollection 2017.
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.
社交媒体帖子中的用户生成内容（UGC）及其相关元数据（如时间和位置标记）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。这些新数据源的主要优势有两方面。首先，从纯粹的补充意义上讲，与传统传感器网络相比，它们可以提供更密集的灾害地理覆盖范围。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历，它们直接记录了灾害对人类环境的影响。因此，对内容（如主题标签、图像、文本、表情符号等）和元数据（如关键词、标签、地理位置）的解读一直是社交媒体分析众多研究的重点。然而，由于当前方法中的语义标签选择通常仅限于事件的确切名称或类型（如主题标签“#桑迪”或“#洪水”），此类方法的主要局限性仍然在于其仅仅具有即时预报能力。在本研究中，我们利用近期几次洪水事件期间发布的图像的多义词标签，并展示了这种自愿提供的地理数据如何能够在事件爆发前用于提供早期预警。
