In recent years, situation awareness has been recognised as a critical part of effective decision making, in particular for crisis management. One way to extract value and allow for better situation awareness is to develop a system capable of analysing a dataset of multiple posts, and clustering consistent posts into different views or stories (or, ‘world views’). However, this can be challenging as it requires an understanding of the data, including determining what is consistent data, and what data corroborates other data. Attempting to address these problems, this article proposes Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on utilising the Subject–Verb–Object typology in order to construct semantically consistent world views, in which individuals—particularly those involved in crisis response—might achieve an enhanced picture of a situation from social media data. To evaluate our system and its ability to provide enhanced situation awareness, we tested it against existing approaches, including human data analysis, using a variety of real-world scenarios. The results indicated a noteworthy degree of evidence (e.g., in cluster granularity and meaningfulness) to affirm the suitability and rigour of our approach. Moreover, these results highlight this article’s proposals as innovative and practical system contributions to the research field.

中文翻译：

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使用语义聚类支持Twitter上的态势感知：世界观案例

近年来，形势意识已被认为是有效决策的关键部分，特别是对于危机管理而言。提取价值并更好地了解情况的一种方法是开发一种系统，该系统能够分析多个帖子的数据集，并将一致的帖子聚集到不同的视图或故事（或“世界视图”）中。但是，这可能具有挑战性，因为它需要了解数据，包括确定什么是一致的数据，以及哪些数据证实了其他数据。为了解决这些问题，本文提出了主谓语-宾语语义后缀树聚类（SVOSSTC）及其支持的系统，特别关注Twitter内容。SVOSSTC的新颖性和价值在于，它强调利用主语-动词-宾语类型学来构建语义上一致的世界观，在这些世界观中，个人（尤其是参与危机应对的人）可能会从社交媒体数据中获得对状况的更全面了解。 。为了评估我们的系统及其提供增强的态势感知的能力，我们使用各种现实场景对现有方法（包括人类数据分析）进行了测试。结果表明了值得注意的证据程度（例如，在集群粒度和意义上），以确认我们方法的适用性和严格性。而且，这些结果突出了本文的建议，为创新和实用的系统对研究领域做出了贡献。