A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura
A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura
复制标题
一种引导潜在狄利克雷分配方法，用于调查劳拉飓风期间 Twitter 数据的实时潜在主题
DOI：
10.1177/01655515211007724
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发表时间：
2021
影响因子：
2.4
通讯作者：
J. Silbernagel
中科院分区：
文献类型：
--
作者：
Sulong Zhou;Pengyu Kan;Qunying Huang;J. Silbernagel
自然灾害造成重大损失、人员伤亡和经济损失。Twitter被用来支持及时的灾难响应和管理，因为人们倾向于在灾难事件期间在公共社交媒体平台上交流和传播信息。为了从推文中检索实时的态势感知(SA)信息，挖掘文本的最有效方法是使用自然语言处理(NLP)。在高级NLP模型中，监督方法可以将推文分类为不同的类别，以获得洞察并利用来自社交媒体数据的有用SA信息。然而，高性能的监督模型需要领域知识来指定类别，并涉及昂贵的标记任务。这项研究提出了一种有指导的潜在狄里克莱特分配(LDA)工作流，以调查最近的灾难事件-2020年飓风劳拉-期间推文中的时间潜在话题。通过整合先验知识、一致性模型、LDA主题可视化和官方报告的验证，我们的指导方法显示，在飓风劳拉的10天期间，大多数推文包含几个潜在的主题。这一结果表明，最新的监督模型没有充分利用推文信息，因为它们只为每条推文分配了一个标签。相比之下，我们的模型不仅可以识别不同灾难事件期间的新兴主题，而且还提供了对分类模式的多标签引用。此外，我们的结果可以帮助快速识别和提取SA信息，以供响应者、利益相关者和普通公众使用，以便他们能够在飓风事件期间采取及时的响应策略并明智地分配资源。
Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.
影响因子：
5
作者：
Jirapa Vongkusolkit;Qunying Huang
通讯作者：
Jirapa Vongkusolkit;Qunying Huang