危机信息学的无监督主题标签检索与可视化
Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics
摘要：
在Twitter这样的社交媒体中，标签携带了大量的语义信息，可以很容易地与正文区分开来。以有意义的方式探索和可视化标签空间可以为数据集提供重要的见解，特别是在危机情况下。在这篇演示论文中，我们提出了一个功能原型HashViz，它摄取了在危机情况（如拉斯维加斯枪击案）之后收集的推文语料库，并使用fastText技巧包语义嵌入算法（来自Facebook Research）将单词和标签嵌入到向量空间中。以这种方式获得的主题标签向量可以使用2D中的t-SNE降维算法来可视化。虽然存在多个Twitter可视化平台，但HashViz的特点是简单，可扩展，交互性和便携性，足以在任意灾难后收集数百万条推文语料库，而无需特殊用途的安装，技术专业知识，人工监督或昂贵的软件或基础设施投资。虽然简单，我们表明，HashViz提供了一种直观的方式来总结，并深入了解，一个发展中的危机情况。HashViz也是完全无监督的，不需要手动输入就可以从原始语料库到可视化和搜索界面。使用最近的拉斯维加斯大规模枪击事件作为案例研究，我们说明了HashViz在客户端仅使用Web浏览器的潜力。
英文摘要：
In social media like Twitter, hashtags carry a lot of semantic information and can be easily distinguished from the main text. Exploring and visualizing the space of hashtags in a meaningful way can offer important insights into a dataset, especially in crisis situations. In this demonstration paper, we present a functioning prototype, HashViz, that ingests a corpus of tweets collected in the aftermath of a crisis situation (such as the Las Vegas shootings) and uses the fastText bag-of-tricks semantic embedding algorithm (from Facebook Research) to embed words and hashtags into a vector space. Hashtag vectors obtained in this way can be visualized using the t-SNE dimensionality reduction algorithm in 2D. Although multiple Twitter visualization platforms exist, HashViz is distinguished by being simple, scalable, interactive and portable enough to be deployed on a server for million-tweet corpora collected in the aftermath of arbitrary disasters, without special-purpose installation, technical expertise, manual supervision or costly software or infrastructure investment. Although simple, we show that HashViz offers an intuitive way to summarize, and gain insight into, a developing crisis situation. HashViz is also completely unsupervised, requiring no manual inputs to go from a raw corpus to a visualization and search interface. Using the recent Las Vegas mass shooting massacre as a case study, we illustrate the potential of HashViz using only a web browser on the client side.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
