Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics
Yao Gu, Mayank Kejriwal

TL;DR
HashViz is an unsupervised, scalable, and interactive tool that visualizes hashtags from crisis-related social media data using semantic embeddings and dimensionality reduction, aiding real-time crisis analysis without technical barriers.
Contribution
The paper introduces HashViz, a simple, scalable, and fully unsupervised visualization system for hashtags in crisis datasets, requiring no manual input or specialized infrastructure.
Findings
Effective visualization of hashtags in crisis data
Supports large-scale, real-time analysis
Operates fully in a web browser without installation
Abstract
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Data Visualization and Analytics · Public Relations and Crisis Communication
