Real-Time Visual Analysis of High-Volume Social Media Posts
Johannes Knittel, Steffen Koch, Tan Tang, Wei Chen, Yingcai Wu, Shixia, Liu, Thomas Ertl

TL;DR
This paper introduces an interactive system for real-time visual analysis of high-volume social media streams, enabling timely insights through efficient clustering and visualization without extensive preprocessing.
Contribution
The work presents a novel, explainable dynamic clustering algorithm and an integrated visual system for analyzing streaming social media data in real-time, including non-geolocated posts.
Findings
Effective real-time thematic visualization of social media streams
Adaptive clustering provides diverse, recent post summaries
System supports exploration without extensive preprocessing
Abstract
Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors…
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