Real-Time Visual Analysis of High-Volume Social Media Posts

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 that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.