TopicFlow: visualizing topic alignment of Twitter data over time

Authors:
Sana Malik;Alison Smith;Timothy Hawes;Panagis Papadatos;Jianyu Li;Cody Dunne;Ben Shneiderman
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD and DECISIVE ANALYTICS Corporation, Arlington, VA;DECISIVE ANALYTICS Corporation, Arlington, VA;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

Social media, particularly Twitter, provides an abundance of real-time data. To account for this volume, researchers often use automated analysis and visualization techniques to produce a high-level overview of a Twitter stream. Existing techniques for understanding Twitter data make use of hashtags or word-pairs and may ignore the complex trends in discussions over time. To remedy this, we present an application of statistical topic modeling and alignment (binned topic models) to group related tweets into automatically generated topics and TopicFlow, an interactive tool to visualize the evolution of these topics. The effectiveness of this visualization for reasoning about large data sets is demonstrated by a usability study with 18 participants.