DocumentCode :
1824226
Title :
TopicFlow: Visualizing topic alignment of Twitter data over time
Author :
Malik, S. ; Smith, A. ; Hawes, Timothy ; Papadatos, Panagis ; Jianyu Li ; Dunne, Cody ; Shneiderman, Ben
Author_Institution :
Univ. of Maryland, College Park, MD, USA
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
720
Lastpage :
726
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.
Keywords :
data visualisation; social networking (online); statistical analysis; TopicFlow; Twitter data; Twitter stream; automated analysis; data visualization; hashtags; interactive tool; real-time data; social media; statistical topic modeling; topic alignment; visualization techniques; word-pairs; Analytical models; Conferences; Data models; Data visualization; Market research; Measurement; Twitter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location :
Niagara Falls, ON
Type :
conf
Filename :
6785782
Link To Document :
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