The Community for Technology Leaders
2015 IEEE Conference on Visual Analytics Science and Technology (VAST) (2015)
Chicago, IL, USA
Oct. 25, 2015 to Oct. 30, 2015
ISBN: 978-1-4673-9783-4
pp: 57-64
Wenwen Dou , UNC Charlotte, USA
Isaac Cho , UNC Charlotte, USA
Omar ElTayeby , UNC Charlotte, USA
Jaegul Choo , Korea University, South Korea
Xiaoyu Wang , Taste Analytics, USA
William Ribarsky , UNC Charlotte, USA
ABSTRACT
The wide-spread of social media provides unprecedented sources of written language that can be used to model and infer online demographics. In this paper, we introduce a novel visual text analytics system, DemographicVis, to aid interactive analysis of such demographic information based on user-generated content. Our approach connects categorical data (demographic information) with textual data, allowing users to understand the characteristics of different demographic groups in a transparent and exploratory manner. The modeling and visualization are based on ground truth demographic information collected via a survey conducted on Reddit.com. Detailed user information is taken into our modeling process that connects the demographic groups with features that best describe the distinguishing characteristics of each group. Features including topical and linguistic are generated from the user-generated contents. Such features are then analyzed and ranked based on their ability to predict the users' demographic information. To enable interactive demographic analysis, we introduce a web-based visual interface that presents the relationship of the demographic groups, their topic interests, as well as the predictive power of various features. We present multiple case studies to showcase the utility of our visual analytics approach in exploring and understanding the interests of different demographic groups. We also report results from a comparative evaluation, showing that the DemographicVis is quantitatively superior or competitive and subjectively preferred when compared to a commercial text analysis tool.
INDEX TERMS
Demographic Analysis, Visual Text Analysis, User Interface, Social Media
CITATION

W. Dou, I. Cho, O. ElTayeby, J. Choo, X. Wang and W. Ribarsky, "DemographicVis: Analyzing demographic information based on user generated content," 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), Chicago, IL, USA, 2015, pp. 57-64.
doi:10.1109/VAST.2015.7347631
95 ms
(Ver 3.3 (11022016))