TY - GEN
T1 - DemographicVis
T2 - 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015
AU - Dou, Wenwen
AU - Cho, Isaac
AU - ElTayeby, Omar
AU - Choo, Jaegul
AU - Wang, Xiaoyu
AU - Ribarsky, William
N1 - Funding Information:
Acknowledgements: The work is supported in part by the Army Research Office under contract number W911NF-13-1-0083 and the U.S. Department of Homeland Security's VACCINE Center under award no. 2009-ST- 061-CI0002.
PY - 2015/12/4
Y1 - 2015/12/4
N2 - 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.
AB - 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.
KW - Demographic Analysis
KW - Social Media
KW - User Interface
KW - Visual Text Analysis
UR - http://www.scopus.com/inward/record.url?scp=84962853190&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962853190&partnerID=8YFLogxK
U2 - 10.1109/VAST.2015.7347631
DO - 10.1109/VAST.2015.7347631
M3 - Conference contribution
AN - SCOPUS:84962853190
T3 - 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
SP - 57
EP - 64
BT - 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
A2 - Chen, Min
A2 - Andrienko, Gennady
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 October 2015 through 30 October 2015
ER -