TY - GEN
T1 - TopicOnTiles
T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018
AU - Choi, Minsuk
AU - Shin, Sungbok
AU - Choi, Jinho
AU - Langevin, Scott
AU - Bethune, Christopher
AU - Horne, Philippe
AU - Kronenfeld, Nathan
AU - Kannan, Ramakrishnan
AU - Drake, Barry
AU - Park, Haesun
AU - Choo, Jaegul
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018/4/20
Y1 - 2018/4/20
N2 - Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task, but the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal the information relevant to anomalous events in a multi-level tile-based map interface by using social media data. To this end, we adopt and improve a recently proposed topic modeling method that can extract spatio-temporally exclusive topics corresponding to a particular region and a time point. Furthermore, we utilize a tile-based map interface to efficiently handle large-scale data in parallel. Our user interface effectively highlights anomalous tiles using our novel glyph visualization that encodes the degree of anomaly computed by our exclusive topic modeling processes. To show the effectiveness of our system, we present several usage scenarios using real-world datasets as well as comprehensive user study results.
AB - Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task, but the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal the information relevant to anomalous events in a multi-level tile-based map interface by using social media data. To this end, we adopt and improve a recently proposed topic modeling method that can extract spatio-temporally exclusive topics corresponding to a particular region and a time point. Furthermore, we utilize a tile-based map interface to efficiently handle large-scale data in parallel. Our user interface effectively highlights anomalous tiles using our novel glyph visualization that encodes the degree of anomaly computed by our exclusive topic modeling processes. To show the effectiveness of our system, we present several usage scenarios using real-world datasets as well as comprehensive user study results.
KW - Anomalous event detection
KW - Social media
KW - Spatio-temporal data analysis
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85046957095&partnerID=8YFLogxK
U2 - 10.1145/3173574.3174157
DO - 10.1145/3173574.3174157
M3 - Conference contribution
AN - SCOPUS:85046957095
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 21 April 2018 through 26 April 2018
ER -