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Title: TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media

Abstract

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; however, 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 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.

Authors:
 [1];  [1];  [1];  [2];  [2];  [2];  [2]; ORCiD logo [3];  [4];  [5];  [1]
  1. Korea University
  2. Uncharted Software Inc
  3. {Ramki} [ORNL
  4. Georgia Tech Research Institute (GTRI), Atlanta, GA
  5. Georgia Institute of Technology, Atlanta
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1470894
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Conference on Human Factors in Computing Systems (CHI 2018) - Montreal, , Canada - 4/21/2018 4:00:00 PM-4/26/2018 4:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Choi, Minsuk, Shin, Sungbok, Choi, Jinho, Langevin, Scott, Bethune, Christopher, Horne, Philippe, Kronenfeld, Nathan, Kannan, Ramakrishnan, Drake, Barry, Park, Haesun, and CHOO, JAEGUL. TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media. United States: N. p., 2018. Web. doi:10.1145/3173574.3174157.
Choi, Minsuk, Shin, Sungbok, Choi, Jinho, Langevin, Scott, Bethune, Christopher, Horne, Philippe, Kronenfeld, Nathan, Kannan, Ramakrishnan, Drake, Barry, Park, Haesun, & CHOO, JAEGUL. TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media. United States. doi:10.1145/3173574.3174157.
Choi, Minsuk, Shin, Sungbok, Choi, Jinho, Langevin, Scott, Bethune, Christopher, Horne, Philippe, Kronenfeld, Nathan, Kannan, Ramakrishnan, Drake, Barry, Park, Haesun, and CHOO, JAEGUL. Sun . "TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media". United States. doi:10.1145/3173574.3174157. https://www.osti.gov/servlets/purl/1470894.
@article{osti_1470894,
title = {TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media},
author = {Choi, Minsuk and Shin, Sungbok and Choi, Jinho and Langevin, Scott and Bethune, Christopher and Horne, Philippe and Kronenfeld, Nathan and Kannan, Ramakrishnan and Drake, Barry and Park, Haesun and CHOO, JAEGUL},
abstractNote = {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; however, 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 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.},
doi = {10.1145/3173574.3174157},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {4}
}

Conference:
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