TY - GEN
T1 - Drugtracker
T2 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
AU - Hu, Han
AU - Phan, Nhat Hai
AU - Ye, Xinyue
AU - Jin, Ruoming
AU - Ding, Kele
AU - Dou, Dejing
AU - Vo, Huy T.
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/11/5
Y1 - 2019/11/5
N2 - In this paper, we present a community-focused drug abuse monitoring and supporting system, called DrugTracker, that utilizes social media and geospatial data in near real-time. Through the system, users can: (1) Detect drug abuse risk behaviors from social media platforms, e.g., Twitter; (2) Analyze drug abuse risk behaviors by querying consolidated and live datasets with keywords, spatial entities, and time constraints; and (3) Explore the query results and associated data through a web-based user interface in thematic choropleth, heatmap, and statistical charts. To protect the privacy of the Twitter users, whose data is collected, the system automatically hides the re-identification elements in tweets and aggregates the geo-tags into areas such as census tracts. For the demonstration purpose, our DrugTracker system is populated with a database that contains about 10 million tweets from the year 2017, that were annotated as drug abuse risk behavior positive by our deep learning model.
AB - In this paper, we present a community-focused drug abuse monitoring and supporting system, called DrugTracker, that utilizes social media and geospatial data in near real-time. Through the system, users can: (1) Detect drug abuse risk behaviors from social media platforms, e.g., Twitter; (2) Analyze drug abuse risk behaviors by querying consolidated and live datasets with keywords, spatial entities, and time constraints; and (3) Explore the query results and associated data through a web-based user interface in thematic choropleth, heatmap, and statistical charts. To protect the privacy of the Twitter users, whose data is collected, the system automatically hides the re-identification elements in tweets and aggregates the geo-tags into areas such as census tracts. For the demonstration purpose, our DrugTracker system is populated with a database that contains about 10 million tweets from the year 2017, that were annotated as drug abuse risk behavior positive by our deep learning model.
KW - Deep learning
KW - Drug abuse
KW - Social media
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85076966800&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076966800&partnerID=8YFLogxK
U2 - 10.1145/3347146.3359076
DO - 10.1145/3347146.3359076
M3 - Conference contribution
AN - SCOPUS:85076966800
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 564
EP - 567
BT - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
A2 - Banaei-Kashani, Farnoush
A2 - Trajcevski, Goce
A2 - Guting, Ralf Hartmut
A2 - Kulik, Lars
A2 - Newsam, Shawn
PB - Association for Computing Machinery
Y2 - 5 November 2019 through 8 November 2019
ER -