DrugTracker

Abstract

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. CCS CONCEPTS• Information Systems → Data Mining; • Collaborative and social computing → Social networks.

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

“…Most of the other works concentrated on analyzing drug use/abuse trends [4][5][6][7][8][9][10][11]. Others conducted research on monitoring social media such as Youtube [12], Twitter [13][14][15], and Instagram [16]. Although those efforts have positively contributed toward critical aspects of combating real-time drug use/abuse issues, they exhibited some crucial challenges.…”