# Enhancing opioid surveillance in RADOR-KY using social media

> **NIH NIH R01** · UNIVERSITY OF KENTUCKY · 2024 · $167,078

## Abstract

ABSTRACT:
The opioid epidemic continues to plague the United States. Unreliable and slow data systems
pose a continuing challenge to the public health response. There is a paucity of real-time data
that can be used to detect or forecast increases in opioid overdoses and coordinate timely
community resource mobilization efforts. Household surveys underestimate use and use
disorder rates, mortality data has a time lag that makes evaluating policy impact difficult, and
new illicit drugs are identified too slowly. Creating valid, sensitive, real-time data systems is thus
a critical priority. Kentucky has suffered greatly during the opioid epidemic. In response, the
Rapid Actionable Data for Opioid Response in Kentucky (RADOR-KY; 1-R01 DA057605-01)
system enhances surveillance capacity with machine learning models designed to forecast
future trends. The system currently uses data from 11 sources and integrates them for
forecasting county-level risk of opioid overdose. The proposed administrative supplement of
RADOR-KY will evaluate the added value of real-time social media data to improve opioid
overdose surveillance and forecasting. Recent studies have shown that social media can signal
opioid trends. Mentions of opioids on Reddit and language markers of distress on Twitter/X
correlate with regional opioid-related overdose deaths. It is unclear if these signals are captured
by current RADOR-KY data streams or if they provide independent information that could be
useful for improving performance of the system. Evaluating whether social media data can
improve the RADOR-KY prediction models is thus a major public health priority for Kentucky,
and to the extent it can be shown effective, could be applied nationwide. In this supplement, we
propose to work with Stanford University to (1) analyze the utility of counting direct social media
mentions of opioids for forecasting overdoses across Reddit and Twitter/X, (2) analyze the utility
of indirect population sentiment signals for forecasting overdoses, and (3) analyze the value of
considering additional social media context around opioid use (e.g., addiction vs. recovery). This
work will measure the potential value of social media data for opioid surveillance and estimate
its incremental value to RADOR-KY.

## Key facts

- **NIH application ID:** 11104947
- **Project number:** 3R01DA057605-01S3
- **Recipient organization:** UNIVERSITY OF KENTUCKY
- **Principal Investigator:** Svetla Stefanova Slavova
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $167,078
- **Award type:** 3
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11104947

## Citation

> US National Institutes of Health, RePORTER application 11104947, Enhancing opioid surveillance in RADOR-KY using social media (3R01DA057605-01S3). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/11104947. Licensed CC0.

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