Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Abstract

Background Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research. Objective This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017. Methods Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs. Results The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03). Conclusions Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.

Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action

“…27 In the context of internet surveillance, Anwar et al sought to determine if opioid-related Twitter posts could have been used to identify the three waves of the opioid crisis in North Carolina from 2009 to 2017. 34 They used a natural language processing (NLP) approach to identify opioid-related tweets. Their findings indicate that the annual number of heroin-and synthetic opioid-related Twitter posts were associated with annual heroin-and synthetic opioid-overdose death rates, respectively.…”

“…Their findings indicate that the annual number of heroin-and synthetic opioid-related Twitter posts were associated with annual heroin-and synthetic opioid-overdose death rates, respectively. 34 Chary et al used NLP (semantic distance specifically) to identify Tweets related to the non-medical use of prescription opioids (NMPO) from 2012 to 2014 and showed these Table I. Papers that met study inclusion criteria.…”

Adolescent Health on Social Media and the Mentorship of Youth Investigators: Five Content Analysis Studies Conducted by Youth Investigators

“…Despite the large number of adolescents who misuse opioids [ 36 ], little is known about how social media influences adolescents’ use or misuse of opiates. One study found an association between a participant tweeting about opioids and offline opioid overdoses [ 39 ]. Furthermore, previous research has shown that engagement with alcohol-related and e-cigarette–related social media is associated with more offline use of these substances [ 40 , 41 ].…”

Exploring the Association Between the “Big Five” Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis

“…Specifically, we provide a more nuanced understanding of personality's influence on fatal opioid overdose through the five distinct dimensions of the five-factor personality trait model. To do so, we build on a burgeoning stream of health care informatics, which establishes social media posts on the topic of opioid substances as a timely indicator of opioid overdose mortality [24,53], by using a combination of advanced computational techniques (cloud computing and text mining) and robust econometric analysis to expand the scope of user-generated content relevant to infoveillance beyond posts directly mentioning opioids. Our study also has several practical implications for health care providers and administrators, as its findings can be applied in opioid overdose prevention and surveillance based on the local counties' prevalent personality traits.…”