Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media

Abstract

Background: The misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month.

Methods: We wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013-2015 National Surveys on Drug Usage and Health (NSDUH).

Results: Tweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18-25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage.

Conclusions: Mentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.

Keywords: Computational linguistics; Epidemiology; Misuse; Natural language processing; Opioids; Social media.

Fig. 1

Study design. Data are collected from Twitter via Twitter’s Streaming API. Tweets having less than one character are excluded. Tweets are filtered into "signal" tweets (tweets of interest) if they have keywords; otherwise, into "basal activity". MUPO tweets are identified by clustering on SemD and validated by expert curation. A scaled version of the fraction of MUPO tweets in each state is compared with the NSDUH estimate for that same state

Fig. 2

Separation of tweets into semantic clusters. Each panel is the projection of the same 0.01% random sample of tweets projected onto the two principal components indicated by the panel’s axes. PC1 refers to principal component 1, PC2 principal component 2, PC3 principal component 3. Diagonal shows the distribution of values projected onto each principal component. Data from 2012

Fig. 3

Twenty most common words in MUPO and not-MUPO clusters in signal stream. X-axis shows the frequency of words in each category on a logarithmic scale. Same logarithmic scale for both panels. Twitter data from 2012

Fig. 4

Twenty most common words from basal activity stream. X-axis shows the frequency of words in each category on a logarithmic scale

Fig. 5

Scatter plot of estimates of MUPO from NSDUH and Twitter for 2012. Title of each panel indicates NSDUH age range. Open circles are estimates for each state scaled as indicated in “Methods” section. Solid line shows linear regression line

Fig. 6

Correlation between NSDUH and Twitter across age groups. Legend indicates NSDUH age groups. All correlation coefficients are significantly greater than 0