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

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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.

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Author information

Authors and Affiliations

  1. Department of Emergency Medicine, NewYork-Presbyterian/Queens, Queens, NY, USA

    Michael Chary

  2. Department of Emergency Medicine, Mount Sinai Hospital, New York, NY, USA

    Nicholas Genes

  3. Department of Computer Science, Brigham Young University, Provo, UT, USA

    Christophe Giraud-Carrier

  4. Department of Health Science, Brigham Young University, Provo, UT, USA

    Carl Hanson

  5. Department of Emergency Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA

    Lewis S. Nelson

  6. Division of Medical Toxicology, The Icahn School of Medicine, New York, NY, USA

    Alex F. Manini

  7. Department of Emergency Medicine, Elmhurst Hospital Center, Queens, NY, USA

    Alex F. Manini

Authors
  1. Michael Chary
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  2. Nicholas Genes
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  3. Christophe Giraud-Carrier
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  4. Carl Hanson
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  5. Lewis S. Nelson
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  6. Alex F. Manini
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Correspondence to Michael Chary.

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Chary, M., Genes, N., Giraud-Carrier, C. et al. Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media. J. Med. Toxicol. 13, 278–286 (2017). https://doi.org/10.1007/s13181-017-0625-5

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  • DOI: https://doi.org/10.1007/s13181-017-0625-5

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