Journal of Medical Toxicology

, Volume 9, Issue 2, pp 184–191 | Cite as

Leveraging Social Networks for Toxicovigilance

  • Michael Chary
  • Nicholas Genes
  • Andrew McKenzie
  • Alex F. Manini
Preliminary Research

Abstract

The landscape of drug abuse is shifting. Traditional means of characterizing these changes, such as national surveys or voluntary reporting by frontline clinicians, can miss changes in usage the emergence of novel drugs. Delays in detecting novel drug usage patterns make it difficult to evaluate public policy aimed at altering drug abuse. Increasingly, newer methods to inform frontline providers to recognize symptoms associated with novel drugs or methods of administration are needed. The growth of social networks may address this need. The objective of this manuscript is to introduce tools for using data from social networks to characterize drug abuse. We outline a structured approach to analyze social media in order to capture emerging trends in drug abuse by applying powerful methods from artificial intelligence, computational linguistics, graph theory, and agent-based modeling. First, we describe how to obtain data from social networks such as Twitter using publicly available automated programmatic interfaces. Then, we discuss how to use artificial intelligence techniques to extract content useful for purposes of toxicovigilance. This filtered content can be employed to generate real-time maps of drug usage across geographical regions. Beyond describing the real-time epidemiology of drug abuse, techniques from computational linguistics can uncover ways that drug discussions differ from other online conversations. Next, graph theory can elucidate the structure of networks discussing drug abuse, helping us learn what online interactions promote drug abuse and whether these interactions differ among drugs. Finally, agent-based modeling relates online interactions to psychological archetypes, providing a link between epidemiology and behavior. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These tools provide a powerful complement to existing methods of toxicovigilance.

Keywords

Epidemiology Internet Social networks Demographics Language 

References

  1. 1.
    RTI International. (2012). National survey on drug use and health: sample redesign issues and methodological studies. http://www.samhsa.gov/data/NSDUH/NSDUHMethodsSIMS2012.pdf. Accessed 5 Jan 2013
  2. 2.
    Sloboda Z (2002) Changing patterns of “drug abuse” in the United States: connecting findings from macro- and microepidemiologic studies. Subst Use Misuse 37(8–10):1229–1251PubMedCrossRefGoogle Scholar
  3. 3.
    Descotes J, Testud F (2005) Toxicovigilance: a new approach for the hazard identification and risk assessment of toxicants in human beings. Toxicol Appl Pharmacol 207(2 Suppl):599–603PubMedCrossRefGoogle Scholar
  4. 4.
    Eysenbach G (2009) Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the internet. J Med Internet Res 11(1):e11PubMedCrossRefGoogle Scholar
  5. 5.
    McNeil K, Brna PM, Gordon KE (2012) Epilepsy in the twitter era: a need to re-tweet the way we think about seizures. Epilepsy Behav: E&B 23(2):127–130CrossRefGoogle Scholar
  6. 6.
    Boyer EW, Wines JD Jr (2008) Impact of internet pharmacy regulation on opioid analgesic availability. J Stud Alcohol Drugs 69(5):703–708PubMedGoogle Scholar
  7. 7.
    Barratt MJ (2012) Silk road: eBay for drugs. Addiction 107(3):683PubMedCrossRefGoogle Scholar
  8. 8.
    Lange JE, Daniel J, Homer K, Reed MB, Clapp JD (2010) Salvia divinorum: effects and use among YouTube users. Drug Alcohol Depend 108(1–2):138–140PubMedCrossRefGoogle Scholar
  9. 9.
    Cavnar WB, and Trenkle JM (1994). N-gram based text categorization. Proc. of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, pp. 161–175Google Scholar
  10. 10.
    Ziv J, Lempel A (1977) A universal algorithm for sequential data compression. IEEE Trans Inf Theory 23(3):337–343CrossRefGoogle Scholar
  11. 11.
    Hu J, Gao J, Principe JC (2006) Analysis of biomedical signals by the Lempel–Ziv complexity: the effect of finite data size. IEEE Trans Biomed Eng 53(12 Pt 2):2606–2609PubMedCrossRefGoogle Scholar
  12. 12.
    Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  13. 13.
    Dumais S (2005) Latent semantic analysis. Annu Rev Inf Sci Technol 38:188CrossRefGoogle Scholar
  14. 14.
    Davis ME, Sigal R, Weyuker EJ (1994) Computability, complexity, and languages: fundamentals of theoretical computer science. Academic, BostonGoogle Scholar
  15. 15.
    Bondy J, Murty U (2008) Graph theory. Graduate texts in mathematics, 3rd edn. New York, SpringerGoogle Scholar
  16. 16.
    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442PubMedCrossRefGoogle Scholar
  17. 17.
    Cha M (2012) The world of connections and information flow in Twitter. IEEE Trans Syst Man Cybern 42(4):991–998CrossRefGoogle Scholar
  18. 18.
    Graham RL, Groetschel M, Lovasz L (eds) (1996) Handbook of combinatorics, vols 1 and 2. Elsevier, AmsterdamGoogle Scholar
  19. 19.
    Schrodinger E (1944). What is life? The physical aspect of the living cell. Dublin: e Dublin Institute for Advanced Studies at Trinity CollegeGoogle Scholar
  20. 20.
    Macklem PT (2008) Emergent phenomena and the secrets of life. J Appl Physiol 104(6):1844–1846PubMedCrossRefGoogle Scholar

Copyright information

© American College of Medical Toxicology 2013

Authors and Affiliations

  • Michael Chary
    • 1
  • Nicholas Genes
    • 2
  • Andrew McKenzie
    • 1
  • Alex F. Manini
    • 2
    • 3
  1. 1.Icahn School of Medicine at Mount SinaiNew YorkUSA
  2. 2.Department of Emergency MedicineIcahn School of Medicine at Mount SinaiNew YorkUSA
  3. 3.Division of Medical ToxicologyIcahn School of Medicine at Mount SinaiNew YorkUSA

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