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Novel psychoactive substances: An investigation of temporal trends in social media and electronic health records

Published online by Cambridge University Press:  23 March 2020

A. Kolliakou*
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
M. Ball
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
L. Derczynski
Department of Computer Science, University of Sheffield, Sheffield, UK
D. Chandran
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
G. Gkotsis
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
P. Deluca
National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
R. Jackson
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
H. Shetty
NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
R. Stewart
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
*Corresponding author. Biomedical Research Centre Nucleus, PO92, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK. Tel.: +00 44 0 20 32 28 85 61. E-mail Kolliakou).
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Public health monitoring is commonly undertaken in social media but has never been combined with data analysis from electronic health records. This study aimed to investigate the relationship between the emergence of novel psychoactive substances (NPS) in social media and their appearance in a large mental health database.


Insufficient numbers of mentions of other NPS in case records meant that the study focused on mephedrone. Data were extracted on the number of mephedrone (i) references in the clinical record at the South London and Maudsley NHS Trust, London, UK, (ii) mentions in Twitter, (iii) related searches in Google and (iv) visits in Wikipedia. The characteristics of current mephedrone users in the clinical record were also established.


Increased activity related to mephedrone searches in Google and visits in Wikipedia preceded a peak in mephedrone-related references in the clinical record followed by a spike in the other 3 data sources in early 2010, when mephedrone was assigned a ‘class B’ status. Features of current mephedrone users widely matched those from community studies.


Combined analysis of information from social media and data from mental health records may assist public health and clinical surveillance for certain substance-related events of interest. There exists potential for early warning systems for health-care practitioners.

Original article
Copyright © Elsevier Masson SAS 2016

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Myslin, MZhu, SHChapman, WConway, MUsing Twitter to examine smoking behavior and perceptions of emerging tobacco products. J Med Internet Res. 15 2013 e174 ScholarPubMed
Struik, LLBaskerville, NBThe role of Facebook in Crush the Crave, a mobile- and social media-based smoking cessation intervention: qualitative framework analysis of posts. J Med Internet Res. 2014;16(7):e170 ScholarPubMed
Hanson, CLCannon, BBurton, SGiraud-Carrier, CAn exploration of social circles and prescription drug abuse through Twitter. J Med Internet Res. 2013;15(9):e189 ScholarPubMed
Nakhasi, RJPassarella, SGBell, MJPaul, MDredze, PJProvost, Malpractice and Malcontent: Analysing Medical Complaints in Twitter, AAAI Technical Report. Information Retrieval and Knowledge Discovery in Biomedical Text.2012 Johns Hopkins UniversityGoogle Scholar
Young, SDJaganath, DOnline social networking for HIV education and prevention: a mixed-methods analysis. Sex Transm Dis. 2013;40(2): 162167CrossRefGoogle ScholarPubMed
Sarker, AGinn, RNikfarjam, AO’Connor, KSmith, KJayaraman, Set al.Utilizing social media data for pharmacovigilance: A review. J Biomed Inform. 2015;54:202212CrossRefGoogle ScholarPubMed
McIver, DJBrownstein, JSWikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol. 2014;10(4):e1003581 ScholarPubMed
Ohno-Machado, LFocusing on the patient: mHealth, social media, electronic health records, and decision support systems. J Am Med Inform Assoc. 2014;21(6): 953CrossRefGoogle ScholarPubMed
O’Reilly, TWhat is Web 2. 0 - Design patterns and business models for the next generation of software 2005 Scholar
Li, JCardie, CEarly stage influenza detection from Twitter 2013 [ar Xiv: 1309.7340v3]Google Scholar
EMCDDA, 2016 EU Drug Markets Report: In-depth Analysis. European Monitoring Centre for Drugs and Drug Addiction 2015 Scholar
Davey, ZSchifano, FCorazza, ODeluca, Pon behalf of the Psychonaut Web Mapping Group e-Psychonauts Conducting research in online drug forum communities. J Ment Health. 2012;21:386394CrossRefGoogle ScholarPubMed
Stewart, RSoremekun, MPerera, GBroadbent, MCallard, FDenis, Met al.The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry. 9 2009 51 ScholarPubMed
Chang, CKHayes, RBroadbent, MFernandes, ACLee, WHotopf, Met al.All-cause mortality among people with serious mental illness (SMI), substance use disorders, and depressive disorders in southeast London: a cohort study. BMC Psychiatry. 10 2010 77 ScholarPubMed
Hayes, RDChang, CKFernandes, ABroadbent, MLee, WHotopf, Met al.Associations between substance use disorder sub-groups, life expectancy and all-cause mortality in a large British specialist mental healthcare service. Drug Alcohol Depend. 2011;118:5661CrossRefGoogle Scholar
Hayes, RDChang, CKFernandes, ACBegum, ATo, DBroadbent, Met al.Functional status and all-cause mortality in serious mental illness. PLoS One. 7 2012 e44613 ScholarPubMed
Wu, CYChang, CKHayes, RDBroadbent, MHotopf, MStewart, Ret al.Clinical risk assessment rating and all-cause mortality in secondary mental healthcare: the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) Case Register. Psychol Med. 2012;42:15811590CrossRefGoogle ScholarPubMed
Wu, CYChang, CKRobson, DChen, S.-J.Hayes, RDStewart, REvaluation of smoking status identification using electronic health records and open-text information in a large mental health case register. PLoS One. 8 2013 e74262 Scholar
Patel, RJayatilleke, NJackson, RStewart, RMcguire, PInvestigation of negative symptoms in schizophrenia with a machine learning text-mining approach. Lancet. 383 2014 S16 Scholar
Fernandes, ACCloete, DBroadbent, MTMHayes, RDChang, C.-K.Roberts, Aet al.Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records. BMC Med Inform Decis Mak. 13 2013 71CrossRefGoogle ScholarPubMed
Winstock, ARMitcheson, LRDeluca, PDavey, ZCorazza, OSchifano, SMephedrone, new kid for the chop?. Addiction. 2010;106:154161CrossRefGoogle ScholarPubMed
Statista, Number of monthly active Twitter users worldwide from 1st quarter 2010 to 2nd quarter 2015 (in millions). The Statistics Portal 2015 Scholar
Twitter, New Tweets per second record, and how! Twitter Engineering 2013 Scholar
Kergl, DRoedler, RSeeber, SOn the endogenesis of Twitter’s Spritzer. Gardenhose sample streams. Proc Adv Soc Networks Analysis Mining. 2014;1:357364 Scholar
Aho, AVCorasick, MJEfficient string matching: an aid to bibliographic search. Commun ACM. 1975;18(6): 333340CrossRefGoogle Scholar
Cunningham, HMaynard, DBontcheva, Kon behalf of the GATE group Text processing with GATE (Version 6).2011 University of SheffieldGoogle Scholar
Bontcheva, KDerczynski, LFunk, AGreenwood, MAMaynard, DAswani, Net al.An Open-Source Information Extraction Pipeline for Microblog Text. Proceedings of the conference on Recent Advances in Natural Language Processing. 2013Google Scholar
Google, Insights into what the world is searching for–the new Google Trends, Google Inc. 2012Google Scholar
Jackson, RBall, MPatel, RHayes, RDDobson, RJBStewart, RTextHunter—a user friendly tool for extracting generic concepts from free text in clinical research. Proc Am Med Informatics Assoc. 2014;1:729738 Scholar
Statacorp, Stata Statistical Software: Release 13. College Station, TX: StataCorp LP; 2011Google Scholar
Wood, DMHunter, LMeasham, FDargan, PILimited use of novel psychoactive substances in South London nightclubs. Q J Med. 2012;105:959964CrossRefGoogle ScholarPubMed
Kelly, BCWells, BEPawson, Leclair, AParsons, JTGolub, SANovel psychoactive drug use among younger adults involved in US nightlife scene. Drug Alcohol Rev. 2013;32:588593CrossRefGoogle Scholar
Johnson, PSJohnson, MWInvestigation of “Bath Salts” use patterns within an online sample of users in the United States. J Psychoactive Drugs. 2014;46(5): 369378CrossRefGoogle ScholarPubMed
Global Drug Survey. The Global Drug Survey 2015 findings.2015 Scholar
Home, Office, Drug misuse declared finding from the 2011 to 2012 Crime Survey for England and Wales (CSEW).2nd ed. 2012 Home Office London Scholar
Measham, DWood, DMDargan, PIMoore, KThe rise in legal highs: prevalence and patterns in the use of illegal drugs and first- and second-generation “legal highs” in South London gay dance clubs. J Subst Use. 2011;16(4): 263272CrossRefGoogle Scholar
Measham, FMoore, KØstergaard, JEmerging Drug Trends in Lancashire: Night Time Economy Surveys. Phase One Report.2011 Lancaster University/LDAATGoogle Scholar
Carhart-Harris, RLKing, LANutt, DJA web-based survey on mephedrone. Drug Alcohol Depend. 2011;118(1): 1922CrossRefGoogle ScholarPubMed
Winstock, AMitcheson, LRamsey, JDavies, SPuchnarewicz, MMarsden, JMephedrone: use, subjective effects and health risks. Addiction. 2011;106(11): 19911996CrossRefGoogle ScholarPubMed
Lally, JHigaya, E.-E.Nisar, ZBainbridge, EHallahan, BPrevalence study of head shop drug usage in mental health services. Psychiatrist Online. 2013;37:4448CrossRefGoogle Scholar
Martinotti, GLupi, MAcciavatti, TCinosi, ESantacroce, RSignorelli, MSet al.Novel psychoactive substances in young adults with and without psychiatric comorbidities. Biomed Res Intern. 2014;1:17Google Scholar
Baca-Garcia, EPerez-Rodriguez, MMBasurte-Villamor, IFernandez del Moral, ALJimenez-Arriero, MAGonzalez de Rivera, JLet al.Diagnostic stability of psychiatric disorders in clinical practice. Br J Psychiatry. 2007;190:210216CrossRefGoogle ScholarPubMed
Measham, FMoore, KNewcombe, RWelch, ZTweaking, bombing, dabbing and stockpiling: the emergence of mephedrone and the perversity of prohibition. Drugs Alcohol Today. 2010;10(1): 1421CrossRefGoogle Scholar
Duh, MSMonitoring online signals: implications of internet data in pharmacovigilance.2014 Health Care Bulletin Scholar
Eysenbach, GInfodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyse search, communication and publication behaviour on the Internet. J Med Internet Res. 2009;11(1):e11 Scholar
Murphy, JKim, AHagood, HRichards, AAugustine, CKroutil, Let al.Twitter feeds and Google search query surveillance: can they supplement survey data collection?.2011 Sixth International Conference of the Association for Survey Computing Bristol UK Scholar
Laurent, MRVickers, TJSeeking health information online: does Wikipedia matter?. J Am Med Inform Assoc. 2009;16:471479CrossRefGoogle ScholarPubMed
Bruns, AStieglitz, STwitter data: what do they represent?. Inf Technol. 2014;56(5): 240245Google Scholar
Duggan, MEllison, NBLampe, CLenhart, AMadden, MDemographics of key social networking platforms. 2015 Pew Research Center Scholar
Littlejohn, CBaldacchino, ASchifano, FDeluca, PInternet pharmacies and online prescription drug sales: a cross-sectional study. Drugs Educ Prev Policy. 2005;12:7580CrossRefGoogle Scholar
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