Published on in Vol 15, No 9 (2013): September

An Exploration of Social Circles and Prescription Drug Abuse Through Twitter

An Exploration of Social Circles and Prescription Drug Abuse Through Twitter

An Exploration of Social Circles and Prescription Drug Abuse Through Twitter

Journals

  1. Ranney M, Chang B, Freeman J, Norris B, Silverberg M, Choo E, Mycyk M. Tweet Now, See You In the ED Later? Examining the Association Between Alcohol‐related Tweets and Emergency Care Visits. Academic Emergency Medicine 2016;23(7):831 View
  2. Lardon J, Abdellaoui R, Bellet F, Asfari H, Souvignet J, Texier N, Jaulent M, Beyens M, Burgun A, Bousquet C. Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review. Journal of Medical Internet Research 2015;17(7):e171 View
  3. Kim S, Marsch L, Hancock J, Das A. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353 View
  4. Salimian P, Chunara R, Weitzman E. Averting the Perfect Storm: Addressing Youth Substance Use Risk From Social Media Use. Pediatric Annals 2014;43(10) View
  5. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  6. Kelley D, Brown M, Murray A, Blake K. Prevalence and Characteristics of Twitter Posts About Court-Ordered, Tobacco-Related Corrective Statements: Descriptive Content Analysis. JMIR Public Health and Surveillance 2019;5(4):e12878 View
  7. Huesch M, Chetlen A, Segel J, Schetter S. Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study. Journal of Medical Internet Research 2017;19(6):e201 View
  8. Suarez-Lledo V, Alvarez-Galvez J. Prevalence of Health Misinformation on Social Media: Systematic Review. Journal of Medical Internet Research 2021;23(1):e17187 View
  9. Sarker A, DeRoos A, Perrone J. Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework. Journal of the American Medical Informatics Association 2020;27(2):315 View
  10. Li J, Xu Q, Shah N, Mackey T. A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study. Journal of Medical Internet Research 2019;21(6):e13803 View
  11. Visweswaran S, Colditz J, O’Halloran P, Han N, Taneja S, Welling J, Chu K, Sidani J, Primack B. Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study. Journal of Medical Internet Research 2020;22(8):e17478 View
  12. Cavazos-Rehg P, Krauss M, Grucza R, Bierut L. Characterizing the Followers and Tweets of a Marijuana-Focused Twitter Handle. Journal of Medical Internet Research 2014;16(6):e157 View
  13. R. Scott K, Nelson L, Meisel Z, Perrone J. Opportunities for Exploring and Reducing Prescription Drug Abuse Through Social Media. Journal of Addictive Diseases 2015;34(2-3):178 View
  14. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  15. Kresina T, Lubran R, Clark H. Technology-Assisted Addiction Treatment for Key Populations. Psychology 2014;05(09):1044 View
  16. Katsuki T, Mackey T, Cuomo R. Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data. Journal of Medical Internet Research 2015;17(12):e280 View
  17. Kalyanam J, Katsuki T, R.G. Lanckriet G, Mackey T. Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning. Addictive Behaviors 2017;65:289 View
  18. Zhang N, Campo S, Janz K, Eckler P, Yang J, Snetselaar L, Signorini A. Electronic Word of Mouth on Twitter About Physical Activity in the United States: Exploratory Infodemiology Study. Journal of Medical Internet Research 2013;15(11):e261 View
  19. Guerra C, Mackey T. USA Criminal and Civil Prosecutions Associated with Illicit Online Pharmacies: Legal Analysis and Global Implications. Medicine Access @ Point of Care 2017;1:maapoc.0000020 View
  20. Cherian R, Westbrook M, Ramo D, Sarkar U. Representations of Codeine Misuse on Instagram: Content Analysis. JMIR Public Health and Surveillance 2018;4(1):e22 View
  21. Sarker A, Chandrashekar P, Magge A, Cai H, Klein A, Gonzalez G. Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis. Journal of Medical Internet Research 2017;19(10):e361 View
  22. Nicholson J, Marcum C, Higgins G. Predictors of Prescription Drug Misuse among High School Students in the United States. Deviant Behavior 2022;43(1):91 View
  23. Yin Z, Fabbri D, Rosenbloom S, Malin B. A Scalable Framework to Detect Personal Health Mentions on Twitter. Journal of Medical Internet Research 2015;17(6):e138 View
  24. Arseniev-Koehler A, Lee H, McCormick T, Moreno M. #Proana: Pro-Eating Disorder Socialization on Twitter. Journal of Adolescent Health 2016;58(6):659 View
  25. Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, Upadhaya T, Gonzalez G. Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics 2015;54:202 View
  26. Murthy D, Eldredge M. Who tweets about cancer? An analysis of cancer-related tweets in the USA. DIGITAL HEALTH 2016;2:205520761665767 View
  27. Kalyanam J, Mackey T. A Review of Digital Surveillance Methods and Approaches to Combat Prescription Drug Abuse. Current Addiction Reports 2017;4(4):397 View
  28. Finfgeld-Connett D. Twitter and Health Science Research. Western Journal of Nursing Research 2015;37(10):1269 View
  29. Lardon J, Bellet F, Aboukhamis R, Asfari H, Souvignet J, Jaulent M, Beyens M, Lillo-LeLouët A, Bousquet C. Evaluating Twitter as a complementary data source for pharmacovigilance. Expert Opinion on Drug Safety 2018;17(8):763 View
  30. Thompson L, Rivara F, Whitehill J. Prevalence of Marijuana-Related Traffic on Twitter, 2012–2013: A Content Analysis. Cyberpsychology, Behavior, and Social Networking 2015;18(6):311 View
  31. Shutler L, Nelson L, Portelli I, Blachford C, Perrone J. Drug Use in the Twittersphere: A Qualitative Contextual Analysis of Tweets About Prescription Drugs. Journal of Addictive Diseases 2015;34(4):303 View
  32. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  33. Braithwaite S, Giraud-Carrier C, West J, Barnes M, Hanson C. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality. JMIR Mental Health 2016;3(2):e21 View
  34. Mackey T, Kalyanam J, Katsuki T, Lanckriet G. Twitter-Based Detection of Illegal Online Sale of Prescription Opioid. American Journal of Public Health 2017;107(12):1910 View
  35. Kim M, Kim J, Kim S, Jeong J. Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study. Journal of Medical Internet Research 2020;22(2):e16466 View
  36. O'Connor K, Sarker A, Perrone J, Gonzalez Hernandez G. Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines. Journal of Medical Internet Research 2020;22(2):e15861 View
  37. Kolliakou A, Ball M, Derczynski L, Chandran D, Gkotsis G, Deluca P, Jackson R, Shetty H, Stewart R. Novel psychoactive substances: An investigation of temporal trends in social media and electronic health records. European Psychiatry 2016;38:15 View
  38. Logghe H, Selby L, Boeck M, Stamp N, Chuen J, Jones C. The academic tweet: Twitter as a tool to advance academic surgery. Journal of Surgical Research 2018;226:viii View
  39. Hoffman B, Rosenthal E, Colditz J, Mcgarry R, Primack B. Use of Twitter to Assess Viewer Reactions to the Medical Drama, Code Black. Journal of Health Communication 2018;23(3):244 View
  40. Kazemi D, Borsari B, Levine M, Dooley B. Systematic review of surveillance by social media platforms for illicit drug use. Journal of Public Health 2017;39(4):763 View
  41. McIver D, Hawkins J, Chunara R, Chatterjee A, Bhandari A, Fitzgerald T, Jain S, Brownstein J. Characterizing Sleep Issues Using Twitter. Journal of Medical Internet Research 2015;17(6):e140 View
  42. Liu J, Ho M, Lu L, Xiao G. Recent Themes in Social Networking Service Research. PLOS ONE 2017;12(1):e0170293 View
  43. Al-Garadi M, Yang Y, Cai H, Ruan Y, O’Connor K, Graciela G, Perrone J, Sarker A. Text classification models for the automatic detection of nonmedical prescription medication use from social media. BMC Medical Informatics and Decision Making 2021;21(1) View
  44. Wongkoblap A, Vadillo M, Curcin V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research 2017;19(6):e228 View
  45. Kayser L, Kushniruk A, Osborne R, Norgaard O, Turner P. Enhancing the Effectiveness of Consumer-Focused Health Information Technology Systems Through eHealth Literacy: A Framework for Understanding Users' Needs. JMIR Human Factors 2015;2(1):e9 View
  46. Gachloo M, Wang Y, Xia J. A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition. Genomics & Informatics 2019;17(2):e18 View
  47. Sarker A, O’Connor K, Ginn R, Scotch M, Smith K, Malone D, Gonzalez G. Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. Drug Safety 2016;39(3):231 View
  48. Barnes M, Hanson C, Giraud-Carrier C. The Case for Computational Health Science. Journal of Healthcare Informatics Research 2018;2(1-2):99 View
  49. Alvaro N, Conway M, Doan S, Lofi C, Overington J, Collier N. Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use. Journal of Biomedical Informatics 2015;58:280 View
  50. Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, Conway M. Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study. Journal of Medical Internet Research 2017;19(2):e48 View
  51. Zhao Y, Guo Y, He X, Wu Y, Yang X, Prosperi M, Jin Y, Bian J. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics Journal 2020;26(2):765 View
  52. Hu H, Phan N, Chun S, Geller J, Vo H, Ye X, Jin R, Ding K, Kenne D, Dou D. An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning. Computational Social Networks 2019;6(1) View
  53. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  54. Sznitman S. Analysing Twitter as an Opportunity to Understand Substance Use. SSRN Electronic Journal 2015 View
  55. Zhao F, Chen Y, Ge S, Yu X, Shao S, Black M, Wang Y, Zhang J, Song M, Wang W. A Quantitative Analysis of the Mass Media Coverage of Genomics Medicine in China: A Call for Science Journalism in the Developing World. OMICS: A Journal of Integrative Biology 2014;18(4):222 View
  56. Anderson L, Bell H, Gilbert M, Davidson J, Winter C, Barratt M, Win B, Painter J, Menone C, Sayegh J, Dasgupta N. Using Social Listening Data to Monitor Misuse and Nonmedical Use of Bupropion: A Content Analysis. JMIR Public Health and Surveillance 2017;3(1):e6 View
  57. Meng H, Kath S, Li D, Nguyen Q, Giraud-Carrier C. National substance use patterns on Twitter. PLOS ONE 2017;12(11):e0187691 View
  58. Simpson S, Adams N, Brugman C, Conners T. Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study. JMIR Public Health and Surveillance 2018;4(1):e2 View
  59. Hammond A, Paul M, Hobelmann J, Koratana A, Dredze M, Chisolm M. Perceived Attitudes About Substance Use in Anonymous Social Media Posts Near College Campuses: Observational Study. JMIR Mental Health 2018;5(3):e52 View
  60. Sequeira R, Gayen A, Ganguly N, Dandapat S, Chandra J. A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets. IEEE Transactions on Computational Social Systems 2019;6(6):1232 View
  61. Liang O, Chen Y, Bennett D, Yang C. Identifying Self-Management Support Needs for Pregnant Women With Opioid Misuse in Online Health Communities: Mixed Methods Analysis of Web Posts. Journal of Medical Internet Research 2021;23(2):e18296 View
  62. Nascimento T, DosSantos M, Danciu T, DeBoer M, van Holsbeeck H, Lucas S, Aiello C, Khatib L, Bender M, Zubieta J, DaSilva A. Real-Time Sharing and Expression of Migraine Headache Suffering on Twitter: A Cross-Sectional Infodemiology Study. Journal of Medical Internet Research 2014;16(4):e96 View
  63. Paul M, Chisolm M, Johnson M, Vandrey R, Dredze M. Assessing the Validity of Online Drug Forums as a Source for Estimating Demographic and Temporal Trends in Drug Use. Journal of Addiction Medicine 2016;10(5):324 View
  64. Lavertu A, Altman R. RedMed: Extending drug lexicons for social media applications. Journal of Biomedical Informatics 2019;99:103307 View
  65. Fogel J, Travis Y. Twitter use related to reality television characters: Association with increased marijuana use. Journal of Organizational Computing and Electronic Commerce 2017;27(2):152 View
  66. Mullins C, ffrench-O'Carroll R, Lane J, O'Connor T. Sharing the pain: an observational analysis of Twitter and pain in Ireland. Regional Anesthesia & Pain Medicine 2020;45(8):597 View
  67. Hamad E, Savundranayagam M, Holmes J, Kinsella E, Johnson A. Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds. Journal of Medical Internet Research 2016;18(3):e60 View
  68. Shaw G, Karami A. Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise. Proceedings of the Association for Information Science and Technology 2017;54(1):357 View
  69. Srisuma S, Bronstein A, Hoyte C. NBOMe and 2C substitute phenylethylamine exposures reported to the National Poison Data System. Clinical Toxicology 2015;53(7):624 View
  70. Karami A, Dahl A, Turner-McGrievy G, Kharrazi H, Shaw G. Characterizing diabetes, diet, exercise, and obesity comments on Twitter. International Journal of Information Management 2018;38(1):1 View
  71. Moore J, Barnett I, Boland M, Chen Y, Demiris G, Gonzalez-Hernandez G, Herman D, Himes B, Hubbard R, Kim D, Morris J, Mowery D, Ritchie M, Shen L, Urbanowicz R, Holmes J. Ideas for how informaticians can get involved with COVID-19 research. BioData Mining 2020;13(1) View
  72. Guirguis A, Moosa I, Gittins R, Schifano F. What About Drug Checking? Systematic Review and Netnographic Analysis of Social Media. Current Neuropharmacology 2020;18(10):906 View
  73. Haupt M, Cuomo R, Li J, Nali M, Mackey T. The influence of social media affordances on drug dealer posting behavior across multiple social networking sites (SNS). Computers in Human Behavior Reports 2022;8:100235 View
  74. Nova F, Coupe A, Mynatt E, Guha S, Pater J. Cultivating the Community. Proceedings of the ACM on Human-Computer Interaction 2022;6(GROUP):1 View
  75. Niu S, McKim K, Palm Reed K. Education, Personal Experiences, and Advocacy. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  76. Hakariya H, Ikejiri T, Yokoyama N, Saito Y. A Survey of Illegal Medication Trading through Twitter in Japan. YAKUGAKU ZASSHI 2022;142(8):901 View
  77. Liu X, Alsghaier H, Tong L, Ataullah A, McRoy S. Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches. JMIR AI 2022;1(1):e37751 View
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  79. Lavertu A, Hamamsy T, Altman R. Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis. Journal of Medical Internet Research 2021;23(10):e27714 View
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