Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

• Published in: Drug safety Vol. 39; no. 3; pp. 231 - 240
• Main Authors: Sarker, Abeed, O’Connor, Karen, Ginn, Rachel, Scotch, Matthew, Smith, Karen, Malone, Dan, Gonzalez, Graciela
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2016; Springer Nature B.V
• Subjects: Amphetamines; Annotations; Classification; Data collection; Data mining; Data Mining - methods; Data Mining - statistics & numerical data; Data Mining - trends; Drug abuse; Drug Safety and Pharmacovigilance; Epidemics; Humans; Medicine; Medicine & Public Health; Natural language processing; Noise; Original; Original Research Article; Oxycodone; Pharmacology/Toxicology; Prescription Drug Misuse - statistics & numerical data; Prescription Drug Misuse - trends; Prescription Drugs; Quetiapine Fumarate; Social Media - statistics & numerical data; Social Media - trends; Social networks; Spelling; Studies; USA; Metformin; Social Media Data; Oxycodone; Quetiapine; Adderall

Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our pr...