title: Exploring brand-name drug mentions on Twitter for pharmacovigilance.
abstract: Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work we analyze the number and features of mentions on Twitter of drug brand names in order to explore the potential usefulness of the automated detection of drug side effects and drug-drug interactions on social media platforms such as Twitter. This information can be used for the development of predictive models for drug toxicity, drug-drug interactions or drug resistance. Taking into account the large number of drug brand mentions that we found on Twitter, it is promising as a tool for the detection, understanding and monitoring the way people manage prescribed drugs.
authors.affiliation: Research Programme on Biomedical Informatics (GRIB), IMIM-Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Pablo
authors.initials: P
authors.lastname: Carbonell
authors.affiliation: Research Programme on Biomedical Informatics (GRIB), IMIM-Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Mayer
authors.affiliation: Research Programme on Biomedical Informatics (GRIB), IMIM-Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Àlex
authors.initials: À
authors.lastname: Bravo
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D060735:Pharmacovigilance; D011159:Population Surveillance; D055553:Prescription Drugs / Q000145:classification*; D061108:Social Media / Q000706:statistics & numerical data*; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
chemical_list: D055553:Prescription Drugs