title: Phonetic spelling filter for keyword selection in drug mention mining from social media.
abstract: Social media postings are rich in information that often remain hidden and inaccessible for automatic extraction due to inherent limitations of the site's APIs, which mostly limit access via specific keyword-based searches (and limit both the number of keywords and the number of postings that are returned). When mining social media for drug mentions, one of the first problems to solve is how to derive a list of variants of the drug name (common misspellings) that can capture a sufficient number of postings. We present here an approach that filters the potential variants based on the intuition that, faced with the task of writing an unfamiliar, complex word (the drug name), users will tend to revert to phonetic spelling, and we thus give preference to variants that reflect the phonemes of the correct spelling. The algorithm allowed us to capture 50.4 - 56.0 % of the user comments using only about 18% of the variants.
authors.affiliation: Arizona State University, Department of Computer Science, Tempe, AZ.
authors.forename: Pranoti
authors.initials: P
authors.lastname: Pimpalkhute
authors.affiliation: Arizona State University, Department of Computer Science, Tempe, AZ.
authors.forename: Apurv
authors.initials: A
authors.lastname: Patki
authors.affiliation: Arizona State University, Department of BioMedical Informatics, Scottsdale, AZ.
authors.forename: Azadeh
authors.initials: A
authors.lastname: Nikfarjam
authors.affiliation: Arizona State University, Department of BioMedical Informatics, Scottsdale, AZ.
authors.forename: Graciela
authors.initials: G
authors.lastname: Gonzalez
keywords: Information Retrieval; Natural Language Processing and Free Text Data Mining; Spelling-Error