Pharmacovigilance on twitter? Mining tweets for adverse drug reactions

Karen O'Connor, Pranoti Pimpalkhute, Azadeh Nikfarjam, Rachel Ginn, Karen L. Smith, Graciela Gonzalez

    Research output: Contribution to journalArticlepeer-review

    102 Scopus citations


    Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied - with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sources of potential signals for adverse drug reactions (ADRs). We created an annotated corpus of 10,822 tweets. Each tweet was annotated for the presence or absence of ADR mentions, with the span and Unified Medical Language System (UMLS) concept ID noted for each ADR present. Using Cohen's kappa1, we calculated the inter-annotator agreement (IAA) for the binary annotations to be 0.69. To demonstrate the utility of the corpus, we attempted a lexicon-based approach for concept extraction, with promising success (54.1% precision, 62.1% recall, and 57.8% F-measure). A subset of the corpus is freely available at:

    Original languageEnglish (US)
    Pages (from-to)924-933
    Number of pages10
    JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
    StatePublished - 2014

    ASJC Scopus subject areas

    • Medicine(all)


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