Analysis of Twitter data for postmarketing surveillance in pharmacovigilance

Julie Pain, Jessie Levacher, Adam Quinqunel, Anja Belz

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs af- ter release for use by the general pop- ulation, but suffers from under-reporting and limited coverage. Automatic meth- ods for detecting drug effect reports, es- pecially for social media, could vastly in- crease the scope of PMS. Very few auto- matic PMS methods are currently avail- able, in particular for the messy text types encountered on Twitter. In this paper we describe first results for developing PMS methods specifically for tweets. We de- scribe the corpus of 125,669 tweets we have created and annotated to train and test the tools. We find that generic tools per- form well for tweet-level language iden- tification and tweet-level sentiment anal- ysis (both 0.94 F1-Score). For detection of effect mentions we are able to achieve 0.87 F1-Score, while effect-level adverse- vs.-beneficial analysis proves harder with an F1-Score of 0.64. Among other things, our results indicate that MetaMap seman- tic types provide a very promising ba- sis for identifying drug effect mentions in tweets.
Original languageEnglish
Title of host publicationProceedings of the 2nd Workshop on Noisy User-generated Text
Place of PublicationOsaka, Japan
Pages94-101
Number of pages8
Publication statusPublished - 1 Jan 2016
EventProceedings of the 2nd Workshop on Noisy User-generated Text - Osaka, Japan, 11 Dec 2016
Duration: 1 Jan 2016 → …

Conference

ConferenceProceedings of the 2nd Workshop on Noisy User-generated Text
Period1/01/16 → …

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    Pain, J., Levacher, J., Quinqunel, A., & Belz, A. (2016). Analysis of Twitter data for postmarketing surveillance in pharmacovigilance. In Proceedings of the 2nd Workshop on Noisy User-generated Text (pp. 94-101).