Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study.

ABSTRACT:

Background

Traditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this.

Objective

The primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify t

SUBMITTER: Khademi Habibabadi S 

PROVIDER: S-EPMC9247809 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature