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Vaccine safety surveillance using social media data

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thesis
posted on 22.06.2021, 00:56 authored by SEDIGHEH KHADEMI HABIBABADI
This research answers the question of how social media surveillance can assist with detection of vaccine safety signals, and what effective techniques can be utilized for identifying posts containing Vaccine Adverse Event Mentions (VAEM). The research has developed a VAEM-Mine method that combines two stages of topic modelling with classification to extract around 90% of all VAEM posts from a twitter stream, with a high degree of confidence. This is a significant achievement, as VAEM posts constitute less than 2% of all vaccine-related twitter posts and obtaining them is foundational to detection of vaccine safety signals.

History

Principal supervisor

Pari Delirhaghighi

Additional supervisor 1

Prof. Frada Burstein

Additional supervisor 2

Prof. Jim Buttery

Year of Award

2021

Department, School or Centre

Caulfield School of IT

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Information Technology