Sequence Labelling with 2 Level Segregation (SL2LS): A framework to extract COVID-19 vaccine adverse drug reactions from Twitter data

Abstract

Social media data is an opportunistic resource for post-marketing surveillance of COVID-19 (Coronavirus disease of 2019) vaccines. However, the usage of the term Adverse Drug Reaction (ADR) in different contexts, which do not refer to adverse reactions to given vaccines, poses a great challenge for existing ADR extraction techniques. In the present work, a framework titled “Sequence Labelling with 2 Level Segregation” (SL2LS) has been proposed to extract adverse reactions of COVID-19 vaccines from Twitter data. It handles the mentioned challenge by deploying a Bidirectional Long Short-Term Memory (Bi-LSTM) based two-level classification model to segregate the tweets containing ADR terms that represent adverse reactions of vaccines from the tweets that comprise ADR terms which are not referring to adverse reactions of vaccines based on the context of ADR term usage and then employ domain-specific pre-trained Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) sequence labelling model for extracting vaccine linked ADRs from segregated tweets. The proposed framework has been applied to Covaxin and Covishield COVID-19 vaccine data collected from Twitter and annotated with the help of a domain expert. It achieved the highest precision, recall and F1-score of 95.46%, 93.2% and 94.33%, respectively, in comparison to other state-of-the-art techniques. The validation of detected top 20 frequent ADRs of the two vaccines with official surveillance reports resulted in 19 verified ADRs for Covaxin and all 20 verified ADRs for Covishield and one unverified ADR “Blood Clot” for Covaxin which is a valuable lead for medical experts for further exploration. Further analysis shows that the two vaccines share some common ADRs viz. “Discomfort”, “Fever”, “Pain”, “Headache” and “Fatigue” and “Discomfort” and “Fever” are the most frequent ADRs associated with Covaxin and Covishield vaccines, respectively.