JMIR Publications

ABSTRACT

Background:

The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation poses a major barrier to achieving herd immunity.

Objective:

We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout.

Methods:

We applied a weakly-supervised aspect-based sentiment analysis (ABSA) technique on COVID-19 vaccination-related tweets in Canada. Automatically-generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiment toward “vaccination” changed over time. In addition, we analyzed the most retweeted/liked tweets by observing most frequent nouns and sentiments toward key aspects.

Results:

After training tweets using an ABSA system, we obtained 108 aspect terms (e.g., “immunity” and “pfizer”) and 6,793 opinion terms (e.g., “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying/editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for more analysis. The results showed that the top-ranked automatically-extracted aspects include “risk”, “delay”, and “hope”. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution”, “side effects”, “allergy”, “reactions” and “anti-vaxxer”, and positive sentiments related to “vaccine campaign”, “vaccine candidates”, and “immune response”. All these results indicate that the Twitter users express concerns about the safety of vaccines, but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of all the tweets, the most retweeted/liked tweets showed more positive sentiment overall, especially about vaccination itself. When looking more closely, the most retweeted/liked tweets showed an interesting dichotomy in Twitter users, i.e., the “anti-vaxxer” population who used a negative sentiment as a means to discourage vaccination and the “Covid Zero” population who used negative sentiments to encourage vaccinations while critiquing the public health response.

Conclusions:

This study is the first to examine public sentiments toward COVID-19 vaccination on tweets over an extended period of time in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination, and get closer to the goal of ending the pandemic.