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Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis.
Jang, Hyeju; Rempel, Emily; Roe, Ian; Adu, Prince; Carenini, Giuseppe; Janjua, Naveed Zafar.
  • Jang H; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
  • Rempel E; British Columbia Centre for Disease Control, Vancouver, BC, Canada.
  • Roe I; British Columbia Centre for Disease Control, Vancouver, BC, Canada.
  • Adu P; British Columbia Centre for Disease Control, Vancouver, BC, Canada.
  • Carenini G; British Columbia Centre for Disease Control, Vancouver, BC, Canada.
  • Janjua NZ; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
J Med Internet Res ; 24(3): e35016, 2022 03 29.
Article in English | MEDLINE | ID: covidwho-1742137
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, which involves the human-in-the-loop system, 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 sentiments toward "vaccination" changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects.

RESULTS:

After applying the ABSA system, we obtained 170 aspect terms (eg, "immunity" and "pfizer") and 6775 opinion terms (eg, "trustworthy" for the positive sentiment and "jeopardize" for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. 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." 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 the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines the "anti-vaxxer" population that used negative sentiments as a means to discourage vaccination and the "Covid Zero" population that used negative sentiments to encourage vaccinations while critiquing the public health response.

CONCLUSIONS:

Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Topics: Vaccines Limits: Humans Country/Region as subject: North America Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 35016

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Topics: Vaccines Limits: Humans Country/Region as subject: North America Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 35016