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. 2021 Jul 3;17(7):2026-2035.
doi: 10.1080/21645515.2020.1850072. Epub 2021 Jan 30.

Vaccine misinformation on social media - topic-based content and sentiment analysis of Polish vaccine-deniers' comments on Facebook

Krzysztof Klimiuk  1 , Agnieszka Czoska  2 , Karolina Biernacka  1 , Łukasz Balwicki  3
Affiliations

Vaccine misinformation on social media - topic-based content and sentiment analysis of Polish vaccine-deniers' comments on Facebook

Krzysztof Klimiuk et al. Hum Vaccin Immunother. .

Abstract

Introduction: Vaccinations are referred to as one of the greatest achievements of modern medicine. However, their effectiveness is also constantly denied by certain groups in society. This results in an ongoing dispute that has been gradually moving online in the last few years due to the development of technology. Our study aimed to utilize social media to identify and analyze vaccine-deniers' arguments against child vaccinations.Method: All public comments posted to a leading Polish vaccination opponents' Facebook page posted between 01/05/2019 and 31/07/2019 were collected and analyzed quantitatively in terms of their content according to the modified method developed by Kata (Kata, 2010). Sentiment analysis was also performed.Results: Out of 18,685 comments analyzed, 4,042 contained content covered by the adopted criteria: conspiracy theories (28.2%), misinformation and unreliable premises (19.9%), content related to the safety and effectiveness of vaccinations (14.0%), noncompliance with civil rights (13.2%), own experience (10.9%), morality, religion, and belief (8.5%), and alternative medicine (5.4%). There were also 1,223 pro-vaccine comments, of which 15.2% were offensive, mocking, or non-substantive. Sentiment analysis showed that comments without any arguments as well as those containing statements about alternative medicine or misinformation were more positive and less angry than comments in other topic categories.Conclusions: The large amount of content in the conspiracy theory and misinformation categories may indicate that authors of such comments may be characterized by a lack of trust in the scientific achievements of medicine. These findings should be adequately addressed in vaccination campaigns.

Keywords: Anti-vaccination movement; misinformation; sentiment analysis; social media; vaccination refusal.

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Figures

Figure 1.
Figure 1.
Procedures in building the database of gathered comments
Figure 2.
Figure 2.
Mean values for anger and happiness in sentiment analysis by comment category
Figure 2.
Figure 2.
Mean values for anger and happiness in sentiment analysis by comment category
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Grants and funding

Part of the research (sentiment analysis) was co-financed by the National Centre for Research and Development, Poland, grant no [POIR.01.01.01-00-0472/16] – Sentimenti (https://sentimenti.com/eu-grant/).