Abstract
The World Health Organization defines vaccine hesitancy as a delay in acceptance or refusal of vaccination despite the availability of vaccination services. Vaccine hesitancy contributes to lower rates of vaccination in a population and delayed vaccine coverage. A large number of COVID-19 vaccines have been administered worldwide against COVID-19. Due to concerns people have about COVID-19 vaccine adverse events, a significant proportion of people exhibit hesitancy towards the vaccines. These are often prompted by information and misinformation spread through social media conversation, which is not driven exclusively by genuine human-run accounts. Social bots have been shown to be very active during the pandemic participating in discussions about vaccines, including the spread of conflicting and misleading information. Using a novel ensemble technique, we sought to identify and describe the involvement of social bots in COVID-19 vaccination-related discussions on Twitter and how this could have influenced sentiments and hesitancies about COVID-19 vaccines. We included tweets from January to December 2021 to present a whole year's analysis in relation to the vaccines. Unique usernames from these posts were passed to Botometer and Tweetbotornot, programs that review Twitter accounts, to detect a broad range of social bots using a scoring system. A domain-oriented transfer learning technique is applied by finetuning the CT-BERT V2 model to detect the influence of social bots on COVID-19 vaccine sentiments. We computed the ratio of sentiment transmission from bots-to-human, human-to-human, human-to-bots, and bots-to-bots. BERTopic was used to extract the topics of discussion to identify the amplified or transferred hesitancies. Social bots' participation in online discussions noticeably influenced human sentiments and hesitancies about COVID-19 vaccination. A major portion of sentiments transferred from bot to human during the period of study appeared to amplify or transfer hesitancies regarding COVID-19 vaccination.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2023 Australasian Computer Science Week (ACSW 2023) |
| Editors | Iqbal Gondal, Carsten Rudolph, Matt Warren, Zahir Tari, Paul Haskell-Dowland, Maggie Liu, Klaus D. Veil, Pari Delir Haghighi, Maria Read, Adel N. Toosi |
| Place of Publication | Danvers MA USA |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 190-197 |
| Number of pages | 8 |
| ISBN (Electronic) | 9798400700057 |
| DOIs | |
| Publication status | Published - 2023 |
| Externally published | Yes |
| Event | Australasian Computer Science Week 2023 - Melbourne, Australia Duration: 31 Jan 2023 → 3 Feb 2023 https://dl.acm.org/doi/proceedings/10.1145/3579375 (Conference proceedings) https://acsw.core.edu.au/2023-home (Website) |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | Australasian Computer Science Week 2023 |
|---|---|
| Abbreviated title | ACSW 2023 |
| Country/Territory | Australia |
| City | Melbourne |
| Period | 31/01/23 → 3/02/23 |
| Internet address |
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UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- COVID-19 vaccine
- Social Bots
- Social Media
- Transfer Learning
- Vaccine Hesitancy
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