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Learning from Machines? Social Bots Influence on COVID-19 Vaccination-Related Discussions: 2021 in Review

  • Muhammad Javed
  • , Gerardo Luis Dimaguila
  • , Sedigh Khademi Habibabadi
  • , Chris Palmer
  • , Jim Buttery

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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 languageEnglish
Title of host publicationProceedings of 2023 Australasian Computer Science Week (ACSW 2023)
EditorsIqbal 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 PublicationDanvers MA USA
PublisherAssociation for Computing Machinery (ACM)
Pages190-197
Number of pages8
ISBN (Electronic)9798400700057
DOIs
Publication statusPublished - 2023
Externally publishedYes
EventAustralasian 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

NameACM International Conference Proceeding Series

Conference

ConferenceAustralasian Computer Science Week 2023
Abbreviated titleACSW 2023
Country/TerritoryAustralia
CityMelbourne
Period31/01/23 → 3/02/23
Internet address

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • COVID-19 vaccine
  • Social Bots
  • Social Media
  • Transfer Learning
  • Vaccine Hesitancy

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