Pre-registration for JITSUVAX WP2.1 Angelo Fasce, Dawn Holford, Stephan Lewandowsky, Philipp Schmid (stephan.lewandowsky@bristol.ac.uk) Introduction Rationale Describe the rationale for the review in the context of existing knowledge. Many studies have catalogued anti-vaccine arguments and misinformation (e.g., Hoffman et al., 2019; Hughes et al. 2021), grouping them into thematic categories. However, few studies seem to differentiate between the "tropes"—broad categories that are sometimes also referred to as "themes", or "narratives"—of a message and its specific method of persuasion. Moreover, there are few discussions of attitudinal roots—underlying fears or ideologies that sustain and motivate specific attitudes (Hornsey & Fielding, 2017)—that may underlie the catalogued anti-vaccine arguments and misinformation. Given that some studies suggest that understanding the attitudinal roots of arguments, and differentiating the narrative content and the method of persuasion can help mitigate the potential damage of anti-vaccination arguments (Hornsey & Fielding, 2017, Schmid & Betsch, 2019), there is a need for a more sophisticated taxonomy of anti-vaccination arguments. Thus, the primary aim of this review is to systematically identify existing tropes and rhetorical strategies present in studies of anti-vaccination content in the literature to date. Based on this, we aim to develop a unifying taxonomy of anti-vaccination content that links tropes and rhetorical strategies to the psychological roots of anti-vaccination attitudes. Objectives Provide an explicit statement of the objective(s) or question(s) the review addresses. The primary aim of this review is to systematically identify existing tropes and rhetorical strategies present in existing studies of anti-vaccination content in the literature to date. Based on this, we aim to develop a taxonomy of anti-vaccination content that links tropes and rhetorical strategies to the psychological roots of anti-vaccination attitudes. Methods Eligibility criteria Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses. Inclusion criteria • Published articles in peer-reviewed journals or on scientific preprint servers (e.g., medrXiv) • In English, although future searches may be conducted in German, Portuguese, Finnish, Swedish, Spanish, and French. • Reports analyses of anti-vaccine statements in public circulation (e.g., on a website, social media, blog etc.). We define "analyses" broadly, including descriptive analyses, conceptual and philosophical analysis, text modelling, etc. Exclusion criteria • Studies focusing on vaccination of non-human subjects Information sources Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted. We will be searching the following major scientific search engines: Scopus, Web of Science, PubMed, and PsycInfo. These sources were selected for their coverage of the relevant literature (e.g., psychology, health studies and argumentation) and their ability to satisfy relevance, transparency, and reproducibility criteria in systematic search (Gusenbauer & Haddaway, 2020). Because much of the research on COVID-19 will still be emerging, we will also search three preprint servers relevant to the field: • PsyArXiv • ArXiv • MedrXiv We will only include articles published in these sources prior to 1 July 2021. We will be recording the date on which the search took place for each database. Search strategy Present the full search strategies for all databases, registers and websites, including any filters and limits used. We will use the following search logic to encompass the different variations in terminology used in the literature: ("vacc* refus*" OR "anti-vacc*" OR "anti-vax*" OR "vacc* oppon*" OR "vacc* oppos*" OR "vacc* skeptic*" OR "vacc* sceptic*") AND (trope* OR meme* OR narrative* OR argumen* OR theme* OR rhetoric) Search keys will be adapted to the capabilities of each search engine as needed. Specifically, the Boolean logic represented by our search string may exceed the capability of search engines for, e.g., preprint servers, in which case we will seek to approximate the logic via partial repeated searches. Selection process Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process. Articles will be collated in an Excel spreadsheet and titles inspected to remove duplicates. Two reviewers will independently review the title and abstracts of each article and categorise them as "relevant", "not relevant", or "uncertain". Where there are discrepancies between reviewers or one reviewers has indicated "uncertain", the reviewers will meet to agree on the categorisation. Two reviewers will then review the full text of articles that were determined to be relevant on the basis of title and abstract and categorise them as "accept", "reject", or "uncertain". If there are discrepancies between reviewers, they will discuss and reach a consensus. If the two reviewers still disagree after discussion, a third reviewer will review the article to determine if it should be accepted. The criteria for acceptance will be based on the eligibility criteria and the reasons for rejection will be recorded. Data collection process Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process. Where possible, we will use database tools that allow automated extraction of article metadata (title, authors, year, doi/URL, journal/preprint server). However, this may not be possible for articles found on preprint servers that do not offer such tools (e.g., PsyArXiv). In this case, reviewers will manually complete this extraction. For each article, a single reviewer will independently complete extraction for all the content items mentioned in "data items". Data items List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect. List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information. For each article accepted for inclusion in the review, we will record the following metadata: • Title • Authors • Year • doi/URL • Journal/preprint server We will also extract the following content from each article: • Content platform o This category refers to the platform, or channel, that was studied. Multiple platforms may be reported in the same article. Common platforms include: websites, Twitter, Facebook, YouTube. • Content type (as described by article authors) o This category refers to the individual articles' authors' terminology of the tropes or strategies reported. Common terms used are "tropes", "themes", "narratives", "tactics", "strategies". • List of tropes and/or strategies identified in the article o All tropes and strategies mentioned in the article will be extracted. Study risk of bias assessment Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process. We do not assess the risk of bias or certainty because the objective is to collect all existing tropes and rhetorical strategies identified in the literature, thus there are no interventions or effect strength analyses. To counter publication bias, our search includes three well-known preprint servers, which have grown in popularity since the onset of the pandemic and provide a means for publication ahead of peer review. Based on a pre-screening of articles on anti-vaccine sentiment, we also expect to reach saturation point soon because most articles in the domain already contain substantial overlap in the tropes and rhetorical strategies they report. Competing interests This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 964728 (JITSUVAX). References Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181–217. https://doi.org/10.1002/jrsm.1378 Hoffman, B. L., Felter, E. M., Chu, K.-H., Shensa, A., Hermann, C., Wolynn, T., Williams, D., & Primack, B. A. (2019). It’s not all about autism: The emerging landscape of anti-vaccination sentiment on Facebook. Vaccine, 37(16), 2216–2223. https://doi.org/https://doi.org/10.1016/j.vaccine.2019.03.003 Hornsey, M. J., & Fielding, K. S. (2017). Attitude roots and Jiu Jitsu persuasion: Understanding and overcoming the motivated rejection of science. In American Psychologist (Vol. 72, Issue 5, pp. 459–473). American Psychological Association. https://doi.org/10.1037/a0040437 Hughes, B., Miller-Idriss, C., Piltch-Loeb, R., White, K., Creizis, M., Cain, C., & Savoia, E. (2021). Development of a Codebook of Online Anti-Vaccination Rhetoric to Manage COVID-19 Vaccine Misinformation. MedRxiv, 2021.03.23.21253727. https://doi.org/10.1101/2021.03.23.21253727 Schmid, P., & Betsch, C. (2019). Effective strategies for rebutting science denialism in public discussions. Nature Human Behaviour, 3(9), 931–939. https://doi.org/10.1038/s41562-019-0632-4