Search

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

This is the preregistration of a secondary data analysis exploring citizens' metacognitive accuracy in detecting political misinformation. It is the first step of a two-step preregistration procedure where the second step will be the preregistration of the actual analysis code.

Preregistration of methodology for document preparation Stephan Lewandowsky, Final version, 25 June 2020 stephan.lewandowsky@bristol.ac.uk Debunking2020 aims to be a consensually-developed document authored by leading researchers in the field of the behavior and cognition of misinformation and its debunking. It seeks to build on the Debunking Handbook, authored by John Cook and Stephan Lewandowsky in 2011 (http://sks.to/debunk), which is no longer current. This document is intended to preregister the process for creating Debunking2020.

This is an updated registration of the project that can be found at https://osf.io/te8hq/ - Our earlier registration contained the wrong p value for the cluster-significance threshold (page 11) Our aim is to gain a better understanding of the cognitive mechanisms underlying continued reliance on misinformation. To this end we will present subjects with brief fictional news reports that may or may not require their memory to be updated. Reliance on misinformation will be assessed through a decision task involving the clarification of a picture as a 'match' or 'mismatch' for the preceding report.

An experiment investigating the effects of different framings of COVID-19 on participants' climate change concern and support for climate mitigation.

UK and US-specific preregistration of global project on social licencing.

The aim of the study is to investigate whether training on misinformation techniques can inoculate people against misinformation about the Covid-19 vaccines.

Full reproduction materials for the manuscript entitled "Social media discourse of political elites: An asymmetric American exceptionalism".

v1.0 of the project

Social media companies face moral dilemmas between two values — freedom of expression and preventing harm from false information — when they decide whether to remove posts containing dangerous misinformation and whether to suspend accounts that spread such falsehoods. In this study, we use a conjoint survey experiment to explore how people approach these moral dilemmas across four misinformation topics: politics (election denial), health (COVID-19 anti-vaccination), history (Holocaust denial), and environment (climate change denial). We focus on factors that shape people's preferences to remove the content and to suspend accounts within each scenario. We are also interested in how these attitudes compare across scenarios and what individual differences can account for variations in people's moral decisions.

this is for Exp. 2

The question whether the increased exposure to and engagement with digital media, is causally related to dynamics of political polarization in recent years is hotly debated among scholars of various disciplines. This project aims to systematically review the available evidence pertaining to different causal and non-causal links between the rise of social media platforms and crisis symptoms of liberal democracies. These links are being studies with a diverse set of methods including observational studies, laboratory experiments, quasi-experiments, field experiments and large-scale online experiments. This protocol serves to register our research question, search strategy, methods of quality assessment and evidence synthesis prior to data collection. As we expect a heterogeneous measurement- and methodology landscape, which makes a standardized classification a priori impossible, the analysis plan for the selected papers is not registered here, instead such a mapping is part of the expected results of this project.

This research aims to determine the effectiveness of protecting the public against misinformation as a means of reducing extremist Islamophobia and Islamic radicalisation. The study aims to test a psychological strategy known as “inoculation”, which equips individuals with the skills to assess and refute potential misinformation critically. Inoculation works by explaining the techniques that are being used to mislead the public before a person is exposed to such misleading rhetoric. This study endeavors to examine inoculation within the context of Islamophobia and Islamist radicalisation. Previous attempts by Western governments to counter extremist messaging have often been problematic. The U.S. government’s “public diplomacy” programs, for example, often inadvertently offended and rebuffed the target audience they were intended to connect with. The inoculation intervention differs from those persuasive attempts by offering tools to scrutinise information, rather than seeking to persuade directly. What will be done in the study? · All participants will first answer demographic questions about their age, gender, and religious beliefs. · Participants will then be split into two groups. One group of participants will be trained (via video) to recognize the strategies by which the public can be misled. Participants in the other group will receive no training. Participants in the training group will be trained to identify three misleading techniques, polarisation, hasty generalisation, and invoking emotion. · Following this, all participants will be presented with either Islamophobic misinformation, or examples of misinformation techniques associated with Islamist extremism. This content is fictitious and designed to demonstrate examples of misinformation within this context. · For each material, participants will decide whether or not they would share it among friends and indicated how credible they found the information.

The spread of Islamophobic and radical Islamist propaganda is facilitated by the internet. For example, studies on radical Islamic behaviour highlight the role of social media as a tool for the radicalisation of extremists by creating more opportunities for individuals to become radicalised. Propaganda used to spread both radical Islamic and Islamophobic ideas can be classified as misinformation. Understanding how misinformation is spread and the nature of the misinformation can help to combat their effects. Inoculation Theory proposes that identifying markers of misinformation and alerting individuals to these markers provides them with cognitive tools to increase sensitivity to these markers and so helps combat susceptibility to the messages within the propaganda. Through inoculation we can prebunk misinformation by boosting cognitive competencies in individuals to support their understanding of, and resilience to, misinformation. This study tests this theory by measuring responses to potential gateway material using an inoculation video. The inoculation video ‘reverse engineers’ three markers of misinformation: hasty generalizations, polarisation and invoking emotion. In a 2 x 2 between-studies design, participants are first shown either the inoculation video or a control video and then randomly allocated to view either radical Islamic or Islamophobic potential gateway content. The study evaluates the effect of the inoculation video by measuring responses to six questions. The questions include perceived reliability of, feelings of anger towards, agreement with, and support for both the Islamophobic and radical Islamic potential gateway content. Questions also ask the participants to choose next choice options from a randomly ordered range of benign-to-extreme thumbnails in a mock YouTube recommender system and measure their likelihood to share.

v1.1 of the project: minor, insubstantive changes during the last round of reviews (labeling of variable levels, figure elements, figure proportions & size etc.). See https://github.com/stefanherzog/ais_surveys/compare/v1.0...v1.1?diff=split for the exact changes.

The novel coronavirus or COVID-19 was declared a pandemic by the World Health Organization on March 11th, 2020 (World Health Organization, 2020). The COVID-19 virus transmits early in its life cycle relative to other coronaviruses, (e.g., SARS), during which time many individuals present as asymptomatic (Zhou et al., 2020). The moderate to high transmissibility of this virus has required governments to consider moderate to extreme measures in order to prevent further transmissions. Indeed, the nature of the COVID-19 pandemic may require governments to use big data technologies to help contain its spread (Bonsall, Parker, & Fraser, 2020). Countries that have managed to “flatten the curve” - the process of slowing the exponential transmission rate of the virus, for example, Singapore and Taiwan - have employed collocation tracking through mobile Wi-Fi, GPS, and Bluetooth as a strategy to mitigate the impact of COVID-19 (Wang et al., 2020). Through collocation tracking, government agencies may observe who you have been in contact with and when this contact occurred, allowing for the rapid implementation of appropriate measures to reduce the spread of COVID-19. The effectiveness of collocation tracking relies on the willingness of the population to support such measures, implying that government policy-making should be informed by the likelihood of public compliance. Gaining the social license - broad community acceptance beyond formal legal requirements - for collocation tracking requires the perceived public health benefits to outweigh concerns of personal privacy, security, and any potential risk of harm. In the current study, we propose a longitudinal cross-cultural study mapping the evolution of people’s attitudes towards government tracking during the COVID-19 crisis. We aim to understand (1) the factors that influence the social license around governmental use of location tracking data in an emergency, (2) how this may change over time, and (3) how it may differ across cultures. We will present participants with one of two scenarios describing mild or severe government tracking methods that may reduce the spread of COVID-19, and then question participants’ attitudes towards the proposed methods.