Source message interaction in credibility judgements of Facebook posts: A comparison between the US and Germany using the example of coronavirus

Misinformation can spread rapidly on social media where users can easily share content. One potential intervention therefore is to help users detect misinformation, which in turn might make them less likely to share it. When users lack issue-specific knowledge necessary to detect misinformation, the decision whether to share a post on social media is often a function of trust. Users may trust the source of the information overall, or they may pay more attention to message characteristics. This study therefore experimentally tests the influence of users’ perceived trustworthiness of accounts on credibility judgements of specific messages, moderated by how much attention users pay to posts. Further, this study examines how credibility judgements influence subsequent action by users

Detecting misinformation in online social networks: A think-aloud study on user strategies

Although online social networks (OSN) facilitate the distribution of misinformation, one way of reducing the spread of false information in OSN is for users to detect it. Building on the framework of how audiences act to authenticate information, this study provides a user perspective on which strategies people use in evaluating information in OSN. In 15 qualitative interviews, participants were asked to think aloud while evaluating whether the content of posts from their own newsfeeds and of interviewer-supplied posts was true or false. Their answers were analyzed to determine which evaluation strategies they used. Analyzing participants’ thoughts as they evaluate information is more reliable than directly asking participants which strategies they think they use. Results show that users’ strategies in information evaluation are searching for more information, knowledge of account or content carries the most weight, and every detail needs to fit. A comparison of strategy usage for posts from befriended versus unknown personal accounts as well as for posts from followed news outlets versus not followed news outlets shows that for posts from followed news outlets, knowledge of the account was the most-used strategy followed by knowledge of the content. For other types of posts, strategy usage varied more widely and depended on each post. This highlights the importance and possible higher ecological validity of research on posts from news outlets that users actually follow, as users’ experiences with previous posts seem to play a major role in how they go about evaluating information in new posts. </p

Scan method tracks reader attention, engagement

A methodological comparison was conducted between copy testing, eye-tracking and a relatively new scan method, where panelists record where they stop reading a news item. Results show: news item characteristics that proved most influential in previous research also significantly affect reading behavior measured with the scan method. By being precise and more practicable than copy testing and eye-tracking, the scan method can help the struggling newspaper industry to better understand their readers. </jats:p

Reply to González-Bailón et al.: Industry control and conflicts of interest in social media research

This manuscript critiques industry control and conflicts of interest in academic-industry collaborations in social media research. When academics rely on social media platforms’ classifiers, concepts, and categorization methods, it undermines the replicability of findings. Not disclosing that reliance also impedes meaningful, intersubjective peer review. Industry control over data collection and curation raises concerns about the self-interested goals of industry players in such collaborations, especially when the social media platforms claim the findings are highly policy-relevant. We specifically discuss Meta’s intentional deployment of "break glass" measures—changing the active algorithm to reduce polarization—during data collections in those collaborations. Since none of the original publications explicitly referenced these manipulations, fundamental questions arise about data reliability and transparency

Religious values and confidence in science: Perceived tensions and common ground.

While confidence in science is high compared to other institutions, many Americans question whether scientists share their values, including religious ones. Narratives surrounding science and religion often focus on a conflict between the two, but religious people, especially, tend to see their religious views as in line with science. Using survey data of two probability samples representative of U.S. adults, we examine how religious views, including perceptions of conflict and harmony between science and religion, predict confidence in science. We found that while general perceptions of religion and science as conflicting negatively predicted confidence in science, when individuals think religion endorses protecting the planet, their confidence in science was higher. The results suggest that attitudes toward religion and science are more nuanced than is often acknowledged and that audiences can be confident in science while holding strong religious beliefs. Further, the work suggests that finding and highlighting common ground between religion and science is a potentially promising avenue for cultivating confidence in science

The ‘infodemic’ infodemic: Toward a more nuanced understanding of truth-claims and the need for (not) combatting misinformation

(Mis)information scholarship struggles to assist policy actors in assessing the relative accuracy of science-related truth claims as well as their potential threat. This limited ability to produce translational research stems in part from conceptualizations of dis- and misinformation that pay insufficient attention to an information ecosystem in which defining “accuracy” is complicated by intersecting uncertainties associated with the nature of science, sociopolitical climates, and media systems. These uncertainties introduce compounding error in accuracy assessments, and they demand more nuanced understandings of mis- and disinformation than those which currently underlie discussions of the “infodemic.” Here, we propose a framework for evaluating claims in terms of a collection of intersecting attributes beset by uncertainty. We apply our framework to real-world examples and conclude by discussing implications for research and action on (mis)information in a socio-scientific world where true and false claims are not as distinct as they are sometimes purported to be

The ‘infodemic’ infodemic: Toward a more nuanced understanding of truth-claims and the need for (not) combatting misinformation

(Mis)information scholarship struggles to assist policy actors in assessing the relative accuracy of science-related truth claims as well as their potential threat. This limited ability to produce translational research stems in part from conceptualizations of dis- and misinformation that pay insufficient attention to an information ecosystem in which defining “accuracy” is complicated by intersecting uncertainties associated with the nature of science, sociopolitical climates, and media systems. These uncertainties introduce compounding error in accuracy assessments, and they demand more nuanced understandings of mis- and disinformation than those which currently underlie discussions of the “infodemic.” Here, we propose a framework for evaluating claims in terms of a collection of intersecting attributes beset by uncertainty. We apply our framework to real-world examples and conclude by discussing implications for research and action on (mis)information in a socio-scientific world where true and false claims are not as distinct as they are sometimes purported to be.</p

Our changing information ecosystem for science and why it matters for effective science communication

Current information ecologies present unique opportunities to communicate science and engage diverse publics in science. Unfortunately, they also present unique challenges. Here, we outline how the public sphere for science is transforming as media evolve, and we connect these changes to the high-stakes issue context of COVID-19. We argue that scientific organizations’ struggles to adapt to evolving media are linked, in part, to asymmetries in which social media platforms prevent researchers from producing reliable data that could inform institutional change and improve science communication. This has been apparent in studies of echo chambers and filter bubbles. Producing a more usable evidence base, we conclude, will require that scholars (a) obtain access to proprietary data, (b) reconceptualize information ecologies as social systems, (c) avoid ceding core research tasks to platforms, (d) address ethical issues, and (e) grapple with the urgency of moving forward productively