User agency–based versus machine agency–based misinformation interventions: The effects of commenting and AI fact-checking labeling on attitudes toward the COVID-19 vaccination
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC10113910 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Since the World Health Organization (WHO, February 2, 2020) reported that the spread of coronavirus disease has been accompanied by a “massive infodemic,” the COVID-19 outbreak has become a national and international battleground of a struggle against misinformation. Fact-checking outlets around the world have been actively counteracting false and misleading information surrounding the pandemic. In this article, we conceptualize fact checkers in terms of the “interpretative power” that journalism holds in processes of political performances (Alexander in Soc Theory 22(4): 527–573, 2004, in: The performance of politics. Obama’s victory and the struggle for democratic power. Oxford University Press, Oxford/New York, 2010). Drawing on virus-related fact checks from Poynter’s International Fact-Checking Network (IFCN) database, we make two arguments. First, we argue that the new phenomenon of specialized “fact checking” might be considered as a further explicitly differentiated element of Alexander’s model of cultural performance, which fulfills a double duty: trying to contribute to further “de-fusion” (separating audiences from actors when the latter lack authenticity and credibility) on the one hand, and working to overcome it on the other. Second, we explain how new fact-checking practices have become a reflexive supplement to the news media of the civil sphere that might be able to help the civil sphere’s communicative institutions to defend truthfulness in a manner that contributes to democracy.
| S-EPMC7520375 | biostudies-literature
Project description:Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.
| S-EPMC4471100 | biostudies-literature
Project description:Today's media landscape affords people access to richer information than ever before, with many individuals opting to consume content through social channels rather than traditional news sources. Although people frequent social platforms for a variety of reasons, we understand little about the consequences of encountering new information in these contexts, particularly with respect to how content is scrutinized. This research tests how perceiving the presence of others (as on social media platforms) affects the way that individuals evaluate information-in particular, the extent to which they verify ambiguous claims. Eight experiments using incentivized real effort tasks found that people are less likely to fact-check statements when they feel that they are evaluating them in the presence of others compared with when they are evaluating them alone. Inducing vigilance immediately before evaluation increased fact-checking under social settings.
| S-EPMC5468680 | biostudies-literature
Project description:Professional fact-checking, a prominent approach to combating misinformation, does not scale easily. Furthermore, some distrust fact-checkers because of alleged liberal bias. We explore a solution to these problems: using politically balanced groups of laypeople to identify misinformation at scale. Examining 207 news articles flagged for fact-checking by Facebook algorithms, we compare accuracy ratings of three professional fact-checkers who researched each article to those of 1128 Americans from Amazon Mechanical Turk who rated each article’s headline and lede. The average ratings of small, politically balanced crowds of laypeople (i) correlate with the average fact-checker ratings as well as the fact-checkers’ ratings correlate with each other and (ii) predict whether the majority of fact-checkers rated a headline as “true” with high accuracy. Furthermore, cognitive reflection, political knowledge, and Democratic Party preference are positively related to agreement with fact-checkers, and identifying each headline’s publisher leads to a small increase in agreement with fact-checkers.
| S-EPMC8442902 | biostudies-literature
Project description:Advances in artificial intelligence (AI) have significantly improved the abilities of machines. Human-unique abilities, such as art creation, are now being challenged by AI. Recent studies have investigated and compared people's attitudes toward human-made and AI-generated artworks. These results suggest that a negative bias may exist toward the latter. However, none of these previous studies has examined the extent of this bias. In this study, we investigate whether a bias against AI art can be found at an implicit level. Viewers' attitudes toward AI art were assessed using eye-tracking measures and subjective aesthetic evaluations. Visual attention and aesthetic judgments were compared between artworks categorized as human-made and AI-made. The results showed that although it was difficult for individuals to identify AI-generated artwork, they exhibited an implicit prejudice against AI art. Participants looked longer at paintings that they thought were made by humans. No significant effect of categorization of paintings was found in subjective evaluations. These findings suggest that although human and AI art may be perceived as having similar aesthetic values, an implicit negative bias toward AI art exists. Although AI can now perform creative tasks, artistic creativity is still considered a human prerogative.
| S-EPMC10663653 | biostudies-literature
Project description:While efficacious vaccines have been developed to inoculate against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; also known as COVID-19), public vaccine hesitancy could still undermine efforts to combat the pandemic. Employing a survey of 1096 adult Americans recruited via the Lucid platform, we examined the relationships between vaccine attributes, proposed policy interventions such as financial incentives, and misinformation on public vaccination preferences. Higher degrees of vaccine efficacy significantly increased individuals' willingness to receive a COVID-19 vaccine, while a high incidence of minor side effects, a co-pay, and Emergency Use Authorization to fast-track the vaccine decreased willingness. The vaccine manufacturer had no influence on public willingness to vaccinate. We also found no evidence that belief in misinformation about COVID-19 treatments was positively associated with vaccine hesitancy. The findings have implications for public health strategies intending to increase levels of community vaccination.
| S-EPMC8121853 | biostudies-literature
Project description:BackgroundEffective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy.ObjectiveThis work investigates (1) the changes in the public's attitude toward COVID-19 vaccines over time, (2) the effectiveness of COVID-19 vaccine fact-checking information on social media engagement and attitude change, and (3) the emotional and linguistic features of the COVID-19 vaccine fact-checking information ecosystem.MethodsWe collected a data set of 12,553 COVID-19 vaccine fact-checking Facebook posts and their associated comments (N=122,362) from January 2020 to March 2022 and conducted a series of natural language processing and statistical analyses to investigate trends in public attitude toward the vaccine in COVID-19 vaccine fact-checking posts and comments, and emotional and linguistic features of the COVID-19 fact-checking information ecosystem.ResultsThe percentage of fact-checking posts relative to all COVID-19 vaccine posts peaked in May 2020 and then steadily decreased as the pandemic progressed (r=-0.92, df=21, t=-10.94, 95% CI -0.97 to -0.82, P<.001). The salience of COVID-19 vaccine entities was significantly lower in comments (mean 0.03, SD 0.03, t=39.28, P<.001) than in posts (mean 0.09, SD 0.11). Third-party fact checkers have been playing a more important role in more fact-checking over time (r=0.63, df=25, t=4.06, 95% CI 0.33-0.82, P<.001). COVID-19 vaccine fact-checking posts continued to be more analytical (r=0.81, df=25, t=6.88, 95% CI 0.62-0.91, P<.001) and more confident (r=0.59, df=25, t=3.68, 95% CI 0.27-0.79, P=.001) over time. Although comments did not exhibit a significant increase in confidence over time, tentativeness in comments significantly decreased (r=-0.62, df=25, t=-3.94, 95% CI -0.81 to -0.31, P=.001). In addition, although hospitals receive less engagement than other information sources, the comments expressed more positive attitudinal valence in comments compared to other information sources (b=0.06, 95% CI 0.00-0.12, t=2.03, P=.04).ConclusionsThe percentage of fact-checking posts relative to all posts about the vaccine steadily decreased after May 2020. As the pandemic progressed, third-party fact checkers played a larger role in posting fact-checking COVID-19 vaccine posts. COVID-19 vaccine fact-checking posts continued to be more analytical and more confident over time, reflecting increased confidence in posts. Similarly, tentativeness in comments decreased; this likewise suggests that public uncertainty diminished over time. COVID-19 fact-checking vaccine posts from hospitals yielded more positive attitudes toward vaccination than other information sources. At the same time, hospitals received less engagement than other information sources. This suggests that hospitals should invest more in generating engaging public health campaigns on social media.
| S-EPMC9217154 | biostudies-literature
Project description:Accurate identification of true versus false climate information in the digital age is critical. Misinformation can significantly affect public understanding and policymaking. Automated fact-checking seeks to validate claims against trustworthy factual data. This study tackles the challenge of fact-checking climate claims by leveraging the currently most capable Large Language Models (LLMs). To this end, we introduce Climinator, an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning. It significantly boosts the performance of automated fact-checking by integrating authoritative, up-to-date sources within a novel debating framework. This framework provides a trustworthy and context-aware analysis incorporating multiple scientific viewpoints. Climinator helps identify misinformation in real time and facilitates informed dialog on climate change, highlighting AI's role in environmental discussions and policy with reliable data.
| S-EPMC11860206 | biostudies-literature
Project description:Understanding attitudes towards anthropogenic disturbances, especially among undergraduates, is important to inform educational practices because of the theoretical link between attitude and behavior. We evaluated the attitudes of undergraduate students in a biology majors course and nonmajors course toward two anthropogenic disturbances: wildfire and urbanization. Student attitudes were assessed via an online Wildfire and Urbanization Attitude survey (WUAS) before and after a video intervention, randomly delivered as either fact- or emotion-based versions. Student beliefs regarding wildfire and urbanization were positively correlated with their general intention to act toward environmental issues on both pre- and postintervention surveys, as suggested by theory. Student belief that urbanization was bad for the environment increased from pre- to postintervention. However, beliefs and intention to act did not statistically differ between majors/nonmajors or intervention video type. This study hints that brief interventions can impact student disturbance beliefs, but more research is needed to guide curriculum development. Despite some research suggesting the value of emotion to inspire climate action, our results suggest that more work needs to be done regarding the value of emotion to increase environmental action toward other anthropogenic disturbances.
| S-EPMC10956598 | biostudies-literature
Project description:BackgroundThe rapid advancement of artificial intelligence (AI) has revolutionized both medical education and healthcare by delivering innovative tools that enhance learning and improve overall outcomes. The study aimed to assess students' perceptions regarding the credibility and effectiveness of AI as a learning tool and to explore the dynamics of integrating AI in medical education.MethodologyA cross-sectional study was carried out across medical colleges in Pakistan. A 26-question survey was developed using Google Forms from previously validated studies. The survey assessed demographics of participants, basic understanding of AI, AI as a learning tool in medical education and socio-ethical impacts of the use of AI. The data was analyzed using SPSS (v 26.0) to derive descriptive and inferential statistics.ResultA total of 702 medical students aged 18 to 26 years (mean age 20.50 ± 1.6 years) participated in the study. The findings revealed a generally favorable attitude towards AI among medical students (80.3%), with the majority considering it an effective (60.8%) and credible (58.4%) learning tool in medical education. Students agreed that AI learning optimized their study time (60.3%) and provided up-to-date medical information (63.1%). Notably, 65.7% of students found AI more efficient in helping them grasp medical concepts compared to traditional tools like books and lectures, while 66.8% reported receiving more accurate answers to their medical inquiries through AI. The study highlighted that medical students view traditional tools as becoming increasingly outdated (59%), emphasizing the importance of integrating AI into medical education and creating dedicated AI tools (80%) for the medical education.ConclusionThis study demonstrated that AI is an effective and credible tool in medical education, offering personalized learning experiences and improved educational outcomes. AI tools are helping students learn medical concepts by cutting down on study-time, providing accurate answers, and ultimately improving study outcomes. We recommend developing dedicated AI tools for medical education and their formal integration into medical curricula, along with appropriate regulatory oversight to ensure AI can enhance human abilities rather than acting as a replacement for humans.
| S-EPMC11744861 | biostudies-literature