Jove
Visualize
Contact Us

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

9.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.0K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.6K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.6K
Language and Cognition01:27

Language and Cognition

323
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
323
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

175
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
175
Accuracy and Precision01:52

Accuracy and Precision

8.7K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
8.7K
Fundamental Attribution Error01:14

Fundamental Attribution Error

12.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
12.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DomainDemo: a dataset of domain-sharing activities among different demographic groups on Twitter.

Scientific data·2025
Same author

Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics.

PNAS nexus·2024
Same author

Identifying and characterizing superspreaders of low-credibility content on Twitter.

PloS one·2024
Same author

Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference.

JMIR infodemiology·2023
Same author

One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.

Journal of medical Internet research·2023
Same author

Sociodemographics and Transdiagnostic Mental Health Symptoms in SOCIAL (Studies of Online Cohorts for Internalizing Symptoms and Language) I and II: Cross-sectional Survey and Botometer Analysis.

JMIR formative research·2022
Same journal

In This Issue.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Correction for Otsuki et al., Extracellular sulfatases support cartilage homeostasis by regulating BMP and FGF signaling pathways.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Hive mind: Microbial communities and the making of memory.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Targets for disease modification in schizophrenia: New findings add to evidence for the involvement of the immune complement system.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Correction for Wang et al., The role of reduced aerosol masking from air pollutant emission reductions in recent global warming acceleration (2013-2023).

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Correction for Mishra, Ecology is not yet ready for AI-and why that matters.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

502

Fact-checking information from large language models can decrease headline discernment.

Matthew R DeVerna1, Harry Yaojun Yan1,2, Kai-Cheng Yang1,3

  • 1Observatory on Social Media, Indiana University, Bloomington, IN 47408.

Proceedings of the National Academy of Sciences of the United States of America
|December 4, 2024
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise in fact-checking online information, but AI-generated fact checks do not improve users' ability to discern accuracy or share true news, and can even be harmful.

Keywords:
AIfact-checkingheadline discernmentlarge language modelsmisinformation

More Related Videos

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

6.7K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

402

Related Experiment Videos

Last Updated: Jun 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

502
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

6.7K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

402

Area of Science:

  • Information Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Fact-checking is crucial for combating online misinformation.
  • Scaling fact-checking is challenging due to information volume.
  • AI language models show potential for automated fact-checking.

Purpose of the Study:

  • Investigate the impact of AI-generated fact-checks on belief and sharing intent for political news.
  • Compare AI fact-checks with human-generated fact-checks.
  • Identify potential harms and benefits of AI fact-checking.

Main Methods:

  • Preregistered randomized controlled experiment.
  • Assessed participants' belief and sharing intent for political headlines.
  • Utilized fact-checking information from a popular large language model (LLM).

Main Results:

  • LLM fact-checks did not improve headline accuracy discernment or sharing of accurate news.
  • Human-generated fact-checks enhanced discernment.
  • AI fact-checks decreased belief in true headlines mislabeled as false and increased belief in uncertain false headlines.
  • AI fact-checking increased sharing intent for correctly labeled true headlines.

Conclusions:

  • AI fact-checking information does not significantly improve users' ability to discern accuracy.
  • AI fact-checking can introduce specific harms, such as misinformed beliefs.
  • Human fact-checks are more effective in enhancing discernment.
  • Policies are needed to mitigate unintended consequences of AI fact-checking applications.