Jove
Visualize
Contact Us
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 Concept Videos

Understanding Deception01:14

Understanding Deception

74
Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
74
False Memories01:18

False Memories

223
False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information...
223
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.6K
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...
6.6K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

7.2K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
7.2K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.7K
Bias01:22

Bias

6.6K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
6.6K

You might also read

Related Articles

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

Sort by
Same journal

The Existential Risks of Nuclear Weapons: A Role for Ethics and Emotions.

Philosophy & technology·2026
Same journal

The Formula of Humanity and AI Use.

Philosophy & technology·2026
Same journal

The Many Faces of Indeterminacy in Interactive Deadbots.

Philosophy & technology·2026
Same journal

Conceptualising conceptual resilience. A comparative approach.

Philosophy & technology·2026
Same journal

Privacy and Human-AI Relationships.

Philosophy & technology·2026
Same journal

Does Accountability Require Agency? Comment on Responsibility and Accountability in the Algorithmic Society.

Philosophy & technology·2026
See all related articles

Related Experiment Video

Updated: Nov 17, 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

840

Detecting Fake News: Two Problems for Content Moderation.

Elizabeth Stewart1

  • 1Department of Philosophy, University of South Carolina, Columbia, SC USA.

Philosophy & Technology
|February 16, 2021
PubMed
Summary
This summary is machine-generated.

Online platforms face pressure to combat fake news, but interventions risk censorship accusations. This study argues that defining and labeling problematic content involves subjective value judgments, potentially eroding user trust in fact-checking initiatives.

Keywords:
Content moderationFake newsSocial media

Related Experiment Videos

Last Updated: Nov 17, 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

840

Area of Science:

  • Digital media studies
  • Information science
  • Communication studies

Background:

  • The proliferation of online fake news poses significant societal risks.
  • Content-sharing platforms are increasingly expected to moderate user-generated content.
  • Interventions against fake news raise concerns about censorship and bias.

Purpose of the Study:

  • To examine the challenges in identifying and labeling online fake news.
  • To explore the inherent value judgments in content moderation.
  • To analyze the impact of these judgments on user trust in fact-checking.

Main Methods:

  • Conceptual analysis of content moderation policies.
  • Examination of the theoretical and practical difficulties in unbiased content labeling.
  • Argumentation based on the nature of value judgments in digital platforms.

Main Results:

  • Defining 'fake news' for flagging purposes requires subjective value judgments.
  • Achieving unbiased data collection and labeling of problematic content is practically and theoretically challenging.
  • These inherent value judgments can undermine user confidence in fact-checking mechanisms.

Conclusions:

  • Fact-checking efforts on digital platforms are susceptible to accusations of bias due to unavoidable value judgments.
  • The tension between content moderation and censorship is rooted in the subjective nature of defining and flagging problematic content.
  • User distrust can arise from the perception that fact-checking is influenced by non-neutral criteria.