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

Goodness-of-Fit Test01:16

Goodness-of-Fit Test

7.8K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
7.8K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

6.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
6.6K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.7K
Factorial Design02:01

Factorial Design

13.6K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.6K
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Canopy structural diversity mediates the effect of climate on primary productivity in forests.

Nature communications·2026
Same author

Liana cutting accelerates the structural recovery of once-logged tropical forests at a fraction of the cost of tree planting.

Current biology : CB·2026
Same author

Tree diversity-soil organic carbon relationships strengthen under colder and more arid conditions.

The New phytologist·2026
Same author

Interactions Between Enrichment Planted Seedlings and Naturally Occurring Trees in Selectively Logged Lowland Dipterocarp Forest.

Ecology and evolution·2026
Same author

Higher-order interactions enhance the latitudinal tree diversity gradient.

Nature·2026
Same author

Modelling cultural responses to disease spread in Neolithic Trypillia mega-settlements.

Journal of the Royal Society, Interface·2026

Related Experiment Video

Updated: Dec 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Rating news claims: Feature selection and evaluation.

Izzat Alsmadi1, Michael J O'Brien2

  • 1Department of Computing and Cyber Security, Texas A&M University-San Antonio, San Antonio, Texas 78224, USA.

Mathematical Biosciences and Engineering : MBE
|April 3, 2020
PubMed
Summary

This study analyzes online news claims, finding that online social networks (OSNs) frequently spread false information. Negative sentiment in content can help identify and predict false claims.

Keywords:
feature extractioninformation credibilityonline social networkspredictive models

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K

Related Experiment Videos

Last Updated: Dec 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K

Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Information Science

Background:

  • Online news claims from diverse sources pose credibility challenges.
  • Human fact-checking is effective but resource-intensive.
  • Automated methods are needed to assess claim credibility.

Purpose of the Study:

  • To identify features distinguishing true from false news claims.
  • To develop algorithms for predicting the veracity of unlabeled claims.
  • To analyze claim credibility across different online platforms.

Main Methods:

  • Extracted news claims from fact-checking websites (Snopes, Emergent).
  • Utilized public datasets with human-assigned claim ratings (true, false, mostly true, mostly false).
  • Evaluated feature extraction methods and feature sets for predictive accuracy.

Main Results:

  • Online social networks (OSNs) exhibit higher rates of false claims compared to other categories.
  • False claims are more prevalent than true claims across most analyzed categories.
  • Content analysis revealed that false claims often contain more negative sentiment.

Conclusions:

  • Distinctive features, particularly negative sentiment, can aid in classifying claim veracity.
  • Automated analysis of online claims is feasible and can identify patterns of misinformation.
  • Findings support the development of algorithmic tools for large-scale credibility assessment.