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

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.9K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.9K
Stereotype Content Model02:16

Stereotype Content Model

14.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.9K
Surveys02:16

Surveys

16.0K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
16.0K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
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...
6.4K
Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

39.3K
When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
39.3K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

3.4K
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...
3.4K

You might also read

Related Articles

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

Sort by
Same author

An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.

Diagnostics (Basel, Switzerland)·2026
Same author

New Gait Representation Maps for Enhanced Recognition in Clinical Gait Analysis.

Bioengineering (Basel, Switzerland)·2025
Same author

A social information sensitive model for conversational recommender systems.

PeerJ. Computer science·2025
Same author

A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities.

Scientific reports·2025
Same author

Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images.

Scientific reports·2025
Same author

Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network.

Frontiers in medicine·2025

Related Experiment Video

Updated: Sep 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts.

Abdullah Y Muaad1,2, Hanumanthappa Jayappa Davanagere1, J V Bibal Benifa3

  • 1Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India.

Computational Intelligence and Neuroscience
|April 5, 2022
PubMed
Summary

This study introduces an AI approach for detecting Arabic misogyny and sarcasm on social media. AraBERT achieved the highest accuracy in identifying these harmful online behaviors.

More Related Videos

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
08:36

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

Published on: August 31, 2017

10.7K
An Experimental Model of Diet-Induced Metabolic Syndrome in Rabbit: Methodological Considerations, Development, and Assessment
10:31

An Experimental Model of Diet-Induced Metabolic Syndrome in Rabbit: Methodological Considerations, Development, and Assessment

Published on: April 20, 2018

10.8K

Related Experiment Videos

Last Updated: Sep 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K
Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
08:36

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

Published on: August 31, 2017

10.7K
An Experimental Model of Diet-Induced Metabolic Syndrome in Rabbit: Methodological Considerations, Development, and Assessment
10:31

An Experimental Model of Diet-Induced Metabolic Syndrome in Rabbit: Methodological Considerations, Development, and Assessment

Published on: April 20, 2018

10.8K

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Social Media Analysis

Background:

  • Social media platforms are key news dissemination channels.
  • Online comments and their impact are subjects of extensive research.
  • Automated detection of harmful content like misogyny and sarcasm is crucial.

Purpose of the Study:

  • To develop and evaluate an AI-based system for automatic detection of Arabic misogyny and sarcasm in social media.
  • To compare the performance of seven Natural Language Processing (NLP) classifiers in binary and multiclass scenarios.
  • To identify the most effective classifier for this task.

Main Methods:

  • Utilized seven state-of-the-art NLP classifiers: ARABERT, PAC, LRC, RFC, LSVC, DTC, and KNNC.
  • Employed two Arabic datasets (misogyny and Abu Farah) for training and validation.
  • Evaluated classifiers in both binary and multiclass classification settings for misogyny and sarcasm detection.

Main Results:

  • AraBERT achieved the highest accuracy for misogyny detection: 91.0% (binary) and 89.0% (multiclass).
  • AraBERT also demonstrated superior performance in sarcasm detection: 88.0% (binary) and 77.0% (multiclass).
  • The proposed AI approach proved effective in identifying misogyny and sarcasm in Arabic social media text.

Conclusions:

  • AraBERT is identified as a superior deep learning classifier for detecting Arabic misogyny and sarcasm.
  • The AI-based methodology offers a robust solution for content moderation on social media platforms.
  • Further research can build upon these findings to enhance online safety and communication.