Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Air-entraining Agents

Air-entraining Agents

Air-entraining agents improve the durability and workability of concrete in climates with frequent freezing and thawing. These agents prevent cracks by introducing small air bubbles into the mix, creating spaces accommodating water expansion when temperatures drop. The air-entraining agents lower the surface tension of water, forming stable, small air bubbles. This method is more effective than having accidental large voids, as the intentional, smaller, and evenly distributed air voids improve...

Language and Cognition

Language and Cognition

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.

Impression Management Techniques IV: Altercasting

Impression Management Techniques IV: Altercasting

Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role is assigned, it becomes socially...

You might also read

Related Articles

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

Sort by

Same author

Enhancing IELTS writing automated scoring with M-LoRA fine-tuned LLAMA-3 and human feedback-driven PPO reinforcement learning.

Scientific reports·2026

Same author

A peek under the mask: exploring dental students' experiences through focus group discussions.

BMC medical education·2025

Same author

Correction: Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models.

PloS one·2025

Same author

Medical radiology report generation: A systematic review of current deep learning methods, trends, and future directions.

Artificial intelligence in medicine·2025

Same author

Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.

PloS one·2025

Same author

A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions.

PeerJ. Computer science·2024

Search research articles

Related Experiment Video

Updated: May 14, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models.

Ali Alhazmi^1,2, Rohana Mahmud¹, Norisma Idris¹

¹Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

|July 17, 2024

Summary

This study enhances hate speech detection for Arabic social media, especially with code-mixing. The TF-IDF feature and SGD model achieved 98.21% accuracy, outperforming previous methods.

More Related Videos

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

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

Published on: December 15, 2023

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

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

Published on: December 6, 2024

Related Experiment Videos

Last Updated: May 14, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

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

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

Published on: December 15, 2023

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

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

Published on: December 6, 2024

Area of Science:

Natural Language Processing
Machine Learning
Computational Linguistics

Background:

Social media facilitates international communication but struggles with hate speech.
Arabic presents unique challenges for hate speech detection due to dialects and code-mixing.
Existing English-centric methods are insufficient for nuanced Arabic content.

Purpose of the Study:

To evaluate machine learning models for Arabic hate speech detection.
To assess the impact of variation features on detecting code-mixed hate speech.
To compare model performance on Arabic and code-mixed hate speech datasets.

Main Methods:

Data collection and pre-processing of Arabic social media text.
Feature extraction, including TF-IDF.
Development and evaluation of various machine learning classification models.
Comparison with existing hate speech detection studies.

Main Results:

The TF-IDF feature combined with the SGD model achieved the highest accuracy of 98.21%.
The proposed approach demonstrated superior performance compared to three prior studies.
Effective identification of hate speech in Arabic tweets, including code-mixed instances.

Conclusions:

Machine learning models with appropriate features can effectively detect Arabic hate speech.
The TF-IDF and SGD model combination offers a robust solution for this challenge.
This research provides a foundation for automated hate speech detection in multilingual contexts.