Multimodal Cyber-Harassment Detection

Multimodal Cyber-Harassment Detection

Cyberbullying is a prevalent social issue that can cause significant harm to individuals, particularly young people. Traditional approaches to detecting cyberbullying rely on text-based analysis of online messages, but the use of multiple modes of communication in cyberbullying presents challenges f...

Full description

Saved in:
Bibliographic Details
Published in:2023 International Symposium on Networks, Computers and Communications (ISNCC) pp. 1 - 6
Main Authors: El Hadi Haouari, Mohamed, Tounsi, Abdelkader, Salem, Osman, Mehaoua, Ahmed
Format: Conference Proceeding
Language:English
Published: IEEE 23-10-2023
Subjects:
Cyberbullying
Cyberbullying detection
Emotion recognition
Ethics
Fingers
landmarks
machine learning
Machine learning algorithms
Multimodal
online harassment
Privacy
sentiment analysis
Visualization
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cyberbullying is a prevalent social issue that can cause significant harm to individuals, particularly young people. Traditional approaches to detecting cyberbullying rely on text-based analysis of online messages, but the use of multiple modes of communication in cyberbullying presents challenges for detection. In this study, we compare the effectiveness of audio-only, visual-only, and text-only approaches for cyberbullying detection. We collected different datasets for the study and used machine learning algorithms, speech emotion recognition, text conversion, middle finger, and landmarks techniques to analyze the data. Our results show that the text-only approach achieved the highest accuracy of 95%. Our study demonstrates that text-based features remain the most informative for cyberbullying detection, while audio and visual features alone are less effective. These findings suggest that the development of more comprehensive cyberbullying detection methods should focus on improving text-based analysis while considering the potential benefits of incorporating audio and visual features.
ISSN:2768-0940
DOI:10.1109/ISNCC58260.2023.10323778