Anti-Semitic Content on Social Media Analyses Using a Hybrid Model

Title

Anti-Semitic Content on Social Media Analyses Using a Hybrid Model

Creator

Nanjundan, Preethi; Deokar, Ruchira; Ramakrishnan, Jayabrabu

Description

In the modern world, people have a variety of channels to freely express their opinions, thoughts, knowledge, and feelings on many topics on social media. However, they abuse this freedom by spewing hate speech based on a person's or group's ethnicity, gender, or religion, caste, sexual orientation, ethnicity, and nationality. Hate speech is the most likely form of expression for hostility and superstition on social media. One of the causes of cyberbullying, which can have an effect on social life on both a national and personal level, is an increase in hate speech. Hateful material has the ability to hurt and an individual and promote social unrest. Social media platforms are unable to monitor every topic that is posted by users, so automated detection of hate speech is a crucial tool. Utilizing machine learning models was first popular. But as the dataset size grows, these models are unable to deliver adequate results. While advanced deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Encoder Representations from Transformers (BERT), and similar architectures have demonstrated greater reliability and effectiveness. Toxic speech is a typical thing which is floating around on social media in this fast-paced world where social media is a significant part of our lives and influences the thoughts of many people, where people have access to distribute whatever sort of information they want. Fighting this is difficult because hate speech recognition is now essential in modern society. In order to extract the necessary features and determine if a section of news contains hate speech or not, this article uses a hybrid CNN+LSTM architecture. 2025 IEEE.

Source

Proceedings of the International Conference on Multimedia Information Processing and Retrieval, MIPR;pp.1-6

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

CNN; Deep Learning; Hate speech; Hybrid model; LSTM; Machine Learning; NLP; RNN; Social networks

Coverage

Nanjundan P., Christ University, Department of Data Science, Lavasa, Pune, India; Deokar R., Christ University, Department of Data Science, Lavasa, Pune, India; Ramakrishnan J., College of Engineering and Computer Science, Department of Computer Science, JAZAN University, Saudi Arabia

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 27704327; ISBN: 979-833159465-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/MIPR67560.2025.00008

https://www.scopus.com/pages/publications/105025012240?origin=resultslist