An inter-modal attention-based deep learning framework using unified modality for multimodal fake news, hate speech and offensive language detection-Reference-Cited by-同舟云学术

An inter-modal attention-based deep learning framework using unified modality for multimodal fake news, hate speech and offensive language detection

Published:2024-07 Issue: Volume:123 Page:102378
ISSN:0306-4379
Container-title:Information Systems
language:en
Short-container-title:Information Systems

Author:

Ayetiran Eniafe Festus^ORCID,Özgöbek Özlem^ORCID

Funder

European Research Consortium for Informatics and Mathematics

Publisher

Elsevier BV

Reference50 articles.

1. The battle against online harmful information: The cases of fake news and hate speech;Giachanou,2020

2. The role of fake news in fueling hate speech and extremism online; promoting adequate measures for tackling the phenomenon;European Foundation for South Asian Studies,2021

3. MVAE: multimodal variational autoencoder for fake news detection;Khattar,2019

4. Multimodal meme dataset (multiOFF) for identifying offensive content in image and text;Suryawanshi,2020

5. SAFE: similarity-aware multi-modal fake news detection;Zhou,2020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring Multimodal Sentiment Analysis Models: A Comprehensive Survey;2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR);2024-08-15

2. Early identification of high attention content for online mental health community users based on multi-level fusion model;Data Technologies and Applications;2024-07-12

3. A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection;IEEE Access;2024

4. A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection;2024