Improving Data Fusion for Fake News Detection: A Hybrid Fusion Approach for Unimodal and Multimodal Data-Reference-Cited by-同舟云学术

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Improving Data Fusion for Fake News Detection: A Hybrid Fusion Approach for Unimodal and Multimodal Data

Published:2024 Issue: Volume:12 Page:112412-112425
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hamed Suhaib Kh.¹^ORCID,Juzaiddin Ab Aziz Mohd¹,Ridzwan Yaakub Mohd²^ORCID

Affiliation:

1. Center for Software Technology and Management (SOFTAM), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia

2. Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia

Funder

Universiti Kebangsaan Malaysia (UKM) under Geran Skim Insentif Penerbitan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10634465.pdf?arnumber=10634465

Reference55 articles.

1. Comparison of Fake News Detection using Machine Learning and Deep Learning Techniques

2. Deep learning for misinformation detection on online social networks: a survey and new perspectives

3. Detecting breaking news rumors of emerging topics in social media

4. Impact of Google searches and social media on digital assets’ volatility

5. Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

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