Cross-Modal Attention Network for Detecting Multimodal Misinformation From Multiple Platforms-Reference-Cited by-同舟云学术

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Cross-Modal Attention Network for Detecting Multimodal Misinformation From Multiple Platforms

Published:2024-08 Issue:4 Volume:11 Page:4920-4933
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Guo Zhiwei¹^ORCID,Li Yang¹^ORCID,Yang Zhenguo¹^ORCID,Li Xiaoping¹^ORCID,Lee Lap-Kei²^ORCID,Li Qing³^ORCID,Liu Wenyin⁴^ORCID

Affiliation:

1. School of Computer Science, Guangdong University of Technology, Guangzhou, China

2. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

4. Zhongguancun Laboratory, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Hong Kong Research Grants Council through the General Research Fund

RGC of the HKSAR, China

Open Fund of Key Laboratory of Computing Power Network and Information Security, Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6570650/10638068/10478449.pdf?arnumber=10478449

Reference55 articles.

1. Disinformation on the Web

2. WELFake: Word Embedding Over Linguistic Features for Fake News Detection

3. Misinformation in Social Media

4. This Just In: Fake News Packs A Lot In Title, Uses Simpler, Repetitive Content in Text Body, More Similar To Satire Than Real News

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