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MULTIMODAL FAKE NEWS DETECTION

In recent years, social media has increasingly become one of the popular ways for people to consume news. As proliferation of fake news on social media has the negative impacts on individuals and society, automatic fake news detection has been explored by different research communities for combati...

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Main Author: PSNZ
Format: Article
Language:English
Published: Universiti Malaysia Terengganu 2024
Subjects:
Fake news detection
Crossmodal attention
Residual network
Convolutional neural network
Online Access:http://umt-ir.umt.edu.my:8080/handle/123456789/20680
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spelling my.umt.ir-206802024-08-21T15:25:35Z MULTIMODAL FAKE NEWS DETECTION PSNZ Fake news detection Crossmodal attention Residual network Convolutional neural network In recent years, social media has increasingly become one of the popular ways for people to consume news. As proliferation of fake news on social media has the negative impacts on individuals and society, automatic fake news detection has been explored by different research communities for combating fake news. With the development of multimedia technology, there is a phenomenon that cannot be ignored is that more and more social media news contains information with different modalities, e.g., texts, pictures and videos. The multiple information modalities show more evidence of the happening of news events and present new opportunities to detect features in fake news. First, for multimodal fake news detection task, it is a challenge of keeping the unique properties for each modality while fusing the relevant information between different modalities. Second, for some news, the information fusion between different modalities may produce the noise information which affects model’s performance. Unfortunately, existing methods fail to handle these challenges. To address these problems, we propose a multimodal fake news detection framework based on Crossmodal Attention Residual and Multichannel convolutional neural Networks (CARMN). The Crossmodal Attention Residual Network (CARN) can selectively extract the relevant information related to a target modality from another source modality while maintaining the unique information of the target modality. The Multichannel Convolutional neural Network (MCN) can mitigate the influence of noise information which may be generated by crossmodal fusion component by extracting textual feature representation from original and fused textual information simultaneously. We conduct extensive experiments on four real-world datasets and demonstrate that the proposed model outperforms the state-of-the-art methods and learns more discriminable feature representations. 2024-08-21T15:25:31Z 2024-08-21T15:25:31Z 2024-08 Article http://umt-ir.umt.edu.my:8080/handle/123456789/20680 en application/pdf Universiti Malaysia Terengganu
institution Universiti Malaysia Terengganu
building Perpustakaan Sultanah Nur Zahirah
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Terengganu
content_source UMT-IR
url_provider http://umt-ir.umt.edu.my:8080/
language English
topic Fake news detection
Crossmodal attention
Residual network
Convolutional neural network
spellingShingle Fake news detection
Crossmodal attention
Residual network
Convolutional neural network
PSNZ
MULTIMODAL FAKE NEWS DETECTION
description In recent years, social media has increasingly become one of the popular ways for people to consume news. As proliferation of fake news on social media has the negative impacts on individuals and society, automatic fake news detection has been explored by different research communities for combating fake news. With the development of multimedia technology, there is a phenomenon that cannot be ignored is that more and more social media news contains information with different modalities, e.g., texts, pictures and videos. The multiple information modalities show more evidence of the happening of news events and present new opportunities to detect features in fake news. First, for multimodal fake news detection task, it is a challenge of keeping the unique properties for each modality while fusing the relevant information between different modalities. Second, for some news, the information fusion between different modalities may produce the noise information which affects model’s performance. Unfortunately, existing methods fail to handle these challenges. To address these problems, we propose a multimodal fake news detection framework based on Crossmodal Attention Residual and Multichannel convolutional neural Networks (CARMN). The Crossmodal Attention Residual Network (CARN) can selectively extract the relevant information related to a target modality from another source modality while maintaining the unique information of the target modality. The Multichannel Convolutional neural Network (MCN) can mitigate the influence of noise information which may be generated by crossmodal fusion component by extracting textual feature representation from original and fused textual information simultaneously. We conduct extensive experiments on four real-world datasets and demonstrate that the proposed model outperforms the state-of-the-art methods and learns more discriminable feature representations.
format Article
author PSNZ
author_facet PSNZ
author_sort PSNZ
title MULTIMODAL FAKE NEWS DETECTION
title_short MULTIMODAL FAKE NEWS DETECTION
title_full MULTIMODAL FAKE NEWS DETECTION
title_fullStr MULTIMODAL FAKE NEWS DETECTION
title_full_unstemmed MULTIMODAL FAKE NEWS DETECTION
title_sort multimodal fake news detection
publisher Universiti Malaysia Terengganu
publishDate 2024
url http://umt-ir.umt.edu.my:8080/handle/123456789/20680
_version_ 1809142425326714880
score 13.222552

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