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DeBERTNeXT: A Multimodal Fake News D...

Saha, Kamonashish.

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DeBERTNeXT: A Multimodal Fake News Detection Framework.

Record Type:	Electronic resources : Monograph/item
Title/Author:	DeBERTNeXT: A Multimodal Fake News Detection Framework./
Author:	Saha, Kamonashish.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	80 p.
Notes:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30425597
ISBN:	9798379597856

DeBERTNeXT: A Multimodal Fake News Detection Framework.
Saha, Kamonashish.

DeBERTNeXT: A Multimodal Fake News Detection Framework. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 80 p.

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.Sc.)--University of Windsor (Canada), 2023.

This item must not be sold to any third party vendors.

There is a rapid influx of fake news nowadays, which poses an immense threat to our society. Fake news has been impacting us in several ways which include changing our thoughts, manipulating opinions, and also causing chaos due to misinformation. With the ease of access and sharing information on social media platforms, such fake news or misinformation has been spreading in different modalities which include text, image, audio, and video. Although there have been a lot of approaches to detecting fake news in textual format only, however, multimodal approaches are less frequent as it is difficult to fully use the information derived from different modalities to achieve high accuracy in a combined format. To tackle these issues, we introduce DeBertNeXT which is a multimodal fake news detection model that utilizes both textual and visual information from an article for fake news classification. We perform experiments on the immense Fakeddit dataset and two other smaller benchmark datasets named Politifact and Gossipcop. Our model outperforms the existing models on the Fakeddit dataset by about 3.80%, Politifact by 2.10% and Gossipcop by 1.00%.

ISBN: 9798379597856Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Fakeddit dataset

DeBERTNeXT: A Multimodal Fake News Detection Framework.
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245 1 0 $a DeBERTNeXT: A Multimodal Fake News Detection Framework.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 80 p.
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Kobti, Ziad.
502 $a Thesis (M.Sc.)--University of Windsor (Canada), 2023.
506 $a This item must not be sold to any third party vendors.
520 $a There is a rapid influx of fake news nowadays, which poses an immense threat to our society. Fake news has been impacting us in several ways which include changing our thoughts, manipulating opinions, and also causing chaos due to misinformation. With the ease of access and sharing information on social media platforms, such fake news or misinformation has been spreading in different modalities which include text, image, audio, and video. Although there have been a lot of approaches to detecting fake news in textual format only, however, multimodal approaches are less frequent as it is difficult to fully use the information derived from different modalities to achieve high accuracy in a combined format. To tackle these issues, we introduce DeBertNeXT which is a multimodal fake news detection model that utilizes both textual and visual information from an article for fake news classification. We perform experiments on the immense Fakeddit dataset and two other smaller benchmark datasets named Politifact and Gossipcop. Our model outperforms the existing models on the Fakeddit dataset by about 3.80%, Politifact by 2.10% and Gossipcop by 1.00%.
590 $a School code: 0115.
650 4 $a Computer science. $3 523869
650 4 $a Mass communications. $3 3422380
650 4 $a Web studies. $3 2122754
653 $a Fakeddit dataset
653 $a DeBERTa
653 $a Fake news
653 $a Multi-modal
653 $a Transfer learning
690 $a 0984
690 $a 0646
690 $a 0708
710 2 $a University of Windsor (Canada). $b COMPUTER SCIENCE. $3 2093677
773 0 $t Masters Abstracts International $g 84-12.
790 $a 0115
791 $a M.Sc.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30425597