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DeBERTNeXT : = A Multimodal Fake News Detection Framework.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	DeBERTNeXT :/
Reminder of title:	A Multimodal Fake News Detection Framework.
Author:	Saha, Kamonashish.
Description:	1 online resource (80 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379597856

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

DeBERTNeXT :A Multimodal Fake News Detection Framework. - 1 online resource (80 pages)

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

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

Includes bibliographical references

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%.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379597856Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Fakeddit datasetIndex Terms--Genre/Form:

554714
Electronic books.

DeBERTNeXT : = A Multimodal Fake News Detection Framework.
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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%.
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653 $a DeBERTa
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