國立虎尾科技大學 |

DeBERTNeXT : = A Multimodal Fake News Detection Framework.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	DeBERTNeXT :/
其他題名:	A Multimodal Fake News Detection Framework.
作者:	Saha, Kamonashish.
面頁冊數:	1 online resource (80 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Web studies. -
電子資源:	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:

1148502
Web studies.
Subjects--Index Terms:

Fakeddit datasetIndex Terms--Genre/Form:

554714
Electronic books.

DeBERTNeXT : = A Multimodal Fake News Detection Framework.
LDR:02472ntm a2200397K 4500 001 1141099
005 20231130111612.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379597856
035 $a (MiAaPQ)AAI30425597
035 $a AAI30425597
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Saha, Kamonashish. $3 1464699
245 1 0 $a DeBERTNeXT : $b A Multimodal Fake News Detection Framework.
264 0 $c 2023
300 $a 1 online resource (80 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Kobti, Ziad.
502 $a Thesis (M.Sc.)--University of Windsor (Canada), 2023.
504 $a Includes bibliographical references
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%.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Web studies. $3 1148502
650 4 $a Mass communications. $3 1335238
650 4 $a Computer science. $3 573171
653 $a Fakeddit dataset
653 $a DeBERTa
653 $a Fake news
653 $a Multi-modal
653 $a Transfer learning
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0646
690 $a 0708
710 2 $a University of Windsor (Canada). $b COMPUTER SCIENCE. $3 1182526
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 84-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30425597 $z click for full text (PQDT)