Fake News Detection Using BERT-VGG19 Multimodal Variational Autoencoder
In this era of readily accessible Internet, there has been a monumental shift in the way information is created, processed and disseminated to the netizens. Moreover, social media has played a very vital role where users can not only interact with one another and share information but also have the...
Uložené v:
| Vydané v: | IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (Online) s. 1 - 5 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
11.11.2021
|
| Predmet: | |
| ISSN: | 2687-7767 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | In this era of readily accessible Internet, there has been a monumental shift in the way information is created, processed and disseminated to the netizens. Moreover, social media has played a very vital role where users can not only interact with one another and share information but also have the capability to influence the thought process of others through their content. One of the major drawbacks of these platforms remains the absence of credibility in the information being circulated and this inherent vulnerability is exploited by many to circulate fake news over these platforms. This falsehood not only jeopardises the credibility of information and the platform itself but is also a growing technological mess simply because fake news spreads much more rapidly and has the capacity to even cause unrest, discontent and misery among the masses. We propose a BERT and VGG19 based multi-modal variational autoencoder for fake news detection. Our proposed model combines the information present in text and image modality to obtain better discriminatory power. The model takes both text and image data of fake news and extract textual feature and visual feature of the News and process both the feature simultaneously into variational autoencoder so the purposed model is call as multi-model variational autoencoder. Specifically, Bert and VGG19 embeddings are obtained for text and image modalities respectively after which the two embeddings are concatenated and passed through a multi-modal variational autoencoder for obtaining the shared latent representation. The shared latent representation so obtained is then fed to a binary classifier that outputs a probability that the input is fake. Our proposed model gives state of the art results on MediaEval2015 data set (with a 0.924 f-score) and remains competitive with state of the art approaches on Weibo dataset (0.656 f-score). |
|---|---|
| AbstractList | In this era of readily accessible Internet, there has been a monumental shift in the way information is created, processed and disseminated to the netizens. Moreover, social media has played a very vital role where users can not only interact with one another and share information but also have the capability to influence the thought process of others through their content. One of the major drawbacks of these platforms remains the absence of credibility in the information being circulated and this inherent vulnerability is exploited by many to circulate fake news over these platforms. This falsehood not only jeopardises the credibility of information and the platform itself but is also a growing technological mess simply because fake news spreads much more rapidly and has the capacity to even cause unrest, discontent and misery among the masses. We propose a BERT and VGG19 based multi-modal variational autoencoder for fake news detection. Our proposed model combines the information present in text and image modality to obtain better discriminatory power. The model takes both text and image data of fake news and extract textual feature and visual feature of the News and process both the feature simultaneously into variational autoencoder so the purposed model is call as multi-model variational autoencoder. Specifically, Bert and VGG19 embeddings are obtained for text and image modalities respectively after which the two embeddings are concatenated and passed through a multi-modal variational autoencoder for obtaining the shared latent representation. The shared latent representation so obtained is then fed to a binary classifier that outputs a probability that the input is fake. Our proposed model gives state of the art results on MediaEval2015 data set (with a 0.924 f-score) and remains competitive with state of the art approaches on Weibo dataset (0.656 f-score). |
| Author | Singh, Upendra Pratap Jaiswal, Ramji Singh, Krishna Pratap |
| Author_xml | – sequence: 1 givenname: Ramji surname: Jaiswal fullname: Jaiswal, Ramji email: mit2019106@iiita.ac.in organization: IIIT Allahabad,MLO Lab,Department of IT,Prayagraj,UP,India – sequence: 2 givenname: Upendra Pratap surname: Singh fullname: Singh, Upendra Pratap email: rsi2017001@iiita.ac.in organization: IIIT Allahabad,MLO Lab,Department of IT,Prayagraj,UP,India – sequence: 3 givenname: Krishna Pratap surname: Singh fullname: Singh, Krishna Pratap email: kpsingh@iiita.ac.in organization: IIIT Allahabad,MLO Lab,Department of IT,Prayagraj,UP,India |
| BookMark | eNotj8FOg0AURUejibXyBS7kB8CZAd6bWVZs0aS2xpRum5nhYUYpGKBp_Hs1dnXP4uQk95pdtF1LjN0JHgvB9X35mq9XmZSYxJJLEWsABJGesUCjEgBZyjVIfc4mEhRGiIBXLBiGD855InmCHCesWJhPCld0HMJHGsmNvmvDcvDte_gwf9tE26IQOnw5NKPfd5Vpwq3pvfmzfnl2GDtqXVdRf8Mua9MMFJx2ysrFfJM_Rct18ZzPlpEXQo1R5TBRurLKoskwRVJVZp0DjWB1nWmq0QLnpqpJgJSCalNbh5kVaDUom0zZ7X_XE9Huq_d703_vTteTH8_ST7Y |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/UPCON52273.2021.9667614 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781665409629 1665409622 |
| EISSN | 2687-7767 |
| EndPage | 5 |
| ExternalDocumentID | 9667614 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK OCL RIE RIL |
| ID | FETCH-LOGICAL-i118t-dc7389db8b7a5747e8d5bcc6976b9f59ef7b600adfe16221efafbc75b17b968b3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 27 02:36:12 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i118t-dc7389db8b7a5747e8d5bcc6976b9f59ef7b600adfe16221efafbc75b17b968b3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_9667614 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-Nov.-11 |
| PublicationDateYYYYMMDD | 2021-11-11 |
| PublicationDate_xml | – month: 11 year: 2021 text: 2021-Nov.-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (Online) |
| PublicationTitleAbbrev | UPCON |
| PublicationYear | 2021 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003203707 |
| Score | 1.8173769 |
| Snippet | In this era of readily accessible Internet, there has been a monumental shift in the way information is created, processed and disseminated to the netizens.... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Bit error rate Blogs Data models Decoder Encoder Fake News Feature extraction Latent Representation Modality Natural languages Social networking (online) Variational Autoencoder Visualization |
| Title | Fake News Detection Using BERT-VGG19 Multimodal Variational Autoencoder |
| URI | https://ieeexplore.ieee.org/document/9667614 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA21ePCk0orf5ODRtJuN2WyOWtt6qkXa0lvJxwSKuit1199vki4VwYu3sBDCzsLkvdn3ZhC6ybUzjsEdcZIbEhA0kZZxwqlJjMlY6BYQh02IySRfLuW0hW53XhgAiOIz6IVl_JdvS1OHUllfBkFmmFq9J0S29Wrt6iksTZhIRCPhoonsz6eD54mHF4J5GpjSXrP71xiVeIuMDv93_hHq_tjx8HR30RyjFhQdNB6pV8AhR-FHqKKgqsBRAIAfhi8zshiPqcTRX_teWvWGF54VN5U_fF9XZehgaWHTRfPRcDZ4Is1UBLL2ZKAi1ggPMqzOtVDckwHILdc-rB5XaOm4BCe0RzHKOqBZmlJwymkjuKZCyyzX7AS1i7KAU4Sp5hastIqCZ4WgNEifOD0gyqykLldnqBOCsPrYNr5YNe9__vfjC3QQ4hyMepReona1qeEK7Zuvav25uY5f6xvIIpX8 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dS8MwFA1DBX1S2cRv8-Cj3Zp2aZpHnfsQZx2yjb2NfNzAUFuZnb_fJCsTwRffQiGQ3sLNObfn3IvQdSqNMjG0A8OpChyCDriOaUCJCpVKYtctwA-bYFmWzmZ8VEM3Gy8MAHjxGTTd0v_L14VauVJZiztBpptavU3b7Shcu7U2FZU4CmMWskrERULemow6z5kFGCy2RDAizWr_r0Eq_h7p7f_vBAeo8WPIw6PNVXOIapDXUb8nXgG7LIXvofSSqhx7CQC-676Mg2m_Tzj2Dtv3Qos3PLW8uKr94dtVWbgelhqWDTTpdcedQVDNRQgWlg6UgVbMwgwtU8kEtXQAUk2lDaxFFpIbysEwaXGM0AZIEkUEjDBSMSoJkzxJZXyEtvIih2OEiaQaNNeCgOWFICRwmzotJEo0JyYVJ6jugjD_WLe-mFfvf_r34yu0Oxg_DefDh-zxDO25mDvbHiHnaKtcruAC7aivcvG5vPRf7hvGsplD |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE+Uttar+Pradesh+Section+International+Conference+on+Electrical%2C+Computer+and+Electronics+%28Online%29&rft.atitle=Fake+News+Detection+Using+BERT-VGG19+Multimodal+Variational+Autoencoder&rft.au=Jaiswal%2C+Ramji&rft.au=Singh%2C+Upendra+Pratap&rft.au=Singh%2C+Krishna+Pratap&rft.date=2021-11-11&rft.pub=IEEE&rft.eissn=2687-7767&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FUPCON52273.2021.9667614&rft.externalDocID=9667614 |