Deepfake tweets automatic detection

Deepfake tweets automatic detection

This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2024-06
Hauptverfasser: Frej, Adam, Kaminski, Adrian, Marciniak, Piotr, Szmajdzinski, Szymon, Kuntur, Soveatin, Wroblewska, Anna
Format: Artikel
Sprache:eng
Schlagworte:
Deception
Machine learning
Natural language processing
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Frej, Adam
Kaminski, Adrian
Marciniak, Piotr
Szmajdzinski, Szymon
Kuntur, Soveatin
Wroblewska, Anna
description This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models. The objective is to identify effective strategies for recognizing DeepFake content, thereby enhancing the integrity of digital communications. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3072057594</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3072057594</sourcerecordid><originalsourceid>FETCH-proquest_journals_30720575943</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mRQdklNLUhLzE5VKClPTS0pVkgsLcnPTSzJTFZISS1JTS7JzM_jYWBNS8wpTuWF0twMym6uIc4eugVF-YWlqcUl8Vn5pUV5QKl4YwNzIwNTc1NLE2PiVAEAuDsukw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3072057594</pqid></control><display><type>article</type><title>Deepfake tweets automatic detection</title><source>Free E- Journals</source><creator>Frej, Adam ; Kaminski, Adrian ; Marciniak, Piotr ; Szmajdzinski, Szymon ; Kuntur, Soveatin ; Wroblewska, Anna</creator><creatorcontrib>Frej, Adam ; Kaminski, Adrian ; Marciniak, Piotr ; Szmajdzinski, Szymon ; Kuntur, Soveatin ; Wroblewska, Anna</creatorcontrib><description>This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models. The objective is to identify effective strategies for recognizing DeepFake content, thereby enhancing the integrity of digital communications. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Deception ; Machine learning ; Natural language processing</subject><ispartof>arXiv.org, 2024-06</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,780</link.rule.ids></links><search><creatorcontrib>Frej, Adam</creatorcontrib><creatorcontrib>Kaminski, Adrian</creatorcontrib><creatorcontrib>Marciniak, Piotr</creatorcontrib><creatorcontrib>Szmajdzinski, Szymon</creatorcontrib><creatorcontrib>Kuntur, Soveatin</creatorcontrib><creatorcontrib>Wroblewska, Anna</creatorcontrib><title>Deepfake tweets automatic detection</title><title>arXiv.org</title><description>This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models. The objective is to identify effective strategies for recognizing DeepFake content, thereby enhancing the integrity of digital communications. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.</description><subject>Deception</subject><subject>Machine learning</subject><subject>Natural language processing</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mRQdklNLUhLzE5VKClPTS0pVkgsLcnPTSzJTFZISS1JTS7JzM_jYWBNS8wpTuWF0twMym6uIc4eugVF-YWlqcUl8Vn5pUV5QKl4YwNzIwNTc1NLE2PiVAEAuDsukw</recordid><startdate>20240624</startdate><enddate>20240624</enddate><creator>Frej, Adam</creator><creator>Kaminski, Adrian</creator><creator>Marciniak, Piotr</creator><creator>Szmajdzinski, Szymon</creator><creator>Kuntur, Soveatin</creator><creator>Wroblewska, Anna</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240624</creationdate><title>Deepfake tweets automatic detection</title><author>Frej, Adam ; Kaminski, Adrian ; Marciniak, Piotr ; Szmajdzinski, Szymon ; Kuntur, Soveatin ; Wroblewska, Anna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_30720575943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Deception</topic><topic>Machine learning</topic><topic>Natural language processing</topic><toplevel>online_resources</toplevel><creatorcontrib>Frej, Adam</creatorcontrib><creatorcontrib>Kaminski, Adrian</creatorcontrib><creatorcontrib>Marciniak, Piotr</creatorcontrib><creatorcontrib>Szmajdzinski, Szymon</creatorcontrib><creatorcontrib>Kuntur, Soveatin</creatorcontrib><creatorcontrib>Wroblewska, Anna</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Frej, Adam</au><au>Kaminski, Adrian</au><au>Marciniak, Piotr</au><au>Szmajdzinski, Szymon</au><au>Kuntur, Soveatin</au><au>Wroblewska, Anna</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Deepfake tweets automatic detection</atitle><jtitle>arXiv.org</jtitle><date>2024-06-24</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models. The objective is to identify effective strategies for recognizing DeepFake content, thereby enhancing the integrity of digital communications. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-06
issn 2331-8422
language eng
recordid cdi_proquest_journals_3072057594
source Free E- Journals
subjects Deception
Machine learning
Natural language processing
title Deepfake tweets automatic detection
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T22%3A19%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Deepfake%20tweets%20automatic%20detection&rft.jtitle=arXiv.org&rft.au=Frej,%20Adam&rft.date=2024-06-24&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3072057594%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3072057594&rft_id=info:pmid/&rfr_iscdi=true