Prediction of self-efficacy in recognizing deepfakes based on personality traits [version 3; peer review: 2 approved]
Background: While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - the realistic-looking videos or images that show people doing or saying things that they neve...
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| Veröffentlicht in: | F1000 research 2022, Vol.11, p.1529-1529 |
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| creator | Abraham, Juneman Putra, Heru Alamsyah Prayoga, Tommy Warnars, Harco Leslie Hendric Spits Manurung, Rudi Hartono Nainggolan, Togiaratua |
| description | Background: While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - the realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual's self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people's personality traits influence their efficacy in recognizing deepfakes?
Methods: Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults.
Results: The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it.
Conclusion: Self-efficacy in spotting deepfakes can be predicted by certain personality traits. |
| doi_str_mv | 10.12688/f1000research.128915.3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10719557</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2902955953</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3593-4f906d2f248a727b40d418e7cafb6e19bbdd9c811922a55ce96973df693b7b8c3</originalsourceid><addsrcrecordid>eNqFkc9uFSEUhydGY5u2r6AkbtxM5c8wgF2YplHbpIkudGUMYeBwS507jDBz9bryjXynPonc3lpbN4YF5PCdLwd-VfWU4ENCWylfeIIxTpDBJHtRalIRfsgeVLsUN21NGkwf3jnvVAc5X5YOrBRrqXhc7TCJlRS83a2-v0_ggp1CHFD0KEPva_A-WGPXKAwogY2LIfwIwwI5gNGbL5BRZzI4VFpGSDkOpg_TGk3JhClf_fyFPq1KeWNkR4WAVCyrAN9eIorMOKa4Avd5v3rkTZ_h4Gbfqz6-ef3h5LQ-f_f27OT4vLaMK1Y3XuHWUU8baQQVXYNdQyQIa3zXAlFd55yykhBFqeHcgmqVYM63inWik5btVa-23nHuluAsDGXOXo8pLE1a62iCvn8zhAu9iCtNsCCKc1EMz28MKX6dIU96GbKFvjcDxDlrqjAtoOKsoM_-QS_jnMr_XFOyLCF5ocSWsinmnMDfTkOwvk5Y30tYbxPWG_-Tu4-57fuTZwGOtoA3du6n9Uaj_3r-o_8NSY65_w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2908080785</pqid></control><display><type>article</type><title>Prediction of self-efficacy in recognizing deepfakes based on personality traits [version 3; peer review: 2 approved]</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>PubMed Central</source><creator>Abraham, Juneman ; Putra, Heru Alamsyah ; Prayoga, Tommy ; Warnars, Harco Leslie Hendric Spits ; Manurung, Rudi Hartono ; Nainggolan, Togiaratua</creator><creatorcontrib>Abraham, Juneman ; Putra, Heru Alamsyah ; Prayoga, Tommy ; Warnars, Harco Leslie Hendric Spits ; Manurung, Rudi Hartono ; Nainggolan, Togiaratua</creatorcontrib><description>Background: While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - the realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual's self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people's personality traits influence their efficacy in recognizing deepfakes?
Methods: Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults.
Results: The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it.
Conclusion: Self-efficacy in spotting deepfakes can be predicted by certain personality traits.</description><identifier>ISSN: 2046-1402</identifier><identifier>EISSN: 2046-1402</identifier><identifier>DOI: 10.12688/f1000research.128915.3</identifier><identifier>PMID: 38098756</identifier><language>eng</language><publisher>England: Faculty of 1000 Ltd</publisher><subject>Age groups ; Anxiety ; Artificial intelligence ; Brief Report ; Cognitive ability ; Cooperation ; Designers ; Emotions ; Generation Z ; Personality ; Personality traits ; Population ; Self report ; Young adults</subject><ispartof>F1000 research, 2022, Vol.11, p.1529-1529</ispartof><rights>Copyright: © 2023 Abraham J et al.</rights><rights>Copyright: © 2023 Abraham J et al. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright: © 2023 Abraham J et al. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3593-4f906d2f248a727b40d418e7cafb6e19bbdd9c811922a55ce96973df693b7b8c3</cites><orcidid>0000-0003-0232-2735</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10719557/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10719557/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,4022,27922,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38098756$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Abraham, Juneman</creatorcontrib><creatorcontrib>Putra, Heru Alamsyah</creatorcontrib><creatorcontrib>Prayoga, Tommy</creatorcontrib><creatorcontrib>Warnars, Harco Leslie Hendric Spits</creatorcontrib><creatorcontrib>Manurung, Rudi Hartono</creatorcontrib><creatorcontrib>Nainggolan, Togiaratua</creatorcontrib><title>Prediction of self-efficacy in recognizing deepfakes based on personality traits [version 3; peer review: 2 approved]</title><title>F1000 research</title><addtitle>F1000Res</addtitle><description>Background: While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - the realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual's self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people's personality traits influence their efficacy in recognizing deepfakes?
Methods: Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults.
Results: The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it.
Conclusion: Self-efficacy in spotting deepfakes can be predicted by certain personality traits.</description><subject>Age groups</subject><subject>Anxiety</subject><subject>Artificial intelligence</subject><subject>Brief Report</subject><subject>Cognitive ability</subject><subject>Cooperation</subject><subject>Designers</subject><subject>Emotions</subject><subject>Generation Z</subject><subject>Personality</subject><subject>Personality traits</subject><subject>Population</subject><subject>Self report</subject><subject>Young adults</subject><issn>2046-1402</issn><issn>2046-1402</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkc9uFSEUhydGY5u2r6AkbtxM5c8wgF2YplHbpIkudGUMYeBwS507jDBz9bryjXynPonc3lpbN4YF5PCdLwd-VfWU4ENCWylfeIIxTpDBJHtRalIRfsgeVLsUN21NGkwf3jnvVAc5X5YOrBRrqXhc7TCJlRS83a2-v0_ggp1CHFD0KEPva_A-WGPXKAwogY2LIfwIwwI5gNGbL5BRZzI4VFpGSDkOpg_TGk3JhClf_fyFPq1KeWNkR4WAVCyrAN9eIorMOKa4Avd5v3rkTZ_h4Gbfqz6-ef3h5LQ-f_f27OT4vLaMK1Y3XuHWUU8baQQVXYNdQyQIa3zXAlFd55yykhBFqeHcgmqVYM63inWik5btVa-23nHuluAsDGXOXo8pLE1a62iCvn8zhAu9iCtNsCCKc1EMz28MKX6dIU96GbKFvjcDxDlrqjAtoOKsoM_-QS_jnMr_XFOyLCF5ocSWsinmnMDfTkOwvk5Y30tYbxPWG_-Tu4-57fuTZwGOtoA3du6n9Uaj_3r-o_8NSY65_w</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Abraham, Juneman</creator><creator>Putra, Heru Alamsyah</creator><creator>Prayoga, Tommy</creator><creator>Warnars, Harco Leslie Hendric Spits</creator><creator>Manurung, Rudi Hartono</creator><creator>Nainggolan, Togiaratua</creator><general>Faculty of 1000 Ltd</general><general>F1000 Research Limited</general><scope>C-E</scope><scope>CH4</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-0232-2735</orcidid></search><sort><creationdate>2022</creationdate><title>Prediction of self-efficacy in recognizing deepfakes based on personality traits [version 3; peer review: 2 approved]</title><author>Abraham, Juneman ; Putra, Heru Alamsyah ; Prayoga, Tommy ; Warnars, Harco Leslie Hendric Spits ; Manurung, Rudi Hartono ; Nainggolan, Togiaratua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3593-4f906d2f248a727b40d418e7cafb6e19bbdd9c811922a55ce96973df693b7b8c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Age groups</topic><topic>Anxiety</topic><topic>Artificial intelligence</topic><topic>Brief Report</topic><topic>Cognitive ability</topic><topic>Cooperation</topic><topic>Designers</topic><topic>Emotions</topic><topic>Generation Z</topic><topic>Personality</topic><topic>Personality traits</topic><topic>Population</topic><topic>Self report</topic><topic>Young adults</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abraham, Juneman</creatorcontrib><creatorcontrib>Putra, Heru Alamsyah</creatorcontrib><creatorcontrib>Prayoga, Tommy</creatorcontrib><creatorcontrib>Warnars, Harco Leslie Hendric Spits</creatorcontrib><creatorcontrib>Manurung, Rudi Hartono</creatorcontrib><creatorcontrib>Nainggolan, Togiaratua</creatorcontrib><collection>F1000Research</collection><collection>Faculty of 1000</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Science Database</collection><collection>Biological Science 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 Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>F1000 research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abraham, Juneman</au><au>Putra, Heru Alamsyah</au><au>Prayoga, Tommy</au><au>Warnars, Harco Leslie Hendric Spits</au><au>Manurung, Rudi Hartono</au><au>Nainggolan, Togiaratua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of self-efficacy in recognizing deepfakes based on personality traits [version 3; 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The first question we need to ask is how good humans are at recognizing deepfakes - the realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual's self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people's personality traits influence their efficacy in recognizing deepfakes?
Methods: Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults.
Results: The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it.
Conclusion: Self-efficacy in spotting deepfakes can be predicted by certain personality traits.</abstract><cop>England</cop><pub>Faculty of 1000 Ltd</pub><pmid>38098756</pmid><doi>10.12688/f1000research.128915.3</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-0232-2735</orcidid><oa>free_for_read</oa></addata></record> |
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| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; PubMed Central |
| subjects | Age groups Anxiety Artificial intelligence Brief Report Cognitive ability Cooperation Designers Emotions Generation Z Personality Personality traits Population Self report Young adults |
| title | Prediction of self-efficacy in recognizing deepfakes based on personality traits [version 3; peer review: 2 approved] |
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