An Empirical Study on the Social Media False Information Dissemination Model Based on AI Confrontation Mechanism.
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| Title: | An Empirical Study on the Social Media False Information Dissemination Model Based on AI Confrontation Mechanism. |
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| Authors: | WANG, Zhongzi1, JI, Cheng2 jicheng@xynu.edu.cn |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p896-908. 13p. |
| Subjects: | Misinformation, Adversarial machine learning, Emotional state, Social media, Risk perception, Information-seeking behavior |
| Abstract: | This article takes false information in social media as a research case to explore the influence mechanism and action path of users' information verification behavior, aiming to enhance the public's ability to distinguish online information and promote the self-purification of the online environment. This study, based on the Fine Processing Possibility Model (ELM), combined with questionnaire surveys and structural equation models, constructs a model of the influence mechanism of social media users' false information verification behavior, and conducts empirical tests on the relevant influencing factors. This research can provide a reference for the management of social media platforms and the prevention of false information by users. Using only one modal discriminator cannot fully exploit the invariance between modalities, thus limiting the accuracy of cross-modal retrieval. This study innovatively constructs a cross-modal feature alignment framework based on dual-channel all-modal encoders and adversarial learning. This model adopts a parallel architecture design, featuring two feature encoding channels: visual and text. It maps heterogeneous data to a unified representation space through a deep neural network. During the model optimization process, we embed a classification and discrimination module in the hidden layer of the feature extraction network and adopt an adversarial training strategy for multi-objective optimization, so that the obtained shared feature space simultaneously possesses: 1) cross-modal semantic consistency; 2) Modal invariant feature expression ability. The experimental results show that an individual's emotional tendency and risk perception level can significantly affect their information discrimination behavior patterns. These two psychological factors constitute the core dimensions of users' information verification decisions. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131775 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Empirical Study on the Social Media False Information Dissemination Model Based on AI Confrontation Mechanism. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22WANG%2C+Zhongzi%22">WANG, Zhongzi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22JI%2C+Cheng%22">JI, Cheng</searchLink><relatesTo>2</relatesTo><i> jicheng@xynu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p896-908. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Misinformation%22">Misinformation</searchLink><br /><searchLink fieldCode="DE" term="%22Adversarial+machine+learning%22">Adversarial machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+state%22">Emotional state</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+perception%22">Risk perception</searchLink><br /><searchLink fieldCode="DE" term="%22Information-seeking+behavior%22">Information-seeking behavior</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article takes false information in social media as a research case to explore the influence mechanism and action path of users' information verification behavior, aiming to enhance the public's ability to distinguish online information and promote the self-purification of the online environment. This study, based on the Fine Processing Possibility Model (ELM), combined with questionnaire surveys and structural equation models, constructs a model of the influence mechanism of social media users' false information verification behavior, and conducts empirical tests on the relevant influencing factors. This research can provide a reference for the management of social media platforms and the prevention of false information by users. Using only one modal discriminator cannot fully exploit the invariance between modalities, thus limiting the accuracy of cross-modal retrieval. This study innovatively constructs a cross-modal feature alignment framework based on dual-channel all-modal encoders and adversarial learning. This model adopts a parallel architecture design, featuring two feature encoding channels: visual and text. It maps heterogeneous data to a unified representation space through a deep neural network. During the model optimization process, we embed a classification and discrimination module in the hidden layer of the feature extraction network and adopt an adversarial training strategy for multi-objective optimization, so that the obtained shared feature space simultaneously possesses: 1) cross-modal semantic consistency; 2) Modal invariant feature expression ability. The experimental results show that an individual's emotional tendency and risk perception level can significantly affect their information discrimination behavior patterns. These two psychological factors constitute the core dimensions of users' information verification decisions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20250929003031 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 896 Subjects: – SubjectFull: Misinformation Type: general – SubjectFull: Adversarial machine learning Type: general – SubjectFull: Emotional state Type: general – SubjectFull: Social media Type: general – SubjectFull: Risk perception Type: general – SubjectFull: Information-seeking behavior Type: general Titles: – TitleFull: An Empirical Study on the Social Media False Information Dissemination Model Based on AI Confrontation Mechanism. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: WANG, Zhongzi – PersonEntity: Name: NameFull: JI, Cheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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