An Empirical Study on the Social Media False Information Dissemination Model Based on AI Confrontation Mechanism

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.