| 标题 |
 QMFND: A quantum multimodal fusion-based fake news detection model for social media QMFND：基于量子多模态融合的社交媒体假新闻检测模型
相关领域
计算机科学
判别式
社会化媒体
稳健性（进化）
卷积神经网络
八卦
假新闻
人工智能
噪音（视频）
机器学习
互联网隐私
万维网
图像（数学）
心理学
社会心理学
生物化学
化学
基因
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| 网址 | |
| DOI |
10.2139/ssrn.5066252
doi
提醒：求助人提供的doi与AI识别不一致
10.1016/j.inffus.2023.102172
Doi
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| 其它 |
Abstract Fake news is frequently disseminated through social media, which significantly impacts public perception and individual decision-making. Accurate identification of fake news on social media is usually time-consuming, laborious, and difficult. Although the leveraging of machine learning technologies can facilitate automated authenticity checks, the time-sensitive and voluminous nature of the data brings considerable challenge for fake news detection. To address this issue, this paper proposes a quantum multimodal fusion-based model for fake news detection (QMFND). QMFND integrates the extracted images and textual features, and passes them through a proposed quantum convolutional neural network (QCNN) to obtain discriminative results. By testing QMFND on two social media datasets, Gossip and Politifact, it is proved that its detection performance is equal to or even surpasses that of classical models. |
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