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An application study on multimodal fake news detection based on Albert-ResNet50 Model

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Abstract

In today’s interconnected world, where individuals can create and receive information freely, the proliferation of fake news has become a significant issue. This type of false information frequently appears in areas such as business or politics, and its widespread dissemination on the internet can disrupt the normal social order and create a biased net- work atmosphere, ultimately leading to the destruction of the normal network environment. The evolution of fake news, from early plain text to complex images and texts, has made its detection more difficult. To address this, we propose an Albert ResNet50 hybrid deep neural net- work model that combines implicit features of both text and images for detecting multimodal fake news. We tested our model on three fake news datasets, and the results showed an accuracy rate of 90.51%, 79.87%, and 92.93%, respectively. Compared to traditional models that only use text data, our multimodal model can better identify fake news.

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Funding

The paper has been supported by various sources, including the Guangzhou Science and Technology Plan Project (No. 201903010103), the 2021 Guangdong Province Undergraduate College Teaching Quality and Teaching Reform Project Construction Project (Guangdong Higher Education [2021] 29 No.154), the 2021 South China Normal University Quality Engineering Construction Project (Teaching (2021) 72 No. 136), the 16th Batch of General Education Curriculum Construction Projects of South China Normal University (Teaching (2021) 74 No. 10), the Guangzhou Philosophy and Social Science Planning 2022 Annual Project (2022GZYB66), the 2022 South China Normal University Quality Engineering Construction project (Teaching [2022] 41 No.109 & 143), the Science and Technology Plan Project of Guangdong Provincial Department of Communications (NO.

2015–02-064). The project also received support from the 2022”Challenge Cup” Gold Seed Cultivation Project of South China Normal University, as well as two general topics of students’ extracurricular scientific research projects:”Auxiliary Diagnosis and Drug Recommendation of Diabetes based on Medical Knowledge Graph”.

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Author notes

  1. Mingyue Jiang and Chang Jing contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. School of Electronics and Information Engineering, Faculty of Engineering South, China Normal University Nanhai, Guangzhou, China

    Mingyue Jiang & Liming Chen

  2. Institute of Intelligent Information Processing South China Business College, Guangdong University of Foreign Studies Guangzhou, Guangzhou, China

    Chang Jing

  3. School of Physics and Telecommunications Engineering South, China Normal University Guangzhou, Guangzhou, China

    Yang Wang

  4. School of Artificial Intelligence, Faculty of Engineering South, China Normal University Nanhai, Guangzhou, China

    Shouqiang Liu

Authors
  1. Mingyue Jiang
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  2. Chang Jing
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  3. Liming Chen
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  4. Yang Wang
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  5. Shouqiang Liu
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Contributions

All authors contributed to the research, experiment and manuscript. Mingyue Jiang,Chang Jing and Shouqiang Liu were responsible for the design and the preparation of the experiment. The expriment and related discussion were performed by Mingyue Jiang, Chang Jing, Liming Chen, Yang Wang and Shouqiang Liu.

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Correspondence to Shouqiang Liu.

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Jiang, M., Jing, C., Chen, L. et al. An application study on multimodal fake news detection based on Albert-ResNet50 Model. Multimed Tools Appl 83, 8689–8706 (2024). https://doi.org/10.1007/s11042-023-15741-y

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  • DOI: https://doi.org/10.1007/s11042-023-15741-y

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