IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v348y2025i1d10.1007_s10479-022-05154-9.html
   My bibliography  Save this article

MRDCA: a multimodal approach for fine-grained fake news detection through integration of RoBERTa and DenseNet based upon fusion mechanism of co-attention

Author

Listed:
  • Lingfei Qian

    (Nanjing University of Aeronautics and Astronautics)

  • Ruipeng Xu

    (Nanjing University of Aeronautics and Astronautics)

  • Zhipeng Zhou

    (Nanjing University of Aeronautics and Astronautics)

Abstract

Being widely produced for misleading and convincing public community with biased information, various fake news has a significantly negative influence on the society as a whole. In order for effective detection of fine-grained fake news, this study developed a multimodal approach integrating RoBERTa with DenseNet through fusion mechanism of co-attention (MRDCA). RoBERTa was employed for extracting text features, and DenseNet was employed for extracting image features. The co-attention mechanism had the advantage of dynamically learning and capturing information interaction between text and image modal features. Based upon the multimodal fine-grained fake news dataset, the model of MRDCA had a higher value for all the indicators of accuracy (88.14%), macro average precision (87.16%), macro average recall (87.94%), and macro average F1 score (87.51%), comparing to unimodal approaches and other multimodal approaches through feature fusion of concatenation. More specifically, there was the unbalanced performance for MRDCA in detecting different classes of fake news. Experimental results demonstrated that the MRDCA performed better in identifying manipulated content, false connection and true than in identifying imposter content, misleading content and satire/parody. Therefore, the task of classifying samples into misleading content, imposter content and satire/parody was extremely challenging. There ought to be much room for performance promotion in detecting the three classes of fake news in future.

Suggested Citation

  • Lingfei Qian & Ruipeng Xu & Zhipeng Zhou, 2025. "MRDCA: a multimodal approach for fine-grained fake news detection through integration of RoBERTa and DenseNet based upon fusion mechanism of co-attention," Annals of Operations Research, Springer, vol. 348(1), pages 257-278, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-022-05154-9
    DOI: 10.1007/s10479-022-05154-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-05154-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-05154-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Patacconi, Andrea & Vikander, Nick, 2015. "A model of public opinion management," Journal of Public Economics, Elsevier, vol. 128(C), pages 73-83.
    2. Shannon M. Fast & Louis Kim & Emily L. Cohn & Sumiko R. Mekaru & John S. Brownstein & Natasha Markuzon, 2018. "Predicting social response to infectious disease outbreaks from internet-based news streams," Annals of Operations Research, Springer, vol. 263(1), pages 551-564, April.
    3. Efsun Kürüm & Gerhard-Wilhelm Weber & Cem Iyigun, 2018. "Early warning on stock market bubbles via methods of optimization, clustering and inverse problems," Annals of Operations Research, Springer, vol. 260(1), pages 293-320, January.
    4. Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Contessi, Silvio & De Pace, Pierangelo & Guidolin, Massimo, 2020. "Mildly explosive dynamics in U.S. fixed income markets," European Journal of Operational Research, Elsevier, vol. 287(2), pages 712-724.
    2. Huosong Xia & Jing Liu & Zuopeng Justin Zhang, 2024. "Identifying Fintech risk through machine learning: analyzing the Q&A text of an online loan investment platform," Annals of Operations Research, Springer, vol. 333(2), pages 579-599, February.
    3. Angelo Corallo & Fabrizio Errico & Laura Fortunato & Alessandra Spennato & Cristina Blasi, 2024. "Effects Influence of Social Media Constructs on Shopping: An Empirical Study on the Prediction of Retail Clothing Sales," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 18257-18285, December.
    4. Francisco de Arriba-P'erez & Silvia Garc'ia-M'endez & Jos'e A. Regueiro-Janeiro & Francisco J. Gonz'alez-Casta~no, 2024. "Detection of financial opportunities in micro-blogging data with a stacked classification system," Papers 2404.07224, arXiv.org.
    5. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    6. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    7. Wang, Wei & Guo, Lihuan & Wu, Yenchun Jim, 2022. "The merits of a sentiment analysis of antecedent comments for the prediction of online fundraising outcomes," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    8. Gurjeet Dhesi & Bilal Shakeel & Marcel Ausloos, 2021. "Modelling and forecasting the kurtosis and returns distributions of financial markets: irrational fractional Brownian motion model approach," Annals of Operations Research, Springer, vol. 299(1), pages 1397-1410, April.
    9. Jose Ramon Saura & Domingo Ribeiro-Soriano & Daniel Palacios-Marqués, 2024. "Data-driven strategies in operation management: mining user-generated content in Twitter," Annals of Operations Research, Springer, vol. 333(2), pages 849-869, February.
    10. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    11. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
    12. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography," Annals of Operations Research, Springer, vol. 341(1), pages 173-195, October.
    13. M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
    14. Foerster, Manuel & Voss, Achim, 2022. "Believe me, I am ignorant, but not biased," European Economic Review, Elsevier, vol. 149(C).
    15. Baranowski Paweł & Korczak Karol & Zając Jarosław, 2020. "Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland," Business Systems Research, Sciendo, vol. 11(1), pages 73-88, March.
    16. Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024. "Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages," Papers 2404.08665, arXiv.org.
    17. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
    18. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
    19. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
    20. Serge Nyawa & Dieudonné Tchuente & Samuel Fosso-Wamba, 2024. "COVID-19 vaccine hesitancy: a social media analysis using deep learning," Annals of Operations Research, Springer, vol. 339(1), pages 477-515, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-022-05154-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.