IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v25y2023i5d10.1007_s10796-022-10315-z.html
   My bibliography  Save this article

Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

Author

Listed:
  • Abderrazek Azri

    (Université de Lyon, Lyon 2, UR ERIC)

  • Cécile Favre

    (Université de Lyon, Lyon 2, UR ERIC)

  • Nouria Harbi

    (Université de Lyon, Lyon 2, UR ERIC)

  • Jérôme Darmont

    (Université de Lyon, Lyon 2, UR ERIC)

  • Camille Noûs

    (Université de Lyon, Lyon 2)

Abstract

The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance.

Suggested Citation

  • Abderrazek Azri & Cécile Favre & Nouria Harbi & Jérôme Darmont & Camille Noûs, 2023. "Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning," Information Systems Frontiers, Springer, vol. 25(5), pages 1795-1810, October.
  • Handle: RePEc:spr:infosf:v:25:y:2023:i:5:d:10.1007_s10796-022-10315-z
    DOI: 10.1007/s10796-022-10315-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-022-10315-z
    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/s10796-022-10315-z?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. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 2018. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 20(3), pages 577-588, June.
    2. Prabh Deep Singh & Rajbir Kaur & Kiran Deep Singh & Gaurav Dhiman, 2021. "A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients," Information Systems Frontiers, Springer, vol. 23(6), pages 1385-1401, December.
    3. Sejeong Kwon & Meeyoung Cha & Kyomin Jung, 2017. "Rumor Detection over Varying Time Windows," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    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. Lingnan He & Haoshen Yang & Xiling Xiong & Kaisheng Lai, 2019. "Online Rumor Transmission Among Younger and Older Adults," SAGE Open, , vol. 9(3), pages 21582440198, September.
    2. Lian, Ying & Liu, Yijun & Dong, Xuefan, 2020. "Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China," Technology in Society, Elsevier, vol. 62(C).
    3. Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.
    4. Bei Bi & Yaojun Wang & Haicang Zhang & Yang Gao, 2022. "Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-18, April.
    5. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.
    6. Serveh Lotfi & Mitra Mirzarezaee & Mehdi Hosseinzadeh & Vahid Seydi, 2021. "Rumor conversations detection in twitter through extraction of structural features," Information Technology and Management, Springer, vol. 22(4), pages 265-279, December.
    7. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.
    8. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.
    9. Jong-Min Kim & Leixin Xia & Iksuk Kim & Seungjoo Lee & Keon-Hyung Lee, 2020. "Finding Nemo: Predicting Movie Performances by Machine Learning Methods," JRFM, MDPI, vol. 13(5), pages 1-12, May.
    10. Jyoti Prakash Singh & Abhinav Kumar & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets," Information Systems Frontiers, Springer, vol. 24(2), pages 459-474, April.
    11. Victor Chang & Carole Goble & Muthu Ramachandran & Lazarus Jegatha Deborah & Reinhold Behringer, 2021. "Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19," Information Systems Frontiers, Springer, vol. 23(6), pages 1363-1367, December.
    12. Zhu, He & Ma, Jing & Li, Shan, 2019. "Effects of online and offline interaction on rumor propagation in activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1124-1135.
    13. Joshua Eklund & Jong-Min Kim, 2022. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression," Forecasting, MDPI, vol. 4(3), pages 1-14, July.
    14. Na Ye & Dingguo Yu & Yijie Zhou & Ke-ke Shang & Suiyu Zhang, 2023. "Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media," Mathematics, MDPI, vol. 11(15), pages 1-11, August.
    15. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.

    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:infosf:v:25:y:2023:i:5:d:10.1007_s10796-022-10315-z. 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.