IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v22y2021i4d10.1007_s10799-021-00335-7.html
   My bibliography  Save this article

Rumor conversations detection in twitter through extraction of structural features

Author

Listed:
  • Serveh Lotfi

    (Islamic Azad University)

  • Mitra Mirzarezaee

    (Islamic Azad University)

  • Mehdi Hosseinzadeh

    (Iran University of Medical Sciences)

  • Vahid Seydi

    (Islamic Azad University)

Abstract

Twitter is one of the most popular and renowned online social networks spreading information which although dependable could lead to spreading improbable and misleading rumors causing irreversible damage to individuals and society. In the present paper, a novel approach for detecting rumor-based conversations of various world events such as real-world emergencies and breaking news on Twitter is investigated. In this study, three aspects of information dissemination including linguistic style used to express rumors, characteristics of people involved in propagating information and structural features are studied. Structural features include features of reply tree and user graph. Structural features were extracted as new features in order to enhance the efficiency of the rumor conversations detection. These features provide valuable clues on how a source tweet is transmitted and responds over time. Experimental results indicate that the new features are effective in detecting rumors and that the proposed method is better than other methods as F1-score increased by 4%. Implementation of the proposed method was carried out on Twitter datasets collected during five breaking news stories.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:infotm:v:22:y:2021:i:4:d:10.1007_s10799-021-00335-7
    DOI: 10.1007/s10799-021-00335-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-021-00335-7
    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/s10799-021-00335-7?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. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cun Fu & Jinru Zhang & Xin Kang, 2024. "True or false? Linguistic and demographic factors influence veracity judgment of COVID-19 rumors," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-7, December.

    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

    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:infotm:v:22:y:2021:i:4:d:10.1007_s10799-021-00335-7. 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.