IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v30y2022i1d10.1007_s10100-021-00738-5.html
   My bibliography  Save this article

Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networks

Author

Listed:
  • Mustafa Alassad

    (University of Arkansas at Little Rock)

  • Muhammad Nihal Hussain

    (University of Arkansas at Little Rock)

  • Nitin Agarwal

    (University of Arkansas at Little Rock)

Abstract

With the power of social media being harnessed to coordinate events and revolutions across the globe, it is important to identify the key sets of individuals that have the power to mobilize crowds. These key sets have higher resources at their disposal and can regulate the flow of information in social networks. They can maximize information spread and influence/manipulate crowds when they are coordinating. But due to the inherent drawbacks in node-based and network-based community detection algorithms, neither of these types of algorithms can be used to detect/identify these key sets. In this study, we present a bi-level max-max optimization approach to identify these key sets, where the degree centrality is used to identify individuals’ influence at the commenter-level, while the network-level is designed to evaluate the spectral modularity values. We also present a set of evaluation metrics that can be used to rank these key sets for an in-depth investigation. We demonstrated the efficacy of the proposed model by identifying key sets hidden in a YouTube network spreading fake news about the conflict in South China Sea. The network consisted of 47,265 comments, 8477 commenters, and 5095 videos. A co-commenter network was constructed, where two commenters were linked together if they comment on same video. The proposed model efficiently identified key sets of commenters spread information to the whole network to manipulate YouTube’s recommendation and search algorithm to increase the information dissemination. Moreover, the projected approach could identify sets of commenters that were key connectors to multiple groups, high influence across the network, higher interactions, and reachability than other regular communities. Besides, the Girvan–Newman modularity method, the depth-first search method, and text analysis was applied to validate the outcomes, categorize the identified key sets, and monitor the commenters’ behaviors and information spread strategies in the network. In addition, the model considered a multi-criteria problem to rank these key sets of commenters based on the small real-world networks’ features.

Suggested Citation

  • Mustafa Alassad & Muhammad Nihal Hussain & Nitin Agarwal, 2022. "Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 367-394, March.
  • Handle: RePEc:spr:cejnor:v:30:y:2022:i:1:d:10.1007_s10100-021-00738-5
    DOI: 10.1007/s10100-021-00738-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-021-00738-5
    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/s10100-021-00738-5?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. Hu, Fang & Liu, Yuhua, 2016. "A new algorithm CNM-Centrality of detecting communities based on node centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 138-151.
    2. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    3. Li, Chao & Wang, Li & Sun, Shiwen & Xia, Chengyi, 2018. "Identification of influential spreaders based on classified neighbors in real-world complex networks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 512-523.
    4. Chen, Naiyue & Liu, Yun & Chen, Haiqiang & Cheng, Junjun, 2017. "Detecting communities in social networks using label propagation with information entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 788-798.
    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. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    2. Natalya Ivanova & Ekaterina Zolotova, 2023. "Landolt Indicator Values in Modern Research: A Review," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    3. Nina Sakinah Ahmad Rofaie & Seuk Wai Phoong & Muzalwana Abdul Talib & Ainin Sulaiman, 2023. "Light-emitting diode (LED) research: A bibliometric analysis during 2003–2018," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 173-191, February.
    4. Giovanni Matteo & Pierfrancesco Nardi & Stefano Grego & Caterina Guidi, 2018. "Bibliometric analysis of Climate Change Vulnerability Assessment research," Environment Systems and Decisions, Springer, vol. 38(4), pages 508-516, December.
    5. Yi-Ming Wei & Jin-Wei Wang & Tianqi Chen & Bi-Ying Yu & Hua Liao, 2018. "Frontiers of Low-Carbon Technologies: Results from Bibliographic Coupling with Sliding Window," CEEP-BIT Working Papers 116, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    6. Loredana Canfora & Corrado Costa & Federico Pallottino & Stefano Mocali, 2021. "Trends in Soil Microbial Inoculants Research: A Science Mapping Approach to Unravel Strengths and Weaknesses of Their Application," Agriculture, MDPI, vol. 11(2), pages 1-21, February.
    7. Jiang, Lincheng & Zhao, Xiang & Ge, Bin & Xiao, Weidong & Ruan, Yirun, 2019. "An efficient algorithm for mining a set of influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 58-65.
    8. Evi Sachini & Nikolaos Karampekios & Pierpaolo Brutti & Konstantinos Sioumalas-Christodoulou, 2020. "Should I stay or should I go? Using bibliometrics to identify the international mobility of highly educated Greek manpower," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 641-663, October.
    9. Natalya Ivanova & Ekaterina Zolotova, 2024. "Vegetation Dynamics Studies Based on Ellenberg and Landolt Indicator Values: A Review," Land, MDPI, vol. 13(10), pages 1-24, October.
    10. Kabir, K.M. Ariful & Kuga, Kazuki & Tanimoto, Jun, 2019. "Effect of information spreading to suppress the disease contagion on the epidemic vaccination game," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 180-187.
    11. Chen, Wei & Hou, Xiaoli & Jiang, Manrui & Jiang, Cheng, 2022. "Identifying systemically important financial institutions in complex network: A case study of Chinese stock market," Emerging Markets Review, Elsevier, vol. 50(C).
    12. Chen, Yi & Ding, Shuai & Zheng, Handong & Zhang, Youtao & Yang, Shanlin, 2018. "Exploring diffusion strategies for mHealth promotion using evolutionary game model," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 148-161.
    13. Cáceres, José & Garijo, Delia & González, Antonio & Márquez, Alberto & Puertas, María Luz & Ribeiro, Paula, 2018. "Shortcut sets for the locus of plane Euclidean networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 192-205.
    14. Wu, Yu’e & Zhang, Zhipeng & Wang, Xinyu & Chang, Shuhua, 2019. "Impact of probabilistic incentives on the evolution of cooperation in complex topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 307-314.
    15. Yu, Shuzhen & Yu, Zhiyong & Jiang, Haijun, 2024. "A rumor propagation model in multilingual environment with time and state dependent impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    16. Vanessa Ioannoni & Tommaso Vitale & Corrado Costa & Iris Elliott, 2020. "Depicting communities of Romani studies: on the who, when and where of Roma related scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1473-1490, March.
    17. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    18. Wang, Weiping & Guo, Junjiang & Wang, Zhen & Wang, Hao & Cheng, Jun & Wang, Chunyang & Yuan, Manman & Kurths, Jürgen & Luo, Xiong & Gao, Yang, 2021. "Abnormal flow detection in industrial control network based on deep reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    19. Tzuhua D. Lin & Nimrod D. Rubinstein & Nicole L. Fong & Megan Smith & Wendy Craft & Baby Martin-McNulty & Rebecca Perry & Martha A. Delaney & Margaret A. Roy & Rochelle Buffenstein, 2024. "Evolution of T cells in the cancer-resistant naked mole-rat," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    20. Deng, Zheng-Hong & Huang, Yi-Jie & Gu, Zhi-Yang & Liu, Dan & Gao, Li, 2018. "Multi-games on interdependent networks and the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 83-90.

    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:cejnor:v:30:y:2022:i:1:d:10.1007_s10100-021-00738-5. 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.