IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i3d10.1007_s42001-025-00396-6.html
   My bibliography  Save this article

Exploring the role of information security news descriptions on retweet proneness and user interactions

Author

Listed:
  • Konstantinos Charmanas

    (Aristotle University of Thessaloniki)

  • Klairi Filippou

    (Aristotle University of Thessaloniki)

  • Nikolaos Mittas

    (Democritus University of Thrace)

  • Lefteris Angelis

    (Aristotle University of Thessaloniki)

Abstract

Nowadays, advanced network and resource capabilities offer many benefits to platform users but also bring malicious opportunities, thus leading experts to raise awareness about malicious threats and discuss potential mitigation practices through security news. An important factor in understanding user engagement and experiences is to investigate their sentiment and interests regarding security threats and tools across online platforms. In this study, we investigate content-based factors in security news affecting user interactions through a dataset of 44,264 tweets posted by seven relevant accounts. The first goal is to discover whether the textual information hidden in security news triggers retweeting through the training and evaluation of a set of classifiers. The findings suggest that words and hashtags can be important in developing prediction mechanisms. The second goal is to distinguish topics of security news leading to relatively more user interactions than the rest, where the topics are discovered using the Non-negative Matrix Factorization algorithm. For this goal, four types of user interactions are studied both independently and aggregated using the Archetypal Analysis and Conover-Iman test, respectively. The outcomes from these two approaches suggest that hacking activities followed by learning materials and webinars should be considered the most popular topics. Overall, the discussed findings can be used to understand the interests and reactiveness of Twitter users across security news, while the framework can be studied for extracting knowledge from Twitter data.

Suggested Citation

  • Konstantinos Charmanas & Klairi Filippou & Nikolaos Mittas & Lefteris Angelis, 2025. "Exploring the role of information security news descriptions on retweet proneness and user interactions," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-31, August.
  • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00396-6
    DOI: 10.1007/s42001-025-00396-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-025-00396-6
    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/s42001-025-00396-6?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. Tittonell, P. & Bruzzone, O. & Solano-Hernández, A. & López-Ridaura, S. & Easdale, M.H., 2020. "Functional farm household typologies through archetypal responses to disturbances," Agricultural Systems, Elsevier, vol. 178(C).
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    3. Puklavec, Žiga & Kogler, Christoph & Stavrova, Olga & Zeelenberg, Marcel, 2023. "What we tweet about when we tweet about taxes: A topic modelling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 1242-1254.
    4. Konstantinos Charmanas & Nikolaos Mittas & Lefteris Angelis, 2024. "Content and interaction-based mapping of Reddit posts related to information security," Journal of Computational Social Science, Springer, vol. 7(2), pages 1187-1222, October.
    5. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sánchez-Alonso, Salvador, 2023. "Twitter as a predictive system: A systematic literature review," Journal of Business Research, Elsevier, vol. 157(C).
    6. Prateeksha Dawn Davidson & Thanujah Muniandy & Dhivya Karmegam, 2023. "Perception of COVID-19 vaccination among Indian Twitter users: computational approach," Journal of Computational Social Science, Springer, vol. 6(2), pages 541-560, October.
    7. Prasha Shrestha & Arun Sathanur & Suraj Maharjan & Emily Saldanha & Dustin Arendt & Svitlana Volkova, 2020. "Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    8. 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.
    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. Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    2. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    3. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    4. Spelta, A. & Pecora, N. & Rovira Kaltwasser, P., 2019. "Identifying Systemically Important Banks: A temporal approach for macroprudential policies," Journal of Policy Modeling, Elsevier, vol. 41(1), pages 197-218.
    5. Thilagavathi Ramamoorthy & Bagavandas Mappillairaju, 2023. "Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling," Journal of Computational Social Science, Springer, vol. 6(2), pages 1033-1054, October.
    6. Paul Fogel & Yann Gaston-Mathé & Douglas Hawkins & Fajwel Fogel & George Luta & S. Stanley Young, 2016. "Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health," IJERPH, MDPI, vol. 13(5), pages 1-14, May.
    7. Le Thi Khanh Hien & Duy Nhat Phan & Nicolas Gillis, 2022. "Inertial alternating direction method of multipliers for non-convex non-smooth optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 247-285, September.
    8. Jingfeng Guo & Chao Zheng & Shanshan Li & Yutong Jia & Bin Liu, 2022. "BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    9. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    10. Zhang, Lifeng & Chao, Xiangrui & Qian, Qian & Jing, Fuying, 2022. "Credit evaluation solutions for social groups with poor services in financial inclusion: A technical forecasting method," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Yi Yu & Jaeseung Baek & Ali Tosyali & Myong K. Jeong, 2024. "Robust asymmetric non-negative matrix factorization for clustering nodes in directed networks," Annals of Operations Research, Springer, vol. 341(1), pages 245-265, October.
    12. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    13. Anna Luiza Silva Almeida Vicente & Alexei Novoloaca & Vincent Cahais & Zainab Awada & Cyrille Cuenin & Natália Spitz & André Lopes Carvalho & Adriane Feijó Evangelista & Camila Souza Crovador & Rui Ma, 2022. "Cutaneous and acral melanoma cross-OMICs reveals prognostic cancer drivers associated with pathobiology and ultraviolet exposure," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    14. Takehiro Sano & Tsuyoshi Migita & Norikazu Takahashi, 2022. "A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence," Journal of Global Optimization, Springer, vol. 84(3), pages 755-781, November.
    15. Adam R. Pines & Bart Larsen & Zaixu Cui & Valerie J. Sydnor & Maxwell A. Bertolero & Azeez Adebimpe & Aaron F. Alexander-Bloch & Christos Davatzikos & Damien A. Fair & Ruben C. Gur & Raquel E. Gur & H, 2022. "Dissociable multi-scale patterns of development in personalized brain networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    16. Konstantinos Charmanas & Nikolaos Mittas & Lefteris Angelis, 2024. "Content and interaction-based mapping of Reddit posts related to information security," Journal of Computational Social Science, Springer, vol. 7(2), pages 1187-1222, October.
    17. Xiangli Li & Hongwei Liu & Xiuyun Zheng, 2012. "Non-monotone projection gradient method for non-negative matrix factorization," Computational Optimization and Applications, Springer, vol. 51(3), pages 1163-1171, April.
    18. Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
    19. Dominik P. Koller & Michael Schirner & Petra Ritter, 2024. "Human connectome topology directs cortical traveling waves and shapes frequency gradients," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    20. Abdul Suleman, 2017. "On ill-conceived initialization in archetypal analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 785-808, December.

    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:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00396-6. 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.