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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
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    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. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
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