Skip to main content

Raising Cybersecurity Awareness Through Electronic Word of Mouth: A Data-Driven Assessment

  • Conference paper
  • First Online:
Augmented Cognition (HCII 2023)

Abstract

Awareness of the many cybersecurity threats, vulnerabilities, and solutions to mitigate these threats/vulnerabilities is instrumental in improving basic cybersecurity behaviours. A healthy body of knowledge has been devoted to exploring how to better increase awareness, in any given topic, among members of the general public which have explored the role of word of mouth (WOM) and electronic word of mouth (eWOM) in spreading awareness. In recent years, the rise of social media platforms as an alternative communication channel has created efforts to promote cybersecurity awareness online regarding the numerous cybersecurity threats. However, little research attention has been devoted to exploring eWOM communication on social media surrounding cybersecurity awareness. Moreover, no research to date has considered the impact of the COVID-19 pandemic on these eWOM discussions related to cybersecurity awareness. To address these literature gaps, this research collected 227, 270 relevant tweets surrounding cybersecurity awareness from 2018 to 2022 conducting an exploratory analysis of the corpus using social network analyses, topic modelling and semantic similarity analysis. The results found topics rose in prominence and then dissipated as newer topics emerged while information was found to spread incredibly far despite a high degree of community forming, suggesting the online discourse is very open and evolving over time. These findings illustrate the potential of social media as an effective tool for raising cybersecurity awareness. The impact of COVID-19 observed an increase in the reach of information in addition to new specific topics emerging in the discourse, but the effects appear to be temporary.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Choo, K.K.R.: The cyber threat landscape: Challenges and future research directions. Comput. Secur. 30, 719–731 (2011). https://doi.org/10.1016/j.cose.2011.08.004

    Article  Google Scholar 

  2. Blackwood-Brown, C., Levy, Y., D’Arcy, J.: Cybersecurity awareness and skills of senior citizens: a motivation perspective. J. Comput. Inf. Syst. 61, 195–206 (2021). https://doi.org/10.1080/08874417.2019.1579076

    Article  Google Scholar 

  3. Rahim, N.H.A., Hamid, S., Kiah, L.M., Shamshirband, S., Furnell, S.: A systematic review of approaches to assessing cybersecurity awareness. Kybernetes 44, 606–622 (2015). https://doi.org/10.1108/K-12-2014-0283

    Article  Google Scholar 

  4. Cram, A.W., D’Arcy, J., Proudfoot, J.G.: Seeing the forest and the trees: a meta-analysis of the antecedents to information security policy compliance. MIS Q. Manag. Inf. Syst. 43, 525–554 (2019). https://doi.org/10.25300/MISQ/2019/15117

  5. Bahl, A., Sharma, A., Asghar, M.R.: Vulnerability disclosure and cybersecurity awareness campaigns on twitter during COVID -19. Secur. Priv. 4, 1–14 (2021). https://doi.org/10.1002/spy2.180

    Article  Google Scholar 

  6. Verma, S., Yadav, N.: Past, present, and future of electronic word of mouth (EWOM). J. Interact. Mark. 53, 111–128 (2021). https://doi.org/10.1016/j.intmar.2020.07.001

    Article  Google Scholar 

  7. Shiue, Y.C., Chiu, C.M., Chang, C.C.: Exploring and mitigating social loafing in online communities. Comput. Human Behav. 26, 768–777 (2010). https://doi.org/10.1016/j.chb.2010.01.014

    Article  Google Scholar 

  8. Nurse, J.R.C.: Cybersecurity awareness. Encycl. Cryptogr. Secur. Priv., 1–4 (2021). https://doi.org/10.1007/978-3-642-27739-9_1596-1

  9. de Bruijn, H., Janssen, M.: Building cybersecurity awareness: the need for evidence-based framing strategies. Gov. Inf. Q. 34, 1–7 (2017). https://doi.org/10.1016/j.giq.2017.02.007

    Article  Google Scholar 

  10. Rani, A., Shivaprasad, H.N.: Revisiting the antecedent of electronic word-of-mouth (eWOM) during COVID-19 pandemic. Decision 48(4), 419–432 (2021). https://doi.org/10.1007/s40622-021-00298-2

    Article  Google Scholar 

  11. Yi, S.K.M., Steyvers, M., Lee, M.D., Dry, M.J.: The wisdom of the crowd in combinatorial problems. Cogn. Sci. 36, 452–470 (2012). https://doi.org/10.1111/j.1551-6709.2011.01223.x

    Article  Google Scholar 

  12. Maass, W., Parsons, J., Purao, S., Storey, V.C., Woo, C.: Data-driven meets theory-driven research in the era of big data: opportunities and challenges for information systems research. J. Assoc. Inf. Syst. 19, 1253–1273 (2018). https://doi.org/10.17705/1jais.00526

  13. Mustak, M., Salminen, J., Plé, L., Wirtz, J.: Artificial intelligence in marketing: topic modeling, scientometric analysis, and research agenda. J. Bus. Res. 124, 389–404 (2021). https://doi.org/10.1016/j.jbusres.2020.10.044

    Article  Google Scholar 

  14. Gruzd, A., Paulin, D., Haythornthwaite, C.: Analyzing social media and learning through content and social network analysis: a faceted methodological approach. J. Learn. Anal. 3, 46–71 (2016). https://doi.org/10.18608/jla.2016.33.4

  15. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  16. D Lee, Seung, H.: Learning the parts of objects by nonnegative matrix factorization. Nature 401(6755). (1999)

    Google Scholar 

  17. Bulgurcu, B., Cavusoglu, H., Benbast, I.: Information security policy compliance: an empirical study of rationality-based beliefs and information security awareness. MIS Q. 34, 523–548 (2010)

    Article  Google Scholar 

  18. Zwilling, M., Klien, G., Lesjak, D., Wiechetek, Ł, Cetin, F., Basim, H.N.: Cyber security awareness, knowledge and behavior: a comparative study. J. Comput. Inf. Syst. 62, 82–97 (2022). https://doi.org/10.1080/08874417.2020.1712269

    Article  Google Scholar 

  19. Alshboul, Y., Streff, K.: Beyond cybersecurity awareness: antecedents and satisfaction. In: ACM International Conference Proceeding Series, pp. 85–91 (2017). https://doi.org/10.1145/3178212.3178218

  20. Quayyum, F., Cruzes, D.S., Jaccheri, L.: Cybersecurity awareness for children: a systematic literature review. Int. J. Child-Comput. Interact. 30, 100343 (2021). https://doi.org/10.1016/j.ijcci.2021.100343

    Article  Google Scholar 

  21. Hong, W.C.H., Chi, C.Y., Liu, J., Zhang, Y.F., Lei, V.N.L., Xu, X.S.: The influence of social education level on cybersecurity awareness and behaviour: a comparative study of university students and working graduates. Springer, US (2022). https://doi.org/10.1007/s10639-022-11121-5

  22. Aloul, F.A.: The need for effective information security awareness. J. Adv. Inf. Technol. 3, 176–183 (2012). https://doi.org/10.4304/jait.3.3.176-183

    Article  Google Scholar 

  23. Potgieter, P.: The awareness behaviour of students on cyber security awareness by using social media platforms: a case study at central university of technology, vol. 12, pp. 272–280 (2019). https://doi.org/10.29007/gprf

  24. Trusov, M., Bucklin, R.E., Pauwels, K., Trusov, M., Bucklin, R.E., Pauwels, K.: Effects of word-of-mouth versus traditional marketing : findings from an internet social networking site. 73, 90–102 (2009)

    Google Scholar 

  25. Wadbring, I., Ödmark, S.: Going viral: news sharing and shared news in social media. Observatorio (OBS*) 10(4) (2016). https://doi.org/10.15847/obsOBS1042016936

  26. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31, 211–236 (2017). https://doi.org/10.1257/jep.31.2.211

    Article  Google Scholar 

  27. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359, 1146–1151 (2018)

    Article  Google Scholar 

  28. Pranggono, B.: COVID-19 pandemic cybersecurity issues. Internet Technol. Lett. 4, 4–9 (2021). https://doi.org/10.1002/itl2.247

    Article  Google Scholar 

  29. Alawida, M., Esther, A., Isaac, O., Al-rajab, M.: A deeper look into cybersecurity issues in the wake of COVID-19 : a survey. J. King Saud Univ. - Comput. Inf. Sci. 34, 8176–8206 (2022). https://doi.org/10.1016/j.jksuci.2022.08.003

  30. Kaya, T.: Technology in society the changes in the effects of social media use of Cypriots due to COVID-19 pandemic. Technol. Soc. 63, 101380 (2020). https://doi.org/10.1016/j.techsoc.2020.101380

    Article  Google Scholar 

  31. Abul-Fottouh, D.: Brokerage roles and strategic positions in twitter networks of the 2011 Egyptian revolution. Policy Internet 10, 218–240 (2018). https://doi.org/10.1002/poi3.169

    Article  Google Scholar 

  32. Hopke, J.E., Hestres, L.E.: Visualizing the paris climate talks on Twitter: media and climate stakeholder visual social media during COP21. Soc. Media + Soc. 4 (2018). https://doi.org/10.1177/2056305118782687

  33. Jacobson, J., Mascaro, C.: Movember : Twitter conversations of a hairy social movement. Soc. Media + Soc. 2 (2016). https://doi.org/10.1177/2056305116637103

  34. Martin, S., Brown, W.M., Wylie, B.N.: DRL: distributed recursive (graph) layout. Sandia National Lab (2007)

    Google Scholar 

  35. Lau, J.H., Newman, D., Baldwin, T.: Machine reading tea leaves: automatically evaluating topic coherence and topic model quality. In: 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, pp. 530–539 (2014). https://doi.org/10.3115/v1/e14-1056

  36. Blair, S.J., Bi, Y., Mulvenna, M.D.: Aggregated topic models for increasing social media topic coherence. Appl. Intell. 50(1), 138–156 (2019). https://doi.org/10.1007/s10489-019-01438-z

    Article  Google Scholar 

  37. Sangari, M.S., Mashatan, A.: A data-driven, comparative review of the academic literature and news media on blockchain-enabled supply chain management: Trends, gaps, and research needs. Comput. Ind. 143, 103769 (2022). https://doi.org/10.1016/j.compind.2022.103769

    Article  Google Scholar 

  38. McCallum, A.K.: Mallet: a machine learning for language toolkit (2002). http://mallet.cs.umass.edu

  39. Alagheband, M.R., Mashatan, A., Zihayat, M.: Time-based gap analysis of cybersecurity trends in academic and digital media. ACM Trans. Manag. Inf. Syst. 11 (2020). https://doi.org/10.1145/3389684

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Atefeh Mashatan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vanderkooi, D., Sangari, M.S., Mashatan, A. (2023). Raising Cybersecurity Awareness Through Electronic Word of Mouth: A Data-Driven Assessment. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2023. Lecture Notes in Computer Science(), vol 14019. Springer, Cham. https://doi.org/10.1007/978-3-031-35017-7_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35017-7_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35016-0

  • Online ISBN: 978-3-031-35017-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics