Skip to main content
Springer Nature Link
Log in

The Improvement of the Stylometry-Based Cognitive Assistant Performance in Conditions of Big Data Analysis

  • Conference paper
  • First Online:
Applied Informatics and Cybernetics in Intelligent Systems (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1226))

Included in the following conference series:

  • 848 Accesses

Abstract

The current research is devoted to the problem of individual’s victim behavior in the Internet. The victim behavior is a complex one and can be characterized by numerous markers from the classical victimology point of view. Yet, there are no general approaches to identify the victim behavior in the Internet due to the novelty of this communication environment. In this paper the marker of numerous false pseudonyms is presented to identify the individual’s victim behavior, and the cognitive assistant is proposed as a facility to deal with this marker. The cognitive assistant is based on the stylometry analysis, yet this approach has some scalability and velocity issues under the big data conditions. So, the technique for cognitive assistant performance improvement is presented. The basic estimates are made to prove the efficiency of the proposed complex technique, based on the fog-computing and the distributed ledger technology.

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

Access this chapter

Log in via an institution

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

Similar content being viewed by others

References

  1. Abramenkova, V.V., Avanesov, V.S., Ageev, V.S.: Short Psychological Vocabulary. Politizdat, Moscow (1990)

    Google Scholar 

  2. Victimology and its main categories and concepts in native and foreign science. http://obrazovanie9.ru/articles/345-victimology.html. Accessed 10 Dec 2019

  3. Psychological factors for adolescent’s victimization. https://www.dissercat.com/content/psikhologicheskie-faktory-vozniknoveniya-viktimnogo-povedeniya-podrostkov. Accessed 10 Dec 2019

  4. Victimization identification. http://psihologn.org/index.php/diagnostika-sklonnosti-k-viktimnomu-povedeniyu. Accessed 10 Dec 2019

  5. Zhang, L., Liu, B.: Sentiment analysis and opinion mining. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning and Data Mining, pp. 41–48. Springer, Boston (2017)

    Google Scholar 

  6. Not an obscene racist after all: 4 flagged for Facebook fake. https://arstechnica.com/tech-policy/2009/09/that-obscene-racist-may-be-fake-4-sued-for-profile-prank/. Accessed 10 Dec 2019

  7. Girl, 13, commits suicide after being cyber-bullied by neighbour posing as teenage boy. https://www.dailymail.co.uk/news/article-494809/Girl-13-commits-suicide-cyber-bullied-neighbour-posing-teenage-boy.html/. Accessed 10 Dec 2019

  8. Man faces cyber-bullying charge in ex-girlfriend’s fake Adult-DateProfile. https://5newsonline.com/2013/11/07/man-faces-cyber-bullying-charge-in-ex-girlfriends-fake-adult-date-profile/. Accessed 10 Dec 2019

  9. Hinduja, S., Patchin, J.: Bullying, cyberbullying, and suicide. Arch. Suicide Res.: Offic. J. Int. Acad. Suicide Res. 14(3), 206–221 (2010)

    Article  Google Scholar 

  10. Afroz, S., Caliskan Islam, A., Stolerman, A., Greenstadt, R., McCoy, D.: Doppelgänger finder: taking stylometry to the underground. In: 2014 IEEE Symposium on Security and Privacy, pp. 212–226. IEEE, Danvers (2014)

    Google Scholar 

  11. Almishari, M., Kaafar, D., Oguz, E., Tsudik, G.: Stylometric linkability of tweets. In: WPES 2014, pp. 205–208 (2014)

    Google Scholar 

  12. Juola, P., Vescovi, D.: Analyzing stylometric approaches to author obfuscation. In: Peterson, G., Shenoi, S. (eds.) Advances in Digital Forensics VII. Digital Forensics 2011. IFIP Advances in Information and Communication Technology, vol 361, pp. 15–125. Springer, Heidelberg (2011)

    Google Scholar 

  13. Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds and deception in writing style online. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 461–475 (2012)

    Google Scholar 

  14. Bhargava, M., Mehndiratta, P., Asawa, K.: Stylometric analysis for authorship attribution on twitter. In: Bhatnagar, V., Srinivasa, S. (eds.) Big Data Analytics. BDA 2013. Lecture Notes in Computer Science, vol. 8302, pp. 37–47. Springer, Cham (2013)

    Google Scholar 

  15. Tempestt, N., Kalaivani, S., Aneez, F., Yiming, Y., Yingfei, X., Damon, W.: Surveying stylometry techniques and applications. ACM Comput. Surv. 50, 1–36 (2017)

    Google Scholar 

  16. Dealing with fake profiles, https://www.thecyberhelpline.com/guides/fake-profiles. Accessed 10 Dec 2019

  17. Students create fake online profiles to bully peers. https://www.edweek.org/ew/articles/2012/04/04/27facebook.h31.html. Accessed 10 Dec 2019

  18. Fake Instagram accounts being used by kids to ‘destroy reputations’, eSafety Commissioner says. https://www.abc.net.au/news/2018-03-16/children-using-fake-instagram-accounts-to-bully-others/9553548. Accessed 10 Dec 2019

  19. Experts warn teenagers are creating fake Instagram accounts to torment their peers in growing ‘finsta’ trend - with a worrying 133 per cent spike in cyber-bullying in just two weeks. https://www.dailymail.co.uk/news/article-5508891/Students-using-fake-Instagram-accounts-bully-children.html. Accessed 10 Dec 2019

  20. How to safeguard children against cyberbullying. https://www.nytimes.com/2019/09/05/well/family/how-to-safeguard-children-against-cyberbullying.html. Accessed 10 Dec 2019

  21. The main Facebook’s problems: sex, hate, fakes. https://texterra.ru/blog/glavnye-problemy-facebook-seks-nenavist-feyki.html. Accessed 10 Dec 2019

  22. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. In: Doklady Akademii Nauk SSSR, vol. 163, no. 4, pp. 845–848 (1965)

    Google Scholar 

  23. Thor, A.: Toward an adaptive string similarity measure for matching product offers. In: GI Jahrestagung, no. 1, pp. 702–710 (2010)

    Google Scholar 

  24. Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60, 538–556 (2009)

    Article  Google Scholar 

  25. Abbasi, A., Chen, H.: Writeprints: a stylometric approach to identity-level identification and similarity detection in cyberspace. ACM Trans. Inf. Syst. (TOIS) 26(2), 1–29 (2008)

    Article  Google Scholar 

  26. Juola, P.: Authorship attribution. Found. Trends Inf. Retr. 1(3), 233–334 (2006)

    Article  Google Scholar 

  27. Johansson, F., Kaati, L., Shrestha, A.: Timeprints for identifying social media users with multiple aliases. Secur. Inform. 4, 7 (2015)

    Article  Google Scholar 

  28. Faouzi Atig, M., Cassel, S., Kaati, L., Shrestha, A.: Activity profiles in online social media. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 850–855. IEEE, Danvers (2014)

    Google Scholar 

  29. Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: MDMKDD, p. 4. ACM (2010)

    Google Scholar 

  30. Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Workshop on Mobile Big Data, pp. 37–42 (2015)

    Google Scholar 

  31. Distributed Ledger Technology: beyond block chain: a report by the UK Government Chief Scientific Adviser. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/492972/gs-16-1-distributed-ledger-technology.pdf. Accessed 10 Dec 2019

  32. Burkhardt, D., Werling, M., Lasi, H.: Distributed ledger definition & demarcation. IEEE (2018)

    Google Scholar 

Download references

Acknowledgements

The paper has been prepared within the RFBR projects 18-29-22093.

Author information

Authors and Affiliations

  1. Federal Research Centre the Southern Scientific Centre of the Russian Academy of Sciences, 41, Chekhov Street, 344006, Rostov-on-Don, Russian Federation

    E. V. Melnik

  2. Scientific Research Institute of Multiprocessor Computer Systems of Southern Federal University, 2, Chekhov Street, 347928, Taganrog, Russian Federation

    I. S. Korovin & A. B. Klimenko

Authors
  1. E. V. Melnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. S. Korovin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. B. Klimenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. B. Klimenko .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Melnik, E.V., Korovin, I.S., Klimenko, A.B. (2020). The Improvement of the Stylometry-Based Cognitive Assistant Performance in Conditions of Big Data Analysis. In: Silhavy, R. (eds) Applied Informatics and Cybernetics in Intelligent Systems. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-51974-2_8

Download citation

Publish with us

Policies and ethics