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Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks

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Data Communication and Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1049))

Abstract

Online social networking sites have become very popular in this era due to easy accessibility of Internet. This popularity leads to continuous availability of multiple users, which resultantly attract more criminals and hence increasing insecurity in OSN. Different types of crimes are committed for multiple reasons in cyber realm by taking assistance of cyber technology. This insecure environment of OSN needs attention to prevent the damage caused by these crimes to society. Cyberbullying is reported as one of the harmful crimes causing psychological damage to victims. Cyberbullying has dangerous effects on the victim, which may also lead the victim to suicidal attempt. Victims of cyberbullying are usually afraid or embarrassed to reveal about their harassment. It has become a necessity to detect and prevent cyberbullying. Many researchers are working in multiple directions to achieve best results for automated cyberbullying detection. We have done a broad survey of all recent techniques proposed by researchers for cyberbullying detection and prediction. In the paper, we have presented taxonomy of multiple methods being used for cyberbullying detection. We also have presented a comparative analysis and classification of the work done in recent years.

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References

  1. D. Boyd, N. Ellison, Social network sites: definition, history, and scholarship. J. Comput. Med. Commun. 13, 210–230 (2007)

    Article  Google Scholar 

  2. Z. Shan, H. Cao, J. Lv, C. Yan, A. Liu, Enhancing and identifying cloning attacks in online social networks, in Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, Article No. 59 (ACM, Kota Kinabalu, Malaysia, 2013), pp. 17–19

    Google Scholar 

  3. M. Di Capua, et al., Unsupervised cyber bullying detection in social networks, in 23rd International Conference on Pattern Recognition (ICPR), México, 4–8 Dec 2016)

    Google Scholar 

  4. Digital Future Project Survey 2017, http://www.digitalcenter.org/wpcontent/uploads/2013/10/2017-Digital-Future-Report.pdf

  5. R. Kowalski, S.P. Limber, A. McCord, A developmental approach to cyberbullying: prevalence and protective factors. Artic. Aggress. Violent Behav. 45, 20–32 (2019) (Elsevier)

    Article  Google Scholar 

  6. A. Lenhart, Teens, social media & technology overview 2015 (Internet American Life Project). Pew Research Center, Aug 2015, http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015

  7. D. Olweus, Bullying at School: What We Know and What We Can Do (Blackwell, New York, 1993)

    Google Scholar 

  8. D. Olweus, School bullying: development and some important challenges. Annu. Rev. Clin. Psychol. 9, 1–14 (2013). https://doi.org/10.1146/annurev-clinpsy-050212-185516

    Article  Google Scholar 

  9. G.W. Giumetti, E.S. McKibben, A.L. Hatfield, A.N. Schroeder, R.M. Kowalski, Cyber incivility @ work: the new age of interpersonal deviance. Cyberpsychology Behav. Soc. Netw. 15, 148–154 (2012). https://doi.org/10.1089/cyber.2011.0336

    Article  Google Scholar 

  10. P.K. Smith, J. Mahdavi, M. Carvalho, S. Fisher, S. Russell, N. Tippett, Cyberbullying: its nature and impact in secondary school pupils. J. Child Psychol. Psychiatry 49(4), 376–385 (2008). https://doi.org/10.1111/j.1469-7610.2007.01846.x

    Article  Google Scholar 

  11. C. Langos, Cyberbullying: the challenge to define. Cyberpsychology Behav. Soc. Netw. 15(6), 285–289 (2012). https://doi.org/10.1089/cyber.2011.0588

    Article  Google Scholar 

  12. N. Willard, Educator’s Guide to Cyberbullying and Cyberthreats. Center for Safe and Responsible Internet Use (2007)

    Google Scholar 

  13. N. Samaneh, A. Masrah, M. Azmi, M.S. Nurfadhilna, A. Mustapha, S. Shojaee, A review of cyberbullying detection. An overview, in 13th International Conrence on Intelligent Systems Design and Applications (ISDA) (2013)

    Google Scholar 

  14. D. Mann, Emotional Troubles for ‘Cyberbullies’ and Victims. WebMD Health News, 6 July 2010. http://www.webmd.com/parenting/news/20100706/emotional-troublesfor-cyberbullies-and-victims. Accessed 24 Aug 2015

  15. T. Wiguna, I.R. Ismail, R. Sekartini, N.S.W. Rahardjo, F. Kaligis, A.L. Prabowo, R. Hendarmo, The gender discrepancy in high-risk behaviour outcomes in adolescents who have experienced cyberbullying in Indonesia. Asian J. Psychiatry 37 (2018) (Elsevier)

    Article  Google Scholar 

  16. C. Nixon, Current perspectives: the impact of cyberbullying on adolescent health, in Adolescent Health, Medicine and Therapeutics (2014), p. 143

    Google Scholar 

  17. B. Haidar, M. Chamoun, A. Serhrouchni, Multilingual cyberbullying detection system, detecting cyberbullying in Arabic content, in 1st Cyber Security in Networking Conference (CSNet) (IEEE, 2017)

    Google Scholar 

  18. Ditch the Label Anti Bullying Charity, The annual cyberbullying survey 2013 (2013), http://www.ditchthelabel.org/annual-cyber-bullying-survey-cyber-bullying-statistics/

  19. B. Haidar, M. Chamoun, F. Yamout, Cyberbullying detection: a survey on multilingual techniques, in European Modelling Symposium (EMS) (IEEE, 2016)

    Google Scholar 

  20. P. Galán-García, J.G. Puerta, C.L. Gómez, I. Santos, P.G. Bringas, Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. Log. J. IGPL 24(1) (2016)

    Google Scholar 

  21. T. Mahlangu, C. Tu, P. Owolawi, A review of automated detection methods for cyberbullying, in International Conference on Intelligent and Innovative Computing Applications (ICONIC) (IEEE, 2018)

    Google Scholar 

  22. H. Hosseinmardi, R.I. Rafiq, R. Han, Q. Lv, S. Mishra, Prediction of cyberbullying incidents on the instagram social network, in International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE/ACM, 2016)

    Google Scholar 

  23. S. Gujral, Predicting and detecting hectoring on social media using machine learning, Int. J. Comput. Sci. Eng. 5(8) (2017)

    Article  Google Scholar 

  24. D. Yin, Z. Xue, L. Hong, B.D. Davison, A. Kontostathis, L. Edwards, Detection of harassment on Web 2.0, in Proceedings of the Content Analysis in the WEB 2.0 (CAW2.0) Workshop at WWW2009, Madrid, Spain (2009)

    Google Scholar 

  25. M. Dadvar1, D. Trieschnigg, R. Ordelman, F. de Jong, Improving cyberbullying detection with user context, in European Conference on Information Retrieval ECIR: Advances in Information Retrieval (Springer, 2013), pp. 693–696

    Google Scholar 

  26. M. Dadvar, F.D. Jong, R. Ordelman, D. Trieschnigg, Improved cyberbullying detection using gender information, in Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (2012)

    Google Scholar 

  27. J.F. Chisholm, Cyberspace violence against girls and adolescent females. Ann. N. Y. Acad. Sci. 1087, 74–89 (2006)

    Article  Google Scholar 

  28. S. Argamon, M. Koppel, J. Fine, A.R. Shimoni, Gender, genre, and writing style in formal written texts. Text Interdiscip. J. Study Discourse 23, 321–346 (2003)

    Article  Google Scholar 

  29. Y.J. Foong, M. Oussalah, Cyberbullying system detection and analysis, in European Intelligence and Security Informatics Conference (IEEE, 2017)

    Google Scholar 

  30. R. Slonje, P.K. Smith, Cyberbullying: another main type of bullying? Scand. J. Psychol. 49(2), 147–154 (2008)

    Article  Google Scholar 

  31. K.R. Williams, N.G. Guerra Prevalence and predictors of internet bullying. J. Adolesc. Health 41(6), S14–S21 (2007)

    Article  Google Scholar 

  32. Y.N. Silva, C. Rich, D. Hall. BullyBlocker: towards the identification of cyberbullying in social networking sites, in International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE/ACM, 2016)

    Google Scholar 

  33. M. Di Capua, E. Di Nardo, A. Petrosino, Unsupervised cyber bullying detection in social networks, in 23rd International Conference on Pattern Recognition (ICPR), Cancún Center, Cancún, México, 4–8 Dec 2016)

    Google Scholar 

  34. M.A. Al-garadi, K.D. Varathan, S.D. Ravana, Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. J. Comput. Hum. Behav. 63, 433–443 (2016) (Elsevier)

    Article  Google Scholar 

  35. M. Ptaszynski, F. Masui, T. Nitta, S. Hatakeyama, Y. Kimura, R. Rzepka, K. Araki, Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization. Int. J. Child Comput. Interact. (2016) (Elsevier)

    Google Scholar 

  36. Noviantho, S.M. Isa, L. Ashianti, Cyberbullying classification using text mining, in 1st International Conference on Informatics and Computational Sciences (IEEE, 2017)

    Google Scholar 

  37. B. Sri Nandhinia, J.I. Sheebab, Online social network bullying detection using intelligence techniques, in International Conference on Advanced Computing Technologies and Applications (2015) (Elsevier)

    Google Scholar 

  38. S. Resett, M. Gamez-Guadix, Traditional bullying and cyberbullying: differences in emotional problems, and personality. Are cyberbullies more Machiavellians? J. Adolesc. 61, 113–116 (2017)

    Article  Google Scholar 

  39. M. van Geel, A. Goemans, A. Toprak, P. Vedder, Which personality traits are related to traditional bullying and cyberbullying? A study with the Big Five, Dark Triad and sadism. Pers. Individ. Differ. 106, 231–235 (2017)

    Article  Google Scholar 

  40. R. Festl, T. Quandt, Social relations and cyberbullying: the influence of individual and structural attributes on victimization and perpetration via the Internet. Hum. Commun. Res. 39(1), 101–126 (2013)

    Article  Google Scholar 

  41. V. Balakrishnana, S. Khana, T. Fernandezb, H.R. Arabniac, Cyberbullying detection on twitter using Big Five and Dark Triad features. J. Pers. Individ. Differ. 141 (2019) (Elsevier)

    Article  Google Scholar 

  42. H. Hosseinmardi, S.A. Mattson, R.I. Rafiq, R. Han, Q. Lv, S. Mishr, Prediction of cyberbullying incidents in a media-based social network, in International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE/ACM, 2016)

    Google Scholar 

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Authors and Affiliations

  1. Pillai College of Engineering, Mumbai University, Navi Mumbai, India

    Madhura Vyawahare

  2. Pillai HOC College of Engineering and Technology, Mumbai University, Rasayani, India

    Madhumita Chatterjee

Authors
  1. Madhura Vyawahare
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  2. Madhumita Chatterjee
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Correspondence to Madhura Vyawahare .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW, Australia

    Lakhmi C. Jain

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. Department of Automation and Applied Informatics, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina E. Balas

  4. Department of Computer Engineering and Applications, Institute of Engineering and Technology, GLA University, Mathura, India

    Dilip Kumar Sharma

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Vyawahare, M., Chatterjee, M. (2020). Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks. In: Jain, L., Tsihrintzis, G., Balas, V., Sharma, D. (eds) Data Communication and Networks. Advances in Intelligent Systems and Computing, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-15-0132-6_3

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  • DOI: https://doi.org/10.1007/978-981-15-0132-6_3

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  • Online ISBN: 978-981-15-0132-6

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