Analysis and detection of labeled cyberbullying instances in Vine, a video-based social network

Abstract

The last decade has experienced an exponential growth of popularity in online social networks. This growth in popularity has also paved the way for the threat of cyberbullying to grow to an extent that was never seen before. Online social network users are now constantly under the threat of cyberbullying from predators and stalkers. In our research paper, we perform a thorough investigation of cyberbullying instances in Vine, a video-based online social network. We collect a set of media sessions (shared videos with their associated meta-data) and then label those using CrowdFlower, a crowd-sourced website for cyberaggression and cyberbullying. We also perform a second survey that labels the videos’ contents and emotions exhibited. After the labeling of the media sessions, we provide a detailed analysis of the media sessions to investigate the cyberbullying and cyberaggression behavior in Vine. After the analysis, we train different classifiers based upon the labeled media sessions. We then investigate, evaluate and compare the classifers’ performances to detect instances of cyberbullying.

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References

  1. Alski J (2010) Electronic aggression among adolescents: an old house with. In: Youth culture and net culture: online social practices, IGI Global, p 278

  2. Basic Emotions http://changingminds.org/. Accessed 24 Apr 2015

  3. Broderick R (2013) 9 Teenage suicides. In: The last year were linked to cyber-bullying on social network Ask.fm. http://www.buzzfeed.com/ryanhatesthis/a-ninth-teenager-since-last-september. Accessed 14 Jan 2014

  4. Cyberbullying Research Center (2013) http://cyberbullying.us. Accessed Sept 2013

  5. Dadvar M, de Jong FMG, Ordelman RJF, Trieschnigg RB (2012) Improved cyberbullying detection using gender information. In: Proceedings of the 12th Dutch–Belgian information retrieval workshop (DIR 2012), Ghent, pp 23–25

  6. De Silva LC, Miyasato T, Nakatsu R (1997) Facial emotion recognition using multi-modal information. In: Proceedings of 1997 international conference on information, communications and signal processing, vol 1. IEEE, pp 397–401 ISBN:0-7803-3676-3

  7. Deirdre M (2016) Kelly, cyberbullying and internet safety. In: Handbook of research on the societal impact of digital media. IGI Global, pp 529–559. doi:10.4018/978-1-4666-8310-5.ch021

  8. Dinakar K, Reichart R, Lieberman H (2011) Modeling the detection of textual cyberbullying. In: The social mobile web

  9. Dinakar K, Jones B, Havasi C, Lieberman H, Picard R (2012) Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans Interact Intell Syst 2(3):18.1–18.30 (ACM)

    Article  Google Scholar 

  10. Dooley J, Alski J, Cross D (2009) Cyberbullying versus face-to-face bullying. Z Psychol 217(4):182–188 (Hogrefe & Huber)

    Article  Google Scholar 

  11. Gordon S (2014) 4 Apps used for sexting and cyberbullying parents should know about. http://bullying.about.com/od/Cyberbullying/fl/4-Apps-Used-for-Sexting-and-Cyberbullying-Parents-Should-Know-About.htm. Accessed 11 June 2014

  12. Hanna Smith suicide fuels calls for action on Ask.fm cyberbullying. http://www.cnn.com/2013/08/07/world/europe/uk-social-media-bullying/. Accessed 14 Jan 2014

  13. Hinduja S, Patchin JW (2010) Cyberbullying research summary. Cyberbullying and suicide, Cyberbullying Research Center

  14. Hosseinmardi H, Ghasemianlangroodi A, Han R, Lv Q, Mishra S (2014a) Towards understanding cyberbullying behavior in a semi-anonymous social network. In: Advances in social networks analysis and mining (ASONAM 2014), pp 244–252

  15. Hosseinmardi H, Rafiq RI, Li S, Yang Z, Han R, Mishra S, Lv Q (2014b) Comparison of common users across instagram and ask.fm to better understand cyberbullying. In: The 7th IEEE international conference on social computing and networking (SocialCom)

  16. Hosseinmardi H, Mattson SA, Rafiq RI, Han R, Lv Q, Mishra S (2015) Detection of cyberbullying incidents on the instagram social network. arXiv:1503.03909

  17. http://expandedramblings.com/index.php/vine-statistics/. Accessed 12 Mar 2016

  18. Huang Q, Singh VK, Atrey PK (2014) Cyber bullying detection using social and textual analysis. In: Proceedings of the 3rd international workshop on socially-aware multimedia. ACM, pp 3–6

  19. Hunter SC, Boyle J, Warden D (2007) Perceptions and correlates of peer-victimization and bullying. Br J Educ Psychol 77(4):797–810 (Wiley Online Library)

    Article  Google Scholar 

  20. Kontostathis A, Reynolds K, Garron A, Lynne E (2013) Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th annual ACM web science conference. ACM, pp 195–204

  21. Kowalski RM, Limber SP, Agatston PW (2012) Cyberbullying: bullying in the digital age. Wiley, Hoboken

    Google Scholar 

  22. Kowalski RM, Giumetti GW, Schroeder AM, Lattanner MR (2014) Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Am Psychol Assoc 140:1173

    Google Scholar 

  23. Limber SP, Kowalski RM, Agatston PA (2008) Cyber bullying: a curriculum for grades 6–12. Hazelden, The Voice Center City

    Google Scholar 

  24. Menesini E, Nocentini A (2009) Cyberbullying definition and measurement. Some critical considerations. J Psychol 217(4):320–323

    Google Scholar 

  25. Monks CP, Smith PK (2006) Definitions of bullying: age differences in understanding of the term, and the role of experience. Br J Dev Psychol 24(4):801–821 (Wiley Online Library)

    Article  Google Scholar 

  26. Nahar V, Unankard S, Li X, Pang C (2012) Sentiment analysis for effective detection of cyber bullying. In: Sheng QZ, Wang G, Jensen CS, Xu G (eds) Web technologies and applications. Springer, Berlin, Heidelberg, pp 767–774

    Google Scholar 

  27. Nahar V, Li X, Pang C (2013) An effective approach for cyberbullying detection. Commun Inf Sci Manage Eng 3:238

    Google Scholar 

  28. Nahar V, Unankard S, Li X, Pang C (2014) Semi-supervised learning for cyberbullying detection in social networks. In: Proceedings of databases theory and applications: 25th Australasian database conference, ADC 2014, Brisbane, QLD, Australia, 14–16 July 2014. Springer, pp 160–171. ISBN 978-3-319-08608-8

  29. Nalini K, Sheela LJ (2015) Classification of tweets using text classifier to detect cyber bullying. In: Emerging ICT for bridging the future-proceedings of the 49th annual convention of the computer society of India CSI, vol 2. Springer, pp 637–646

  30. National Crime Prevention Council (2007) Teens and cyberbullying. Executive summary of a report on research conducted for National Crime Prevention Council

  31. Negative Words List form Luis von Ahn’s Research Group. http://www.cs.cmu.edu/~biglou/resources/. Accessed 14 Jan 2014

  32. Olweus D (1993) Bullying at school: what we know and what we can do. Blackwell, Oxford

    Google Scholar 

  33. Patchin JW, Hinduja S (2012) An update and synthesis of the research, cyberbullying prevention and response: expert perspectives. Routledge, New York

    Google Scholar 

  34. Potha N, Maragoudakis M (2014) Cyberbullying detection using time series modeling. In: IEEE international conference on data mining workshop (ICDMW). IEEE, pp 373–382

  35. Ptaszynski M, Dybala P, Matsuba T, Masui F, Rzepka R, Araki K, Momouchi Y (2010) In the service of online order tackling cyberbullying with machine learning and affect analysis. Int J Comput Linguist Res 1(3):135–154

    Google Scholar 

  36. Rafiq RI, Hosseinmardi H, Mattson S, Han R, Lv Q, Mishra S (2015) Careful what you share in six seconds: detecting cyberbullying instances in vine. In: IEEE/ACM international conference on advances in social networks analysis and minin. ACM, pp 617–622

  37. Reynolds K, Kontostathis A, Edwards L (2011) Using machine learning to detect cyberbullying. In: 4th international conference on machine learning and applications, vol 2. IEEE Computer Society, pp 241–244. ISBN:978-0-7695-4607-0

  38. Sanchez H, Kumar S (2012) Twitter bullying detection. In: NSDI 2012. USENIX Association, p 15

  39. Smith PK, del Barrio C,Tokunaga R (2012) Principles of cyberbullying research. Definitions, measures and methodology, chapter: definitions of bullying and cyberbullying: how useful are the terms? In: Principles of cyberbullying research. Definitions, measures and methodology. Routledge, New York, pp 26–40

  40. Sun Y, Sebe N, Lew MS, Gevers T (2004) Authentic emotion detection in real-time video. In: Computer vision in human-computer interaction. IEEE

  41. Teens Indicted After Allegedly Taunting Girl Who Hanged Herself. http://abcnews.go.com/Technology/TheLaw/teens-charged-bullying-mass-girl-kill/story?id=10231357,2010. Accessed 14 Jan 2014

  42. Xu J, Jun K, Zhu X, Bellmore A (2012) Learning from bullying traces in social media, In: Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: human language technologies. Association for Computational Linguistics, pp 656–666

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Acknowledgments

Funding was provided by National Science Foundation (Grant No. CNS1528138).

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Correspondence to Rahat Ibn Rafiq.

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Rafiq, R.I., Hosseinmardi, H., Mattson, S.A. et al. Analysis and detection of labeled cyberbullying instances in Vine, a video-based social network. Soc. Netw. Anal. Min. 6, 88 (2016). https://doi.org/10.1007/s13278-016-0398-x

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Keywords

  • Cyberbullying
  • Social networks
  • User behavior
  • Video-based social network