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Detection and Prevention of Bullying on Online Social Networks: The Combination of Textual, Visual and Cognitive

  • Carlos SilvaEmail author
  • Ricardo Santos
  • Ricardo Barbosa
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 273)

Abstract

The adoption of online social platforms as a common space for the virtualisation of identities is also correlated with the replication of real-world social hazards in the virtual world. Bullying, or cyberbullying, is a very common practice among people nowadays, becoming much more present due to the increase of online time, especially in online social networks, and having more serious consequences among younger audiences. Related work includes the analysis and classification of textual characteristics that can be indicative of a bullying situation and even a visual analysis approach through the adoption of image recognition techniques. While agreeing that the combination of textual and visual analysis can help the identification of bullying practice, or the identification of bullies, we also believe that a part is missing. In this work, we propose a combination of textual and visual classification techniques, associated with a cognitive aspect that can help to identify possible bullies. Based on a previous model definition for a virtual social sensor, we propose the analysis of textual content present on online social networks, check the presence of people in multimedia content, and identification of the stakeholders on a possible bullying situation by identifying cognitive characteristics and similarities on the behaviours of possible bullies and/or victims. This identification of possible bullying scenario can help to address them before they occur or reach unmanageable proportions.

Keywords

Online social networks Bullying Virtual social sensor Cognitive 

References

  1. 1.
    Evans, C., Smokowski, P.: Theoretical explanations for bullying in school: how ecological processes propagate perpetration and victimization. University of Kansas, University of North Carolina, USA (2016)Google Scholar
  2. 2.
    Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. MIT Media Lab, Massachusetts Institute of Technology, Cambridge, USA (2011)Google Scholar
  3. 3.
    Lightbody, G., Bond, R., Mulvenna, M., Bi, Y., Mulligan, M.: Investigation into the automated detection of image based cyberbullying on social media platforms. School of Computing and Mathematics, University of Ulster, Northern Ireland. Carnbane Business Centre, Newry, Northern Ireland (2014)Google Scholar
  4. 4.
    Mascheroni, G., Cuman, A.: Net Children Go Mobile: Final Report. Educatt, Milano (2014)Google Scholar
  5. 5.
    McClowry, R., Miller, M., Mills, G.: Theoretical explanations for bullying in school: what family physicians can do to combat bullying. Department of Family and Community Medicine, Thomas Jefferson University, Philadelphia, USA (2017)Google Scholar
  6. 6.
    Hee, C., et al.: Automatic detection of cyberbullying in social media text. Ghent University, University of Antwerp, Belgium (2018)Google Scholar
  7. 7.
    Hardy, R., Norgaard, J.: Reputation in the internet black market: an empirical and theoretical analysis of the Deep Web. J. Inst. Econ. (2017). George Mason University, Virginia, USAGoogle Scholar
  8. 8.
    Patchin, J., Hinduja, S.: Digital self-harm among adolescents. University of Wisconsin-Eau Claire, Eau Claire, Wisconsin, USA. Florida Atlantic University, Jupiter, Florida, USA (2017)Google Scholar
  9. 9.
    The Next Web - “How Dangerous is Cyberbullying?”. www.thenextweb-com/contributors/2017/10/04/how-dangerous-is-cyberbullying. Accessed 31 Oct 2017
  10. 10.
    Chatzakou, D., Kourtellis, N., Blackburn, J., Cristofaro, E., Strighini, G., Vakali, A.: Mean birds: detection aggression and bullying on Twitter. In: Proceedings of the 2017 ACM on Web Science Conference, pp. 13–22. ACM (2017)Google Scholar
  11. 11.
    Hosseinmardi, H., Mattson, S., Rafiq, R., Han, R., Lv, Q., Mishra, S.: Analyzing labeled cyberbullying incidents on the Instagram social networks. University of Colorado Boulder, Boulder, USA (2015)Google Scholar
  12. 12.
    Huang, K., Singh, V., Atrey, P.: Cyber Bullying Detection Using Social and Textual Analysis. ACM, New York (2014)CrossRefGoogle Scholar
  13. 13.
    Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore (2016)Google Scholar
  14. 14.
    Soni, S., Singh, V.: See No Evil, Hear No Evil: Audio-Visual-Textual Cyberbullying Detection. Rutgers University, New Brunswick (2018)CrossRefGoogle Scholar
  15. 15.
    Zhong, H., et al.: Content-driven detection of cyberbullying on the Instagram social network. In: IJCAI, pp. 3952–3958 (2016)Google Scholar
  16. 16.
    Barbosa, R., Santos, R.: Online social networks as sensors in smart environments. CIICESI, ESTGF, IPP School of Technology and Management of Felgueiras, Felgueiras, Portugal (2016)Google Scholar
  17. 17.
    Ghavami, S., Asadpour, M., Mahdavi, M.: Facebook user’s like behavior can reveal personality. In: 2015 7th Conference on Information and Technology (IKT), Urmia, pp. 1–3 (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.CIICESI - Center for Research and Innovation in Business Sciences and Information Systems, School of Management and TechnologyPolytechnic Institute of PortoFelgueirasPortugal

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