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Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis

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Abstract

Cyberbullying is to bully someone in the digital realm. It has become extremely detrimental as the social media and the internet have become more popular and omnipresent. People use the internet services to viciously attack others from behind a screen. The substantial growth in the dimensionality, heterogeneity, subjectivity and multimodality of social media and the pressing need to timely curtail the damage instigated through cyberbullying, has fostered the need to devise automated mechanisms which detect such unfavorable activities. The use of soft computing techniques to handle such pernicious issue has been studied invariably and widely in literature. This study is to understand the viability, scope and significance of this alliance of using soft computing techniques for cyberbullying detection on social multimedia. This work is a systematic literature review to gather, explore, comprehend and analyze the research trends, gaps and prospects of this pairing in a well-organized way. The contribution of this study is noteworthy as it focuses on the use and application of soft computing techniques for cyberbullying detection on social multimedia utilizing a meta-analytic approach in order to integrate, interpret and critically analyze the findings in the original studies for expounding novel approaches to achieve comparable and effectual results pertaining to the defined research domain. Published studies starting April 2003, accessed from six digital portals (ACM, IEEE, Elsevier, Wiley, Springer and Taylor and Francis) have been reviewed to expound the state-of-art within the domain to give insightsand finally identify the directions of future research.

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References

  1. Aggarwal CC (2018) Neural networks and deep learning: a textbook. Springer, Berlin

    Book  MATH  Google Scholar 

  2. Agrawal S, Awekar A (2018) Deep learning for detecting cyberbullying across multiple social media platforms. In: European conference on information retrieval. Springer, Cham, pp 141–153

    Google Scholar 

  3. Al-garadi MA, Varathan KD, Ravana SD (2016) Cybercrime detection in online communications: the experimental case of cyberbullying detection in the twitter network. Comput Hum Behav 63:433–443

    Article  Google Scholar 

  4. Ashktorab Z, Haber E, Golbeck J, Vitak J (2017) Beyond cyberbullying: self-disclosure, harm and social support on ASKfm. In: Proceedings of the 2017 ACM on Web Science Conference, p 3-12

  5. Balakrishnan V (2015) Cyberbullying among young adults in Malaysia: the roles of gender, age and internet frequency. Comput Hum Behav 46:149–157

    Article  Google Scholar 

  6. Balci K, Salah AA (2015) Automatic analysis and identification of verbal aggression and abusive behaviors for online social games. Comput Hum Behav 53:517–526

    Article  Google Scholar 

  7. Bourgonje P, Moreno-Schneider J, Srivastava A, Rehm G (2017) Automatic classification of abusive language and personal attacks in various forms of online communication. In: International conference of the German Society for Computational Linguistics and Language Technology. Springer, Cham, pp 180–191

    Google Scholar 

  8. Bu SJ, Cho SB (2018) A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. International Conference on Hybrid Artificial Intelligence Systems 2018, Springer, p 561–572

  9. Byrne S, Katz SJ, Lee T, Linz D, McIlrath M (2014) Peers, predators, and porn: predicting parental underestimation of children's risky online experiences. J Comput-Mediat Commun 19(2):215–231

    Article  Google Scholar 

  10. Campbell MA (2005) Cyber bullying: an old problem in a new guise? J Psychol Couns Sch 15(1):68–76

    Google Scholar 

  11. Chatzakou D, Kourtellis N, Blackburn J, De Cristofaro E, Stringhini G, Vakali A (2017a) Hate is not binary: studying abusive behavior of# gamergate on twitter. In Proceedings of the 28th ACM conference on hypertext and social media, p 65-74

  12. Chatzakou D, Kourtellis N, Blackburn J, De Cristofaro E, Stringhini G, Vakali A (2017b) Detecting aggressors and bullies on Twitter. In: Proceedings of the 26th International Conference on World Wide Web Companion, p 767-768

  13. Chatzakou D, Kourtellis N, Blackburn J, De Cristofaro E, Stringhini G, Vakali A (2017c) Mean birds: Detecting aggression and bullying on twitter. In: Proceedings of the 2017 ACM on web science conference, p 13-22

  14. Chavan VS, Shylaja SS (2015) Machine learning approach for detection of cyber-aggressive comments by peers on social media network. In: Advances in computing, communications and informatics (ICACCI), 2015 International Conference on IEEE, p 2354-2358

  15. Chen J, Yan S, Wong KC (2018) Verbal aggression detection on twitter comments: convolutional neural network for short-text sentiment analysis. Neural Comput & Applic:1–10

  16. Coletto M, Lucchese C, Orlando S (2018) Do violent people smile: social media analysis of their profile pictures. In: Companion of the Web Conference 2018. International World Wide Web Conferences Steering Committee, ACM, p 1465–1468

  17. Dadvar M, Trieschnigg D, Ordelman R, de Jong F (2013) Improving cyberbullying detection with user context. In: European conference on information retrieval. Springer, Berlin, Heidelberg, pp 693–696

    Google Scholar 

  18. Dadvar M, Trieschnigg D, de Jong F (2014) Experts and machines against bullies: a hybrid approach to detect cyberbullies. In: Canadian conference on artificial intelligence. Springer, Cham, pp 275–281

    Google Scholar 

  19. García-Recuero Á (2017) Efficient privacy-preserving adversarial learning in decentralized online social networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, p 1132–1135

  20. Gordeev D (2016a) Detecting state of aggression in sentences using CNN. In: International Conference on Speech and Computer, Springer, Cham, p 240-245

  21. Gordeev D (2016b) Automatic detection of verbal aggression for Russian and American image boards. Procedia Soc Behav Sci 236:71–75

    Article  Google Scholar 

  22. Haidar B, Chamoun M, Serhrouchni A (2017) Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content. In: Cyber Security in Networking Conference (CSNet), IEEE, p 1-8

  23. Hammer HL (2016) Automatic detection of hateful comments in online discussion. In: International Conference on Industrial Networks and Intelligent Systems, Springer, Cham, p 164-173

  24. Holt TJ, Turner MG, Exum ML (2014) The impact of self control and neighborhood disorder on bullying victimization. J Crim Just 42(4):347–355

    Article  Google Scholar 

  25. Hosseinmardi H, Rafiq RI, Han R, Lv Q, Mishra S (2016) Prediction of cyberbullying incidents in a media-based social network. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, p 186–192

  26. http://www.ryanpatrickhalligan.org/ Accessed 14 July 2018

  27. https://coschedule.com/ Accessed 14 July 2018

  28. Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Tech Rep EBSE 1:1–57

    Google Scholar 

  29. Koban K, Stein JP, Eckhardt V, Ohler P (2018) Quid pro quo in web 2.0. Connecting personality traits and Facebook usage intensity to uncivil commenting intentions in public online discussions. Comput Hum Behav 79:9–18

    Article  Google Scholar 

  30. Kontostathis A, Reynolds K, Garron A, Edwards L (2013) Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th annual ACM web science conference, p 195-204

  31. Michalopoulos D, Mavridis I, Jankovic M (2014) GARS: real-time system for identification, assessment and control of cyber grooming attacks. Computers & Security 42:177–190

    Article  Google Scholar 

  32. Nahar V, Unankard S, Li X, Pang C (2012) Sentiment analysis for effective detection of cyber bullying. Asia-Pacific Web Conference, Springer, Berlin, Heidelberg, p 767-774

  33. Nahar V, Al-Maskari S, Li X, Pang C (2014) Semi-supervised learning for cyberbullying detection in social networks. In: Australasian database conference. Springer, Cham, pp 160–171

    Google Scholar 

  34. Nandhini BS, Sheeba JI (2015) Online social network bullying detection using intelligence techniques. Procedia Computer Science 45:485–492. International Conference on Advanced Computing Technologies and Applications (ICACTA)

  35. National Bullying Prevention Center https://www.pacer.org/bullying/. Accessed 26 July 2018

  36. Papegnies E, Labatut V, Dufour R, Linares G (2017) Graph-based features for automatic online abuse detection. In: International conference on statistical language and speech processing. Springer, Cham, pp 70–81

    Chapter  Google Scholar 

  37. Parime S, Suri V (2014) Cyberbullying detection and prevention: data mining and psychological perspective. In: Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference IEEE, p 1541-1547

  38. Potha N, Maragoudakis M, Lyras D (2016) A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data. Knowl-Based Syst 96:134–155

    Article  Google Scholar 

  39. Rafiq RI, Hosseinmardi H, Han R, Lv Q, Mishra S, Mattson SA (2015) Careful what you share in six seconds: detecting cyberbullying instances in Vine. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, p 617–622

  40. Rafiq RI, Hosseinmardi H, Mattson SA, Han R, Lv Q, Mishra S (2016) Analysis and detection of labeled cyberbullying instances in vine, a video-based social network. Soc Netw Anal Min 6(1):88

    Article  Google Scholar 

  41. Raisi E, Huang B (2017) Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, p 409–416

  42. Rakib TB, Soon LK (2018) Using the Reddit Corpus for cyberbully detection. In: Asian conference on intelligent information and database systems. Springer, Cham, pp 180–189

    Chapter  Google Scholar 

  43. Reynolds K, Kontostathis A, Edwards L (2011) Using machine learning to detect cyberbullying. In: Machine learning and applications and workshops (ICMLA), 2011 10th International Conference, IEEE 2:241-244

  44. Salawu S, He Y, Lumsden J (2017) Approaches to automated detection of cyberbullying: a survey. IEEE Trans Affect Comput 1:1–20

    Article  Google Scholar 

  45. Sarna G, Bhatia MP (2017) Content based approach to find the credibility of user in social networks: an application of cyberbullying. Int J Mach Learn Cybern 8(2):677–689

    Article  Google Scholar 

  46. Sedano CR, Ursini EL, Martins PS (2017) A bullying-severity identifier framework based on machine learning and fuzzy logic. In: International conference on artificial intelligence and soft computing. Springer, Cham, pp 315–324

    Google Scholar 

  47. Sharma HK, Kshitiz K, Shailendra (2018) NLP and machine learning techniques for detecting insulting comments on social networking platforms. International Conference on Advances in Computing and Communication Engineering (ICACCE) 2018, IEEE, p 265-272

  48. Sheeba JI, Vivekanandan K (2013) Low frequency keyword extraction with sentiment classification and cyberbully detection using fuzzy logic technique. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), p 1–5

  49. The National Crime Prevention Cyberbullying. http://www.ncpc.org/cyberbullying. Accessed 10 March 2018

  50. Thu PP, New N (2017) Implementation of emotional features on satire detection. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2017 18th IEEE/ACIS International Conference, IEEE, p 149-154

  51. Wint ZZ, Ducros T, Aritsugi M (2017) Spell corrector to social media datasets in message filtering systems. In: Digital Information Management (ICDIM), 2017 Twelfth International Conference, IEEE, p 209–215

  52. Xu JM, Jun KS, 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, p 656-666

  53. Ybarra M (2010) Trends in technology-based sexual and non-sexual aggression over time and linkages to nontechnology aggression. National Summit on Interpersonal Violence and Abuse Across the Lifespan: Forging a Shared Agenda

  54. Yin D, Xue Z, Hong L, Davison BD, Kontostathis A, Edwards L (2009) Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB 2:1-7

  55. Zhang X, Tong J, Vishwamitra N, Whittaker E, Mazer JP, Kowalski R, Hu H, Luo F, Macbeth J, Dillon E (2016) Cyberbullying detection with a pronunciation based convolutional neural network. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), p 740–745

  56. Zhao R, Mao K (2017) Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans Affect Comput 8(3):328–339

    Article  Google Scholar 

  57. Zhao R, Zhou A, Mao K (2016) Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th international conference on distributed computing and networking, p 43-48

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  1. Department of Computer Science & Engineering, Delhi Technological University, Delhi, India

    Akshi Kumar & Nitin Sachdeva

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  1. Akshi Kumar
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  2. Nitin Sachdeva
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Correspondence to Akshi Kumar.

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Kumar, A., Sachdeva, N. Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed Tools Appl 78, 23973–24010 (2019). https://doi.org/10.1007/s11042-019-7234-z

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