A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

T. Vaillancourt, R. Faris, and F. Mishna, "Cyberbullying in children and youth: implications for health and clinical practice," Can. J. Psychiatry, vol. 62, no. 6, pp. 368-373, 2017.

C. Burger, D. Strohmeier, N. Spröber, S. Bauman, and K. Rigby, "How teachers respond to school bullying: An examination of self-reported intervention strategy use, moderator effects, and concurrent use of multiple strategies," Teach. Teach. Educ., vol. 51, pp. 191-202, Oct. 2015, doi: 10.1016/j.tate.2015.07.004.

L. Arseneault, L. Bowes, and S. Shakoor, "Bullying victimization in youths and mental health problems:'much ado about nothing'?," Psychol. Med., vol. 40, no. 5, p. 717, 2010.

O. Aluede, F. Adeleke, D. Omoike, and J. Afen-Akpaida, "A review of the extent, nature, characteristics and effects of bullying behaviour in schools," J. Instr. Psychol., vol. 35, no. 2, p. 151, 2008.

I. Rivers, V. P. Poteat, N. Noret, and N. Ashurst, "Observing bullying at school: The mental health implications of witness status," Sch. Psychol. Q., vol. 24, no. 4, p. 211, 2009.

B. Henson, "Bullying beyond the schoolyard: Preventing and responding to cyberbullying," ed: Springer, 2012.

C. Langos, "Cyberbullying: The challenge to define," Cyberpsychol. Behav. Soc. Netw., vol. 15, no. 6, pp. 285-289, 2012.

D. Zhang, E. S. Huebner, and L. Tian, "Longitudinal associations among neuroticism, depression, and cyberbullying in early adolescents," Comput. Hum. Behav., vol. 112, p. 106475, 2020.

M. C. Martínez-Monteagudo, B. Delgado, Á. Díaz-Herrero, and J. M. García-Fernández, "Relationship between suicidal thinking, anxiety, depression and stress in university students who are victims of cyberbullying," Psychiatry Res., vol. 286, p. 112856, 2020.

C. E. Torres, S. J. D’Alessio, and L. Stolzenberg, "The effect of social, verbal, physical, and cyberbullying victimization on academic performance," Vict. Offender, vol. 15, no. 1, pp. 1-21, 2020.

A. Oksanen, R. Oksa, N. Savela, M. Kaakinen, and N. Ellonen, "Cyberbullying victimization at work: Social media identity bubble approach," Comput. Hum. Behav., vol. 109, p. 106363, 2020, doi: 10.1016/j.chb.2020.106363.

T. K. Chan, C. M. Cheung, and Z. W. Lee, "Cyberbullying on social networking sites: A literature review and future research directions," Inf. Manag., vol. 58, no. 3, p. 103411, Mar. 2021, doi: 10.1016/j.im.2020.103411.

P. Fortuna and S. Nunes, "A survey on automatic detection of hate speech in text," ACM Comput. Surv. (CSUR), vol. 51, no. 4, pp. 1-30, 2018.

S. MacAvaney, H.-R. Yao, E. Yang, K. Russell, N. Goharian, and O. Frieder, "Hate speech detection: Challenges and solutions," PLOS One, vol. 14, no. 8, p. e0221152, 2019.

M. Tomaiuolo, G. Lombardo, M. Mordonini, S. Cagnoni, and A. Poggi, "A survey on troll detection," Future Internet, vol. 12, no. 2, p. 31, 2020.

T. Jiang, J. P. Li, A. U. Haq, A. Saboor, and A. Ali, "A Novel Stacking Approach for Accurate Detection of Fake News," IEEE Access, vol. 9, pp. 22626-22639, 2021.

A. Kumar, V. Singh, T. Ali, S. Pal, and J. Singh, "Empirical evaluation of shallow and deep classifiers for rumor detection," in Proc. ICACM 2019, in Advances in Computing and Intelligent Systems, in Algorithms for Intelligent Systems, 2020, pp. 239-252.

S. R. Sahoo and B. Gupta, "Real-time detection of fake account in twitter using machine-learning approach," in Proc. CICT 2019, in Advances in computational intelligence and communication technology, in Advances in Intelligent Systems and Computing, vol. 1086, 2021, pp. 149-159.

E. W. Pamungkas, V. Basile, and V. Patti, "Misogyny detection in twitter: a multilingual and cross-domain study," Inf. Process. Manag., vol. 57, no. 6, p. 102360, 2020.

H.-S. Lee, H.-R. Lee, J.-U. Park, and Y.-S. Han, "An abusive text detection system based on enhanced abusive and non-abusive word lists," Decis. Support Syst., vol. 113, pp. 22-31, 2018.

A. Schmidt and M. Wiegand, "A survey on hate speech detection using natural language processing," in Proc. 5th Int. Workshop Nat. Lang. Process. Soc. Media, Spain, Apr. 2017, pp. 1-10, doi: 10.18653/v1/W17-11.

N. Willard, Cyberbullying and Cyberthreats: responding to the challenge of online social aggression, threats, and distress. Champaign, IL, USA: Research Press, 2007.

T.-M. Song and J. Song, "Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data," Telemat. Inform., vol. 58, p. 101524, 2021.

A. J. Sánchez-Medina, I. Galván-Sánchez, and M. Fernández-Monroy, "Applying artificial intelligence to explore sexual cyberbullying behaviour," Heliyon, vol. 6, no. 1, p. e03218, 2020.

S. Alsafari, S. Sadaoui, and M. Mouhoub, "Hate and offensive speech detection on arabic social media," Online Soc. Netw. Media, vol. 19, p. 100096, 2020.

T. Bosse and S. Stam, "A normative agent system to prevent cyberbullying," in 2011 IEEE/WIC/ACM Int. Conf. Web Intell. Intell. Agent Technol., 2011, pp. 425-430, doi: 10.1109/WI-IAT.2011.24.

K. Dinakar, R. Reichart, and H. Lieberman, "Modeling the detection of textual cyberbullying," in Proc. Int. AAAI Conf. Web Soc. Media, Spain, 2011, vol. 5, no. 1.

S. Agrawal and A. Awekar, "Deep learning for detecting cyberbullying across multiple social media platforms," in Eur. Conf. Inf. Retr. ECIR 2018, in Advances in Information Retrieval, in Lecture Notes in Computer Science, vol. 10772, 2018, pp. 141-153.

Noviantho, S. M. Isa and L. Ashianti, "Cyberbullying classification using text mining," in 2017 1st Int. Conf. Inform. Comput. Sci. (ICICoS), Indonesia, 2017, pp. 241-246, doi: 10.1109/ICICOS.2017.8276369.

J. Zhang, T. Otomo, L. Li, and S. Nakajima, "Cyberbullying Detection on Twitter using Multiple Textual Features," in 2019 IEEE 10th Int. Conf. Aware. Sci. Technol. (iCAST), Japan, 2019, pp. 1-6, doi: 10.1109/ICAwST.2019.8923186.

H. Zhong et al., "Content-Driven Detection of Cyberbullying on the Instagram Social Network," in Proc. 25th Int. Jt. Conf. Artif. Intell. IJCAI, July 2016, pp. 3952-3958.

D. Chatzakou, N. Kourtellis, J. Blackburn, E. De Cristofaro, G. Stringhini, and A. Vakali, "Mean birds: Detecting aggression and bullying on twitter," in Proc. 2017 ACM Web Sci. Conf., USA, June 2017, pp. 13-22, doi: 10.1145/3091478.3091487.

D. Chatzakou et al., "Detecting cyberbullying and cyberaggression in social media," ACM Trans. Web (TWEB), vol. 13, no. 3, pp. 1-51, 2019, doi: 10.1145/3343484.

R. Zhao and K. Mao, "Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder," IEEE Trans. Affect. Comput., vol. 8, no. 3, pp. 328-339, 1 July-Sept. 2017, doi: 10.1109/TAFFC.2016.2531682.

E. Raisi and B. Huang, "Weakly supervised cyberbullying detection using co-trained ensembles of embedding models," in 2018 IEEE/ACM Int. Conf. Adv Soci. Netw. Anal. Min. (ASONAM), Spain, 2018, pp. 479-486, doi: 10.1109/ASONAM.2018.8508240.

B. A. Rachid, H. Azza, and H. H. B. Ghezala, "Classification of Cyberbullying Text in Arabic," in 2020 Int. Jt. Conf. Neural Netw. (IJCNN), UK, 2020, pp. 1-7, doi: 10.1109/IJCNN48605.2020.9206643.

Z. Zhao, M. Gao, F. Luo, Y. Zhang, and Q. Xiong, "LSHWE: Improving Similarity-Based Word Embedding with Locality Sensitive Hashing for Cyberbullying Detection," in 2020 Int. Jt. Conf. Neural Netw. (IJCNN), UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207640.

A. Muneer and S. M. Fati, " A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter," Future Internet, vol. 12, no. 11, p. 187, Oct. 29, 2020, doi: 10.3390/fi12110187.

B. A. Talpur and D. O’Sullivan, "Cyberbullying severity detection: A machine learning approach," PLOS One, vol. 15, no. 10, p. e0240924, 2020, doi: 10.1371/journal.pone.0240924.

M. F. López-Vizcaíno, F. J. Nóvoa, V. Carneiro, and F. Cacheda, "Early detection of cyberbullying on social media networks," Future Gener. Comput. Syst., vol. 118, pp. 219-229, May 2021, doi: 10.1016/j.future.2021.01.006.

D. Soni and V. K. Singh, "See no evil, hear no evil: Audio-visual-textual cyberbullying detection," Proc. ACM Hum.-Comput. Interact., vol. 2, no. CSCW, pp. 1-26, Nov. 2018, doi: 10.1145/3274433.

P. Badjatiya, S. Gupta, M. Gupta, and V. Varma, "Deep learning for hate speech detection in tweets," in Proc. 26th Int. Conf. World Wide Web Companion, Australia, Apr. 2017, pp. 759-760, doi: 10.1145/3041021.3054223.

M. A. Al-Garadi, K. D. Varathan, and S. D. Ravana, "Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network," Comput. Hum. Behav., vol. 63, pp. 433-443, Oct. 2016, doi: 10.1016/j.chb.2016.05.051.

R. I. Rafiq, H. Hosseinmardi, S. A. Mattson, R. Han, Q. Lv, and S. Mishra, "Analysis and detection of labeled cyberbullying instances in Vine, a video-based social network," Soc. Netw. Anal. Min., vol. 6, no. 1, pp. 1-16, 2016.

V. Balakrishnan, S. Khan, and H. R. Arabnia, "Improving cyberbullying detection using Twitter users’ psychological features and machine learning," Comput. Secur., vol. 90, p. 101710, Mar. 2020, doi: 10.1016/j.cose.2019.101710.

N. Yuvaraj et al., "Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking," Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/6644652.

M. Dadvar, F. d. Jong, R. Ordelman, and D. Trieschnigg, "Improved cyberbullying detection using gender information," in Proc. Twelfth Dutch-Belgian Inf. Retr. Workshop (DIR 2012), Belgium, 2012.

L. Cheng, J. Li, Y. N. Silva, D. L. Hall, and H. Liu, "PI-Bully: Personalized Cyberbullying Detection with Peer Influence," in Proc. Twenty-Eighth Int. Jt. Conf. Artif. Intell., 2019, pp. 5829-5835, doi: 10.24963/ijcai.2019/808.

N. Rezvani, A. beheshti, and A. Tabebordbar, "Linking textual and contextual features for intelligent cyberbullying detection in social media," in Proc. 18th Int. Conf. Adv. Mob. Comput. Multimed., 2020, pp. 3-10.

S. Paul, S. Saha, and M. Hasanuzzaman, "Identification of cyberbullying: A deep learning based multimodal approach," Multimed. Tools Appl. (2020), pp. 1-20, Sept. 10, 2020, doi: 10.1007/s11042-020-09631-w.

E. Stamatatos, "On the robustness of authorship attribution based on character n-gram features," J. Law Policy, vol. 21, no. 2, pp. 421-439, 2013.

E. Raff et al., "An investigation of byte n-gram features for malware classification," J. Comput. Virol. Hacking Tech., vol. 14, no. 1, pp. 1-20, 2018.

W. Khreich, B. Khosravifar, A. Hamou-Lhadj, and C. Talhi, "An anomaly detection system based on variable N-gram features and one-class SVM," Inf. Softw. Technol., vol. 91, pp. 186-197, Nov. 2017, doi: 10.1016/j.infsof.2017.07.009.

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," arXiv:1310.4546, 2013, [Online]Available: https://arxiv.org/abs/1310.4546

T. Mikolov, W.-t. Yih, and G. Zweig, "Linguistic regularities in continuous space word representations," in Proc. 2013 Conf. N. Am. Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol., USA, June 2013, pp. 746-751.

Y. Zhang, R. Jin, and Z.-H. Zhou, "Understanding bag-of-words model: a statistical framework," Int. J. Mach. Learn. Cybern., vol. 1, no. 1-4, pp. 43-52, 2010, doi: 10.1007/s13042-010-0001-0.

J. Pennington, R. Socher, and C. D. Manning, "Glove: Global vectors for word representation," in Proc. 2014 Conf. Empir. Methods Nat. Lang. Process. (EMNLP), Qatar, 2014, pp. 1532-1543, doi: 10.3115/v1/D14-1162.

L. Havrlant and V. Kreinovich, "A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation)," Int. J. Gen. Syst., vol. 46, no. 1, pp. 27-36, May 14, 2017, doi: 10.1080/03081079.2017.1291635.

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," Trans. Assoc. Comput. Linguist., vol. 5, pp. 135-146, 2017, doi: 10.1162/tacl_a_00051.

V. Nahar, S. Unankard, X. Li, and C. Pang, "Sentiment analysis for effective detection of cyber bullying," in Asia-Pac. Web Conf., in Web Technologies and Applications, in Lecture Notes in Computer Science, vol. 7235, 2012, pp. 767-774.

J. N. Navarro and J. L. Jasinski, "Going cyber: Using routine activities theory to predict cyberbullying experiences," Sociol. Spectr., vol. 32, no. 1, pp. 81-94, 2012, doi: 10.1080/02732173.2012.628560.

L. Corcoran, C. M. Guckin, and G. Prentice, "Cyberbullying or cyber aggression?: A review of existing definitions of cyber-based peer-to-peer aggression," Soc., vol. 5, no. 2, pp. 245-255, 2015, doi: 10.3390/soc5020245.

P. Gradinger, D. Strohmeier, and C. Spiel, "Definition and measurement of cyberbullying," Cyberpsychology: J. Psychosoc. Res. Cyberspace, vol. 4, no. 2, 2010.

L. A. Zezulka and K. Seigfried-Spellar, "Differentiating cyberbullies and internet trolls by personality characteristics and self-esteem," J. Digit. Forensics, Secur. Law, vol. 11, no. 3, pp. 7-26, Sept. 2016, doi: 10.15394/jdfsl.2016.1415.

T. Kucukyilmaz, B. B. Cambazoglu, C. Aykanat, and F. Can, "Chat mining for gender prediction," in 4th Int. Conf. Adv. Inf. Syst. ADVIS 2006, in Advances in information Systems, in Lecture Notes in Computer Science, vol. 4243, 2006, pp. 274-283.

M. Sap et al., "Developing age and gender predictive lexica over social media," in Proc. 2014 Conf. Empir. Methods Nat. Lang. Process. (EMNLP), Qatar, Oct. 2014, pp. 1146-1151, doi: 10.3115/v1/D14-1121.

C. P. Barlett and K. Chamberlin, "Examining cyberbullying across the lifespan," Comput. Hum. Behav., vol. 71, pp. 444-449, June 2017, doi: 10.1016/j.chb.2017.02.009.

H. A. Schwartz et al., "Personality, gender, and age in the language of social media: The open-vocabulary approach," PLOS one, vol. 8, no. 9, p. e73791, 2013, doi: 10.1371/journal.pone.0073791.

O. P. John and S. Srivastava, “The Big-Five trait taxonomy: History, measurement, and theoretical perspectives,” in Handbook of Personality: Theory and Research, L. A. Pervin and O. P. John, Ed, 2nd ed, New York, USA: Guilford, 1999, ch. 4, pp. 102-138.

E. Mitsopoulou and T. Giovazolias, "Personality traits, empathy and bullying behavior: A meta-analytic approach," Aggress. Violent Behave., vol. 21, pp. 61-72, Mar.-Apr. 2015, doi: 10.1016/j.avb.2015.01.007.

D. Azucar, D. Marengo, and M. Settanni, "Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis," Pers. Individ. Differ., vol. 124, pp. 150-159, Apr. 2018, doi: 10.1016/j.paid.2017.12.018.

H. Wei et al., "Beyond the words: Predicting user personality from heterogeneous information," in Proc. Tenth ACM Int. Conf. Web Search Data Min., NY, USA, Feb. 2017, pp. 305-314, doi: 10.1145/3018661.3018717.

N. K. Singh, D. S. Tomar, and A. K. Sangaiah, "Sentiment analysis: a review and comparative analysis over social media," J. Ambient Intell. Humaniz. Comput., vol. 11, no. 1, pp. 97-117, 2020, doi: 10.1007/s12652-018-0862-8.

F. A. Acheampong, H. Nunoo-Mensah, and W. Chen, "Transformer models for text-based emotion detection: a review of BERT-based approaches," Artif. Intell. Rev., pp. 1-41, Feb. 08, 2021, doi: 10.1007/s10462-021-09958-2.

E. W. Pamungkas, V. Basile, and V. Patti, "A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection," Inf. Process. Manag., vol. 58, no. 4, p. 102544, July 2021, doi: 10.1016/j.ipm.2021.102544.

B. Haidar, M. Chamoun, and A. Serhrouchni, "Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content," in 2017 1st Cyber Secur. Netw. Conf. (CSNet), Brazil, Oct. 18-20, 2017, pp. 1-8, doi: 10.1109/CSNET.2017.8242005.

D. Mouheb, R. Albarghash, M. F. Mowakeh, Z. Al Aghbari, and I. Kamel, "Detection of Arabic Cyberbullying on Social Networks using Machine Learning," in 2019 IEEE/ACS 16th Int. Conf. Comput. Syst. Appl. (AICCSA), UAE, Nov. 3-7, 2019, pp. 1-5, doi: 10.1109/AICCSA47632.2019.9035276.

D. Van Bruwaene, Q. Huang, and D. Inkpen, "A multi-platform dataset for detecting cyberbullying in social media," Lang. Resour. Eval., vol. 54, no. 4, pp. 851-874, Apr. 06, 2020, doi: 10.1007/s10579-020-09488-3.

B. Haidar, M. Chamoun, and A. Serhrouchni, "Arabic Cyberbullying Detection: Enhancing Performance by Using Ensemble Machine Learning," in 2019 Int. Conf. Internet of Things (iThings) IEEE Green Comput. Commun. (GreenCom) IEEE Cyber, Phys. Soc. Comput. (CPSCom) IEEE Smart Data (SmartData), USA, Oct. 21, 2019, pp. 323-327, doi: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00074.

X. Zhang et al., "Cyberbullying detection with a pronunciation based convolutional neural network," in 2016 15th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), USA, Dec. 18-20, 2016, pp. 740-745, doi: 10.1109/ICMLA.2016.0132.

N. Potha, M. Maragoudakis, and D. Lyras, "A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data," Knowl. Based Syst., vol. 96, pp. 134-155, Mar. 15, 2016, doi: 10.1016/j.knosys.2015.12.021.

A. Kumar and N. Sachdeva, "Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network," Multimed. Syst., pp. 1-10, 2021, doi: 10.1007/s00530-020-00747-5.

L. Cheng, R. Guo, Y. Silva, D. Hall, and H. Liu, "Hierarchical attention networks for cyberbullying detection on the instagram social network," in Proc. 2019 SIAM Int. Conference Data Min., 2019, pp. 235-243, doi: 10.1137/1.9781611975673.27.

C. Iwendi, G. Srivastava, S. Khan, and P. K. R. Maddikunta, "Cyberbullying detection solutions based on deep learning architectures," Multimed. Syst., pp. 1-14, Oct. 13, 2020, doi: 10.1007/s00530-020-00701-5.

S. Paul and S. Saha, "CyberBERT: BERT for cyberbullying identification," Multimed. Syst., pp. 1-8, Nov. 11, 2020, doi: 10.1007/s00530-020-00710-4.

A. T. Aind, A. Ramnaney, and D. Sethia, "Q-Bully: A Reinforcement Learning based Cyberbullying Detection Framework," in 2020 Int. Conf. Emerg. Technol. (INCET), India, June 5-7, 2020, pp. 1-6, doi: 10.1109/INCET49848.2020.9154092.

D. A. Freedman, Statistical models: theory and practice. New York, NY, USA: Cambridge Univ. Press, 2009.

D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression. New York, NY, USA: Wiley, 2000.

E. Fix and J. L. Hodges, “Discriminatory analysis: nonparametric discrimination, consistency properties,” Int. Stat. Rev., vol. 57, no. 3, pp. 238-247, Dec. 1989, doi: 10.2307/1403797.

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning: data mining, inference, and prediction. New York, NY, USA: Springer-Verlag, 2009.

D. Steinberg and P. Colla, "CART: classification and regression trees," in The top ten algorithms in data mining, X. Wu and V. Kumar, Ed, Boca Raton, FL, USA: CRC Press, 2009, ch. 10, p. 179.

J. R. Quinlan, C4.5: PROGRAMS FOR MACHINE LEARNING, San Rafael, CA, USA: Morgan Kaufmann, 2014.

G. V. Kass, "An exploratory technique for investigating large quantities of categorical data," J. R. Stat. Soc. Ser. C-Appl. Stat., vol. 29, no. 2, pp. 119-127, 1980, doi: 10.2307/2986296.

D. Biggs, B. De Ville, and E. Suen, "A method of choosing multiway partitions for classification and decision trees," J. Appl. Stat., vol. 18, no. 1, pp. 49-62, 1991, doi: 10.1080/02664769100000005.

W.-Y. Loh and Y.-S. Shih, "Split selection methods for classification trees," Stat. Sin., vol. 7, no. 4, pp. 815-840, Oct. 1997.

C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995, doi: 10.1007/BF00994018.

Y. Ma and G. Guo, Support vector machines applications, New York, NY, USA: Springer, 2014.

B. Kumar, O. Vyas, and R. Vyas, "A comprehensive review on the variants of support vector machines," Mod. Phys. Lett. B, vol. 33, no. 25, p. 1950303, 2019, doi: 10.1142/S0217984919503032.

X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, "A survey on ensemble learning," Front. Comput. Sci., vol. 14, no. 2, pp. 241-258, 2020, doi: 10.1007/s11704-019-8208-z.

L. Breiman, "Bagging predictors," Mach. Learn., vol. 24, no. 2, pp. 123-140, 1996, doi: 10.1007/BF00058655.

R. E. Schapire and Y. Freund, "Boosting: Foundations and algorithms," Kybernetes, vol. 42, no. 1, pp. 164-166, 2013, doi: 10.1108/03684921311295547.

R. E. Schapire, "Explaining adaboost," in Empirical inference, B. Scholkopf, Z. Luo and V. Vovk, Ed. Berlin, Germany: Springer-Verlag, 2013, ch. 5, pp. 37-52.

Y. Freund, "An adaptive version of the boost by majority algorithm," Mach. Learn., vol. 43, no. 3, pp. 293-318, 2001, doi: 10.1023/A:1010852229904.

A. Torralba, K. P. Murphy, and W. T. Freeman, "Shared features for multiclass object detection," in Toward Category-Level Object Recognition, J. Ponce, M. Hebert, C. Schmid and A. Zisserman, Ed. Berlin, Germany: Springer-Verlag, 2006, pp. 345-361.

J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)," Ann. Stat., vol. 28, no. 2, pp. 337-407, Apr. 2000, doi: 10.1214/aos/1016218223.

T. K. Ho, "Random decision forests," in Proc. 3rd Int. Conf. Doc. Anal. Recognit., Canada, Aug. 14-16, 1995, vol. 1, pp. 278-282, doi: 10.1109/ICDAR.1995.598994.

L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001, doi: 10.1023/A:1010933404324.

A. Prinzie and D. Van den Poel, "Random forests for multiclass classification: Random multinomial logit," Expert Syst. Appl., vol. 34, no. 3, pp. 1721-1732, 2008, doi: 10.1016/j.eswa.2007.01.029.

A. Paul, D. P. Mukherjee, P. Das, A. Gangopadhyay, A. R. Chintha, and S. Kundu, "Improved random forest for classification," IEEE Trans. Image Proc., vol. 27, no. 8, pp. 4012-4024, 2018, doi: 10.1109/TIP.2018.2834830.

M. Belgiu and L. Drăguţ, "Random forest in remote sensing: A review of applications and future directions," ISPRS J. Photogramm. Remote Sens., vol. 114, pp. 24-31, 2016, doi: 10.1016/j.isprsjprs.2016.01.011.

M. Kaur, H. K. Gianey, D. Singh, and M. Sabharwal, "Multi-objective differential evolution based random forest for e-health applications," Mod. Phys. Lett. B, vol. 33, no. 05, p. 1950022, 2019, doi: 10.1142/S0217984919500222.

A. B. Shaik and S. Srinivasan, "A brief survey on random forest ensembles in classification model," in Int. Conf. Innov. Comput. Commun., vol. 2, pp. 253-260, 2019, doi: 10.1007/978-981-13-2354-6_27.

E. Scornet, "Random forests and kernel methods," IEEE Trans. Inf. Theory, vol. 62, no. 3, pp. 1485-1500, 2016, doi: 10.1109/TIT.2016.2514489.

A. Davies and Z. Ghahramani, "The random forest kernel and other kernels for big data from random partitions," arXiv:1402.4293, 2014, [Online]Available: https://arxiv.org/abs/1402.4293

R. Sarikaya, G. E. Hinton, and A. Deoras, "Application of deep belief networks for natural language understanding," IEEE/ACM Trans. Audio, Speech, and Language Process., vol. 22, no. 4, pp. 778-784, 2014, doi: 10.1109/TASLP.2014.2303296.

D. W. Otter, J. R. Medina, and J. K. Kalita, "A survey of the usages of deep learning for natural language processing," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 2, pp. 604-624, Feb. 2020, doi: 10.1109/TNNLS.2020.2979670.

Z. Yang, X. He, J. Gao, L. Deng, and A. Smola, "Stacked attention networks for image question answering," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., USA, June 27-30, 2016, pp. 21-29, doi: 10.1109/CVPR.2016.10.

S. Nam, Y. Kim, and S. J. Kim, "Text-adaptive generative adversarial networks: manipulating images with natural language," arXiv:1810.11919, 2018, [Online]Available: https://arxiv.org/abs/1810.11919

B. Jang, M. Kim, G. Harerimana, S.-u. Kang, and J. W. Kim, "Bi-LSTM model to increase accuracy in text classification: combining Word2vec CNN and attention mechanism," Appl. Sci., vol. 10, no. 17, p. 5841, 2020, doi: 10.3390/app10175841.

M. Bi, Q. Zhang, M. Zuo, Z. Xu, and Q. Jin, "Bi-directional LSTM Model with Symptoms-Frequency Position Attention for Question Answering System in Medical Domain," Neural Process. Lett., vol. 51, no. 2, pp. 1185-1199, 2020, doi: 10.1007/s11063-019-10136-3.

Y. LeCun and Y. Bengio, "Convolutional networks for images, speech, and time series," in The handbook of brain theory and neural networks, M. A. Arbib, Ed. Cambridge, MA, USA: MIT Press, Oct. 1998, pp. 255-258.

J. Sun, X. Cao, H. Liang, W. Huang, Z. Chen, and Z. Li, "New interpretations of normalization methods in deep learning," Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 04, pp. 5875-5882.

T. Salimans and D. P. Kingma, "Weight normalization: A simple reparameterization to accelerate training of deep neural networks," arXiv:1602.07868, 2016, [Online]Available: https://arxiv.org/abs/1602.07868

R. Xiong et al., "On layer normalization in the transformer architecture," in Proc. 37th Int. Conf. Mach. Learn., in Proceedings of machine learning research, vol. 119, July 2020, pp. 10524-10533.

Y. Wu and K. He, "Group normalization," in Proc. 15th Eur. Conf. Comput. Vis. (ECCV), in Computer Vision ECCV 2018, in Lecture Notes in Computer Science, vol. 11217, 2018, pp. 3-19.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Commun. ACM, vol. 60, no. 6, pp. 84-90, May 2012, doi: 10.1145/3065386.

K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv:1409.1556, 2014, [Online]Available: https://arxiv.org/abs/1409.1556

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90..

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artif. Intell. Rev., vol. 53, no. 8, pp. 5455-5516, 2020, doi: 10.1007/s10462-020-09825-6.

S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997, doi: 10.1162/neco.1997.9.8.1735.

O. Melamud, J. Goldberger, and I. Dagan, "context2vec: Learning generic context embedding with bidirectional lstm," in Proc. 20th SIGNLL Conf. Comput. Natural Lang. Learn., Berlin, Germany, Aug. 2016, pp. 51-61, doi: 10.18653/v1/K16-1006.

S. Vosoughi, P. Vijayaraghavan, and D. Roy, "Tweet2vec: Learning tweet embeddings using character-level cnn-lstm encoder-decoder," in Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., Italy, July 2016, pp. 1041-1044, doi: 10.1145/2911451.2914762.

C. Wang, M. Li, and A. J. Smola, "Language models with transformers," arXiv:1904.09408, 2019, [Online]Available: https://arxiv.org/abs/1904.09408.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv:1810.04805, 2018, [Online]Available: https://arxiv.org/abs/1810.04805

Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, "Albert: A lite bert for self-supervised learning of language representations," arXiv:1909.11942, 2019, [Online]Available: https://arxiv.org/abs/1909.11942

Z. Jiang, W. Yu, D. Zhou, Y. Chen, J. Feng, and S. Yan, "Convbert: Improving bert with span-based dynamic convolution," arXiv:2008.02496, 2020, [Online]Available: https://arxiv.org/abs/2008.02496

S. Geng, P. Gao, Z. Fu, and Y. Zhang, "RomeBERT: Robust Training of Multi-Exit BERT," arXiv:2101.09755, 2021, [Online]Available: https://arxiv.org/abs/2101.09755

Z. Liu, G. Li, and J. Cheng, "Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing," arXiv:2103.02800, 2021, [Online]Available: https://arxiv.org/abs/2103.02800

R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. Cambridge, MA, USA: MIT press, 2018.

K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, "Deep reinforcement learning: A brief survey," IEEE Signal Process. Mag., vol. 34, no. 6, pp. 26-38, 2017, doi: 10.1109/MSP.2017.2743240.

Y. Chen, Y. Zhou, S. Zhu, and H. Xu, "Detecting offensive language in social media to protect adolescent online safety," in 2012 Int. Conf. Priv. Secur. Risk Trust And 2012 Int. Conf. Soc. Comput., Netherlands, Sept. 2012, pp. 71-80, doi: 10.1109/SocialCom-PASSAT.2012.55.

P. F. Brown, V. J. Della Pietra, P. V. Desouza, J. C. Lai, and R. L. Mercer, "Class-based n-gram models of natural language," Comput. Linguist., vol. 18, no. 4, pp. 467-480, 1992.

M. Ptaszynski et al., "Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization," Int. J. Child Comput. Interact., vol. 8, pp. 15-30, 2016, doi: 10.1016/j.ijcci.2016.07.002

P. Fortuna, J. Soler-Company, and L. Wanner, "How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?," Inf. Process. Manag., vol. 58, no. 3, p. 102524, 2021, doi: 10.1016/j.ipm.2021.102524.

G. Hinton, O. Vinyals, and J. Dean, "Distilling the knowledge in a neural network," arXiv:1503.02531, 2015, [Online]Available: https://arxiv.org/abs/1503.02531.

Y.-J. Choi, B.-J. Jeon, and H.-W. Kim, "Identification of key cyberbullies: A text mining and social network analysis approach," Telemat. Inform., vol. 56, p. 101504, 2021, doi: 10.1016/j.tele.2020.101504

K. Yokotani and M. Takano, "Social contagion of cyberbullying via online perpetrator and victim networks," Comput. Hum. Behav., vol. 119, p. 106719, 2021, doi: 10.1016/j.chb.2021.106719.

P. K. Jonason and G. D. Webster, "The dirty dozen: a concise measure of the dark triad," Psychol. Assess., vol. 22, no. 2, p. 420-432, 2010, doi: 10.1037/a0019265.

B. Çetin, E. Yaman, and A. Peker, "Cyber victim and bullying scale: A study of validity and reliability," Comput. Educ., vol. 57, no. 4, pp. 2261-2271, 2011, doi: 10.1016/j.compedu.2011.06.014.

E. Menesini, A. Nocentini, and P. Calussi, "The measurement of cyberbullying: Dimensional structure and relative item severity and discrimination," Cyberpsychol. Behav. Soc. Netw., vol. 14, no. 5, pp. 267-274, 2011, doi: 10.1089/cyber.2010.0002

L. Cheng, K. Shu, S. Wu, Y. Silva, D. Hall, and H. Liu, "Unsupervised Cyberbullying Detection via Time-Informed Deep Clustering," in 29th ACM Int. Conf. Inf. Knowl. Manag. (CIKM), Virtual Event Ireland, Oct. 2020, pp. 185-194, doi: 10.1145/3340531.3411934.

N. Lu, G. Wu, Z. Zhang, Y. Zheng, Y. Ren, and K. K. R. Choo, "Cyberbullying detection in social media text based on character‐level convolutional neural network with shortcuts," Concurr. Comput., vol. 32, no. 23, p. e5627, 2020, doi: 10.1002/cpe.5627.

P. Zhang, Y. Gao, and S. Chen, "Detect Chinese Cyber Bullying by Analyzing User Behaviors and Language Patterns," in 2019 3rd Int. Symp. Auto. Syst. (ISAS), China, 2019, pp. 370-375, doi: 10.1109/ISASS.2019.8757714.

S. Mishra, S. Prasad, and S. Mishra, "Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020," in Proc. Second Workshop Trolling Aggress. Cyberbullying, France, 2020, pp. 120-125.

S. S. Aluru, B. Mathew, P. Saha, and A. Mukherjee, "Deep learning models for multilingual hate speech detection," arXiv:2004.06465, 2020, [Online]Available: https://arxiv.org/abs/2004.06465

F. Cacheda, D. Fernandez, F. J. Novoa, and V. Carneiro, "Early detection of depression: social network analysis and random forest techniques," J. Med. Int. Res., vol. 21, no. 6, p. e12554, 2019.

K. Kumari, J. P. Singh, Y. K. Dwivedi, and N. P. Rana, "Multi-modal aggression identification using Convolutional Neural Network and Binary Particle Swarm Optimization," Future Gener. Comput. Syst., vol. 118, pp. 187-197, May 2021, doi: 10.1016/j.future.2021.01.014.

S. Sabour, N. Frosst, and G. E. Hinton, "Dynamic routing between capsules," arXiv:1710.09829, 2017, [Online]Available: https://arxiv.org/abs/1710.09829

W. Rawat and Z. Wang, "Deep convolutional neural networks for image classification: A comprehensive review," Neural Comput., vol. 29, no. 9, pp. 2352-2449, 2017, doi: 10.1162/neco_a_00990.

K. Kumari, J. P. Singh, Y. K. Dwivedi, and N. P. Rana, "Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach," Soft Comput., vol. 24, no. 15, pp. 11059-11070, 2020, doi: 10.1007/s00500-019-04550-x.

G. Jacobs, C. Van Hee, and V. Hoste, "Automatic classification of participant roles in cyberbullying: Can we detect victims, bullies, and bystanders in social media text?," Nat. Lang. Eng., pp. 1-26, 2020, doi: 10.1017/S135132492000056X.

E. Garcia-Ceja, M. Riegler, T. Nordgreen, P. Jakobsen, K. J. Oedegaard, and J. Tørresen, "Mental health monitoring with multimodal sensing and machine learning: A survey," Pervasive Mob. Comput., vol. 51, pp. 1-26, 2018, doi: 10.1016/j.pmcj.2018.09.003.

A. B. Shatte, D. M. Hutchinson, and S. J. Teague, "Machine learning in mental health: a scoping review of methods and applications," Psychol. Med., vol. 49, no. 9, pp. 1426-1448, 2019, doi: 10.1017/S0033291719000151.

S. D’Alfonso, "AI in mental health," Curr. Opin. Psychol., vol. 36, pp. 112-117, Dec. 2020, doi: 10.1016/j.copsyc.2020.04.005.

M. Arif, A. Basri, and G. Melibari, "Classification of Anxiety Disorders using Machine Learning Methods: A Literature Review," Insights Biomed. Res, vol. 4, no. 1, pp. 95-110, 2020, doi: 10.36959/584/455.

T. Ranasinghe and M. Zampieri, "Multilingual offensive language identification with cross-lingual embeddings," arXiv:2010.05324, 2020, [Online]Available: https://arxiv.org/abs/2010.05324

A. Kumar and N. Sachdeva, "Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data," Multimed. Syst., pp. 1-15, 2020, doi: 10.1007/s00530-020-00672-7.

A. Malte and P. Ratadiya, "Multilingual cyber abuse detection using advanced transformer architecture," in TENCON 2019-2019 IEEE Reg. 10 Conf. (TENCON), 2019, pp. 784-789, doi: 10.1109/TENCON.2019.8929493.

E. W. Pamungkas and V. Patti, "Cross-domain and cross-lingual abusive language detection: A hybrid approach with deep learning and a multilingual lexicon," in Proc. 57th Annu. Meet. Assoc. Comput. Linguist. Student Res. Workshop, Italy, July 2019, pp. 363-370, doi: 10.18653/v1/P19-2051.

O. Gencoglu, "Cyberbullying detection with fairness constraints," IEEE Internet Comput., vol. 25, no. 1, pp. 20-29, Jan.-Feb. 2020, doi: 10.1109/MIC.2020.3032461.

H. Zhang, A. X. Lu, M. Abdalla, M. McDermott, and M. Ghassemi, "Hurtful words: quantifying biases in clinical contextual word embeddings," in Proc. ACM Conf. Health Inference Learn., Canada, Apr. 2020, pp. 110-120, doi: 10.1145/3368555.3384448.

S. Dev, T. Li, J. M. Phillips, and V. Srikumar, "On measuring and mitigating biased inferences of word embeddings," in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 05, pp. 7659-7666, doi: 10.1609/aaai.v34i05.6267.

B. Hutchinson, V. Prabhakaran, E. Denton, K. Webster, Y. Zhong, and S. Denuyl, "Unintended machine learning biases as social barriers for persons with disabilitiess," ACM SIGACCESS Access. Comput., no. 9, Mar. 2020, doi: 10.1145/3386296.3386305.

M. Wich, H. Al Kuwatly, and G. Groh, "Investigating Annotator Bias with a Graph-Based Approach," in Proc. Fourth Workshop Online Abuse Harms, Nov. 2020, pp. 191-199, doi: 10.18653/v1/2020.alw-1.22.

H. Al Kuwatly, M. Wich, and G. Groh, "Identifying and measuring annotator bias based on annotators’ demographic characteristics," in Proc. Fourth Workshop Online Abuse Harms, Nov. 2020, pp. 184-190, doi: 10.18653/v1/2020.alw-1.21.

J. Li et al., "Textshield: Robust text classification based on multimodal embedding and neural machine translation," in 29th {USENIX} Secur. Symp. ({USENIX} Security 20), 2020, pp. 1381-1398.

G. Kovács, P. Alonso, and R. Saini, "Challenges of Hate Speech Detection in Social Media," SN Comput. Sci., vol. 2, no. 2, pp. 1-15, 2021, doi: 10.1007/s42979-021-00457-3.

D. Weber and F. Neumann, "Who's in the Gang? Revealing Coordinating Communities in Social Media," arXiv:2010.08180, 2020, [Online]Available: https://arxiv.org/abs/2010.08180

S. Nikiforos, S. Tzanavaris, and K.-L. Kermanidis, "Virtual learning communities (VLCs) rethinking: influence on behavior modification—bullying detection through machine learning and natural language processing," J. Comput. Educ., vol. 7, pp. 531-551, 2020, doi: 10.1007/s40692-0s20-00166-5.

S. Nikiforos, S. Tzanavaris, and K.-L. Kermanidis, "Virtual learning communities (VLCs) rethinking: Collaboration between learning communities," Educ. Inf. Technol., vol. 25, pp. 3659-3675, 2020, doi: 10.1007/s10639-020-10132-4.

I. Fleury and E. Dowdy, "Social Media Monitoring of Students for Harm and Threat Prevention: Ethical Considerations for School Psychologists," Contemp. School Psychol., 2020, doi: 10.1007/s40688-020-00311-y.

R. Clarke, "Information technology and dataveillance," Commun. ACM, vol. 31, no. 5, pp. 498-512, 1988, doi: 10.1145/42411.42413.

R. Clarke and G. Greenleaf, "Dataveillance regulation: A research framework," J. Law Inf. Sci., vol. 25, no. 1, 2018.