Cyber Harassment Prediction In Social Media Using Word CNN
Keywords:
CNNAbstract
Information and Communication Technologies have propelled social networking and communication, but cyber bullying poses significant challenges. Existing user-dependent mechanisms for reporting and blocking cyber bullying are manual and inefficient. Conventional Machine Learning and Transfer Learning approaches were explored for automatic cyber bullying detection. The study utilized a comprehensive dataset and structured annotation process. Textual, sentiment and emotional, static and contextual word embeddings, psycholinguistics, term lists, and toxicity features were employed in the Conventional Machine Learning approach. This research introduced the use of toxicity features for cyber bullying detection. Contextual embeddings of word Convolutional Neural Network (Word CNN) demonstrated comparable performance, with embeddings chosen for its higher F-measure. Textual features, embeddings, and toxicity features set new benchmarks when fed individually. This outperformed Linear SVC in terms of training time and handling high-dimensionality features. Transfer Learning utilized Word CNN for fine- tuning, achieving a faster training computation compared to the base models. Additionally, cyber bullying detection through Flask web was implemented, yielding an accuracy of 97.06%. The reference to the specific dataset name was omitted for privacy.
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[1]. B. Cagirkan and G. Bilek, ‘‘Cyberbullying among Turkish high school students,’’ Scandin. J. Psychol., vol. 62, no. 4, pp. 608–616, Aug. 2021, doi: 10.1111/sjop.12720.
[2]. P. T. L. Chi, V. T. H. Lan, N. H. Ngan, and N.
T. Linh, ‘‘Online time, experience of cyber bullying and practices to cope with it among high school students in Hanoi,’’ Health Psychol. Open, vol. 7, no. 1, Jan. 2020, Art. no. 205510292093574, doi: 10.1177/2055102920935747.
[3]. A. López-Martínez, J. A. García-Díaz, R. Valencia-García, and A. Ruiz-Martínez, ‘‘CyberDect. A novel approach for cyberbullying detection on Twitter,’’ in Proc. Int. Conf. Technol. Innov., Guayaquil, Ecuador: Springer, 2019, pp. 109–121, doi: 10.1007/978-3-030-34989-9_9.
[4]. R. M. Kowalski and S. P. Limber, ‘‘Psychological, physical, and academic correlates of cyberbullying and traditional bullying,’’ J. Adolescent Health, vol. 53, no. 1, pp. S13–S20, Jul. 2013, doi: 10.1016/j.jadohealth.2012.09.018.
[5]. Y.-C. Huang, ‘‘Comparison and contrast of piaget and Vygotsky’s theo-ries,’’ in Proc. Adv. Social Sci., Educ. Humanities Res., 2021, pp. 28–32, doi: 10.2991/assehr.k.210519.007.
[6]. A. Anwar, D. M. H. Kee, and A. Ahmed, ‘‘Workplace cyberbullying and interpersonal deviance: Understanding the mediating effect of silence and emotional exhaustion,’’ Cyberpsychol., Behav., Social Netw., vol. 23, no. 5, pp. 290–296, May 2020, doi: 10.1089/cyber.2019.0407.
[7]. D. M. H. Kee, M. A. L. Al-Anesi, and S. A. L.
Al-Anesi, ‘‘Cyberbul-lying on social media under the influence of COVID-19,’’ Global Bus. Organizational Excellence, vol. 41, no. 6, pp. 11–22, Sep. 2022, doi: 10.1002/joe.22175.
[8]. I. Kwan, K. Dickson, M. Richardson, W. MacDowall, H. Burchett, C. Stansfield, G. Brunton,
K. Sutcliffe, and J. Thomas, ‘‘Cyberbullying and children and young people’s mental health: A systematic map of systematic reviews,’’ Cyberpsychol., Behav., Social Netw., vol. 23, no. 2,
pp. 72–82, Feb. 2020, doi: 10.1089/cyber.2019.0370.
[9]. R. Garett, L. R. Lord, and S. D. Young, ‘‘Associations between social media and cyberbullying: A review of the literature,’’ mHealth, vol. 2, p. 46, Dec. 2016, doi: 10.21037/mhealth.2016.12.01.
[10]. M. Ptaszynski, F. Masui, Y. Kimura, R. Rzepka, and K. Araki, ‘‘Automatic extraction of harmful sentence patterns with application in cyberbullying detection,’’ in Proc. Lang. Technol. Conf. Poznań, Poland: Springer, 2015, pp. 349–362, doi: 10.1007/978-3-319-93782-3_25.
[11]. M. Ptaszynski, P. Lempa, F. Masui, Y. Kimura,
R. Rzepka, K. Araki, M. Wroczynski, and G. Leliwa, ‘‘"Brute-force sentence pattern extortion from harmful messages for cyberbullying detection,’’’ J. Assoc. Inf. Syst., vol. 20, no. 8, pp. 1075–1127, 2019.
[12]. M. O. Raza, M. Memon, S. Bhatti, and R. Bux, ‘‘Detecting cyber-bullying in social commentary using supervised machine learning,’’ in Proc. Future Inf. Commun. Conf. Cham, Switzerland: Springer, 2020, pp. 621–630.
[13]. D. Nguyen, M. Liakata, S. Dedeo, J. Eisenstein, D. Mimno, R. Tromble, and J. Winters, ‘‘How we do things with words: Analyzing text as social and cultural data,’’ Frontiers Artif. Intell., vol. 3, p. 62, Aug. 2020, doi: 10.3389/frai.2020.00062.
[14]. J. Cai, J. Li, W. Li, and J. Wang, ‘‘Deeplearning model used in text classification,’’ in Proc. 15th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process. (ICCWAMTIP), Dec. 2018,
pp. 123–126, doi: 10.1109/ICCWAMTIP.2018.8632592.
[15]. N. Tiku and C. Newton. Twitter CEO: We Suck at Dealing With Abuse. Verge. Accessed: Aug. 17, 2022. [Online]. Available: https://www.theverge.com/2015/2/4/7982099/twitte r-ceo-sent-memo-taking-personal-responsibility- for-the
[16]. D. Noever, ‘‘Machine learning suites for online toxicity detection,’’ 2018, arXiv:1810.01869.
[17]. D. G. Krutka, S. Manca, S. M. Galvin, C. Greenhow, M. J. Koehler, and E. Askari, ‘‘Teaching ‘against’ social media: Confronting prob-lems of profit in the curriculum,’’ Teachers College Rec., Voice Scholarship Educ., vol. 121, no. 14, pp. 1–42, Dec. 2019, doi: 10.1177/016146811912101410.
[18]. H. Rosa, N. Pereira, R. Ribeiro, P. C. Ferreira,
J. P. Carvalho, S. Oliveira, L. Coheur, P. Paulino, A.
M. V. Simão, and I. Trancoso, ‘‘Automatic cyberbullying detection: A systematic review,’’ Comput. Hum. Behav., vol. 93, pp. 333–345, Apr. 2019, doi: 10.1016/j.chb.2018.12.021.
[19]. S. Bharti, A. K. Yadav, M. Kumar, and D. Yadav, ‘‘Cyberbullying detection from tweets using deep learning,’’ Kybernetes, vol. 51, no. 9, pp. 2695–2711, Sep. 2022.
[20]. A. Bozyiğit, S. Utku, and E. Nasibov, ‘‘Cyberbullying detection: Uti-lizing social media features,’’ Expert Syst. Appl., vol. 179, Oct. 2021, Art. no. 115001, doi: 10.1016/j.eswa.2021.115001. [21]. 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, Sep. 2018, doi: 10.1016/j.dss.2018.06.009.
[22]. Y. Fang, S. Yang, B. Zhao, and C. Huang, ‘‘Cyberbullying detection in social networks using bi-GRU with self-attention mechanism,’’ Informa- tion, vol. 12, no. 4, p. 171, Apr. 2021, doi: 10.3390/info12040171.
[23]. G. Jacobs, C. Van Hee, and V. Hoste, ‘‘Automatic classification of partici-pant roles in cyberbullying: Can we detect victims, bullies, and bystanders in social media text?’’ Natural Lang. Eng., vol. 28, no. 2, pp. 141–166, Mar. 2022, doi: 10.1017/S135132492000056X.
[24]. M. Gada, K. Damania, and S. Sankhe, ‘‘Cyberbullying detection using LSTM-CNN architecture and its applications,’’ in Proc. Int. Conf. Comput. Commun. Informat. (ICCCI), Jan. 2021,
pp. 1–6, doi: 10.1109/ICCCI50826.2021.9402412.
[25]. H. H.-P. Vo, H. Trung Tran, and S. T. Luu, ‘‘Automatically detecting cyberbullying comments on online game forums,’’ in Proc. RIVF Int. Conf. Comput. Commun. Technol. (RIVF), Aug. 2021, pp. 1–5, doi: 10.1109/RIVF51545.2021.9642116.
