Araştırma Makalesi
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Can Artificial Intelligence Predict Cyberbullying?

Yıl 2023, Cilt: 4 Sayı: 1, 1 - 21, 30.04.2023

Öz

This study was carried out to contribute to the literature and to define and compare the algorithms used in the detection of cyberbullying. Research articles published in academic journals in the last 10 years, in Turkish and English, whose full text can be accessed, were included in the study. The literature search was conducted with the keywords “Cyber Bullying”, “Guess” and “Artificial Intelligence” in Turkish and “Predicting Cyberbullying” and “Artificial Intelligence” in English from Google Scholar, ProQuest, ScienceDirect, Scopus, Wiley Online Library and Pubmed online databases. Since no results could be reached with Turkish keywords, only English keywords were made in October 2022 with the joint decision of the researchers. 19 research articles were included in the study. Eighteen of the included studies are in English and one is in Turkish. The aim of the studies is to detect and/or prevent cyberbullying with the help of algorithms based on artificial intelligence. As a result of the studies examined, the proposed artificial intelligence models have achieved successful results in achieving their goals for cyberbullying. In one of the studies, an artificial intelligence model was developed that can detect cyberbullying from the message content and prevent the message from being sent. In addition, artificial intelligence applications have been able to successfully detect cyberbullying not only in online social networks, but also in networks used in-house at workplaces. A contribution has been made to the literature on the basis of the theory that cyberbullies abuse cyber victims by using an accusatory language in social media. Studies on cyberbullying usually include text-based analysis, but it is recommended to develop techniques that can analyze images, videos and sounds in order to better detect cyberbullying. Additional updates to apps can be scaled up to perform real-time text prediction before programs suggest any abusive content to the user for typing.

Kaynakça

  • 1. Olweus D, Limber SP. Some problems with cyberbullying research. Curr Opin Psychol. 2018;19:139-43.
  • 2. Vanderbilt D, Augustyn M. The effects of bullying. Paediatrics and Child Health. 2010;20:315-20.
  • 3. Thomas HJ, Connor JP, Scott JG. Integrating Traditional Bullying and Cyberbullying: Challenges of Definition and Measurement in Adolescents – a Review. Educational Psychology Review. 2015;27(1):135-52.
  • 4. Slonje R, Smith PK, FriséN A. The nature of cyberbullying, and strategies for prevention. Comput Hum Behav. 2013;29(1):26-32.
  • 5. Sangwan SR, Bhatia MPS. Soft computing for abuse detection using cyber-physical and social big data in cognitive smart cities. Expert Systems. 2022;39(5):e12766.
  • 6. Huang Y-y, Chou C. An analysis of multiple factors of cyberbullying among junior high school students in Taiwan. Computers in Human Behavior. 2010;26:1581-90.
  • 7. Li J, Hesketh T. Experiences and Perspectives of Traditional Bullying and Cyberbullying Among Adolescents in Mainland China-Implications for Policy. Frontiers in Psychology. 2021;12.
  • 8. Rajesh S, Sharanya B. Recognition and prevention of cyberharassment in social media using classification algorithms. Materials Today: Proceedings. 2021.
  • 9. Yazğılı E, Baykara M. Türkçe metinlerde makine öğrenmesi yöntemleri ile siber zorbalık tespiti. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2022;12(2):443-53.
  • 10. Al-Marghilani A. Artificial Intelligence-Enabled Cyberbullying-Free Online Social Networks in Smart Cities. International Journal of Computational Intelligence Systems. 2022;15(1):9.
  • 11. Aliyu N, Abdulrahaman M, Ajibade F, Abdurauf T. Analysis of Cyber Bullying on Facebook Using Text Mining. Journal of Applied Artificial Intelligence. 2020;1:1-12.
  • 12. Balakrishna S, Gopi Y, Solanki VK. Comparative analysis on deep neural network models for detection of cyberbullying on Social Media. Ingeniería Solidaria. 2022;18(1):1-33.
  • 13. Balakrishnan V, Ng S. Personality and emotion based cyberbullying detection on YouTube using ensemble classifiers. Behaviour & Information Technology. 2022:1-12.
  • 14. Fang Y, Yang S, Zhao B, Huang C. Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism. Information [Internet]. 2021; 12(4).
  • 15. Hamlett M, Powell G, Silva YN, Hall D. A Labeled Dataset for Investigating Cyberbullying Content Patterns in Instagram. Proceedings of the International AAAI Conference on Web and Social Media. 2022;16(1):1251-8.
  • 16. Ho SM, Li W. “I know you are, but what am I?” Profiling cyberbullying based on charged language. Computational and Mathematical Organization Theory. 2022.
  • 17. Kompally P, Sethuraman SC, Walczak S, Johnson S, Cruz MV. MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content. Applied Sciences [Internet]. 2021; 11(18).
  • 18. Muneer A, Fati SM. A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet [Internet]. 2020; 12(11).
  • 19. Paruchuri VL, Rajesh P. CyberNet: a hybrid deep CNN with N-gram feature selection for cyberbullying detection in online social networks. Evolutionary Intelligence. 2022.
  • 20. Song T-M, Song J. Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data. Telematics and Informatics. 2021;58:101524.
  • 21. Tahmasbi N, Rastegari E. A Socio-Contextual Approach in Automated Detection of Public Cyberbullying on Twitter. ACM Transactions on Social Computing. 2018;1:1-22.
  • 22. Toapanta M, Recalde Monar JA, Mafla E. Prototype to Perform Audit in Social Networks to Determine Cyberbullying2020.
  • 23. Urbaniak R, Ptaszyński M, Tempska P, Leliwa G, Brochocki M, Wroczyński M. Personal attacks decrease user activity in social networking platforms. Computers in Human Behavior. 2022;126:106972.
  • 24. Wan Ali WNH, Mohd M, Fauzi F, Shirai K, Noor M. Implemention of hyperparameter optimisation and over-sampling in detecting cyberbullying using machine learning approach. Malaysian Journal of Computer Science. 2021:78-100.
  • 25. Yi P, Zubiaga A. Session-based Cyberbullying Detection in Social Media: A Survey. arXiv preprint arXiv:220710639. 2022.
  • 26. Zambrano P, Torres J, Yánez Á, Macas A, Tello-Oquendo L. Understanding cyberbullying as an information security attack—life cycle modeling. Annals of Telecommunications. 2021;76(3):235-53.
  • 27. Oksanen A, Oksa R, Savela N, Kaakinen M, Ellonen N. Cyberbullying victimization at work: Social media identity bubble approach. Computers in Human Behavior. 2020;109:106363.
  • 28. Kowalski RM, Toth A, Morgan M. Bullying and cyberbullying in adulthood and the workplace. The Journal of Social Psychology. 2018;158(1):64-81.
  • 29. Oksa R, Saari T, Kaakinen M, Oksanen A. The Motivations for and Well-Being Implications of Social Media Use at Work among Millennials and Members of Former Generations. International Journal of Environmental Research and Public Health [Internet]. 2021; 18(2).

Yapay Zeka Siber Zorbalığı Önceden Tahmin Edebilir mi?

Yıl 2023, Cilt: 4 Sayı: 1, 1 - 21, 30.04.2023

Öz

Bu çalışma literatüre katkı sağlamak ve siber zorbalığın tespitinde kullanılan algoritmaların tanımlanması ve karşılaştırılma amacıyla yapılmıştır. Çalışmaya Türkçe ve İngilizce dillerinde, son 10 yıl içinde akademik dergilerde yayınlanmış olan, tam metnine ulaşılabilen araştırma makaleleri dahil edilmiştir. Literatür taraması Google Scholar, ProQuest, Science Direct, Scopus, Wiley Online Library ve Pubmed çevrimiçi veri tabanlarından Türkçe “Siber Zorbalık”, “Tahmin Etme” ve “Yapay Zeka” ve İngilizce “Predicting Cyberbullying” ve “Artificial Intelligence” anahtar kelimeleri ile yapılmış ancak Türkçe anahtar kelimelerle herhangi bir sonuca ulaşılamadığından araştırmacıların ortak kararı ile yalnızca İngilizce anahtar kelimeler Ekim 2022’de yapılmıştır. Çalışmaya 19 araştırma makalesi dahil edilmiştir. Dahil edilen çalışmaların 18 tanesi İngilizce 1 tanesi Türkçedir. Çalışmaların amacı yapay zekaya dayanan algoritmalar yardımı ile siber zorbalığın tespit edilmesi ve/veya önlenmesidir. İncelenen çalışmalar sonucunda önerilen yapay zeka modelleri siber zorbalığa yönelik amaçlarını gerçekleştirme konusunda başarılı sonuçlar elde etmiştir. Çalışmalardan birinde siber zorbalığı mesaj içeriğinden tespit ederek mesajın gönderilmesini engelleyebilen bir yapay zeka modeli geliştirilmiştir. Ayrıca yapa zeka uygulamaları yalnızca çevrimiçi sosyal ağlarda değil, iş yerlerinde kurum içi kullanılan ağlarda da siber zorbalık başarılı bir şekilde tespit edilmiştir. Siber zorbaların sosyal medyada suçlayıcı bir dil kullanarak siber kurbanları istismar etmesi teorisi temelinde literatüre katkı sağlanmıştır. Siber zorbalıkla ilgili çalışmalar genellikle metin tabanlı analizleri içermektedir ancak siber zorbalığı daha iyi bir şekilde tespit edebilmek için resim, video ve sesleri analiz edebilen tekniklerin geliştirilmesi önerilmektedir. Uygulamalarda yapılabilecek ek güncellemeler programlar kullanıcıya yazması için herhangi bir kötüye kullanım içeriği önermeden önce gerçek zamanlı metin tahmini yapılacak şekilde ölçeklendirilebilir.

Kaynakça

  • 1. Olweus D, Limber SP. Some problems with cyberbullying research. Curr Opin Psychol. 2018;19:139-43.
  • 2. Vanderbilt D, Augustyn M. The effects of bullying. Paediatrics and Child Health. 2010;20:315-20.
  • 3. Thomas HJ, Connor JP, Scott JG. Integrating Traditional Bullying and Cyberbullying: Challenges of Definition and Measurement in Adolescents – a Review. Educational Psychology Review. 2015;27(1):135-52.
  • 4. Slonje R, Smith PK, FriséN A. The nature of cyberbullying, and strategies for prevention. Comput Hum Behav. 2013;29(1):26-32.
  • 5. Sangwan SR, Bhatia MPS. Soft computing for abuse detection using cyber-physical and social big data in cognitive smart cities. Expert Systems. 2022;39(5):e12766.
  • 6. Huang Y-y, Chou C. An analysis of multiple factors of cyberbullying among junior high school students in Taiwan. Computers in Human Behavior. 2010;26:1581-90.
  • 7. Li J, Hesketh T. Experiences and Perspectives of Traditional Bullying and Cyberbullying Among Adolescents in Mainland China-Implications for Policy. Frontiers in Psychology. 2021;12.
  • 8. Rajesh S, Sharanya B. Recognition and prevention of cyberharassment in social media using classification algorithms. Materials Today: Proceedings. 2021.
  • 9. Yazğılı E, Baykara M. Türkçe metinlerde makine öğrenmesi yöntemleri ile siber zorbalık tespiti. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2022;12(2):443-53.
  • 10. Al-Marghilani A. Artificial Intelligence-Enabled Cyberbullying-Free Online Social Networks in Smart Cities. International Journal of Computational Intelligence Systems. 2022;15(1):9.
  • 11. Aliyu N, Abdulrahaman M, Ajibade F, Abdurauf T. Analysis of Cyber Bullying on Facebook Using Text Mining. Journal of Applied Artificial Intelligence. 2020;1:1-12.
  • 12. Balakrishna S, Gopi Y, Solanki VK. Comparative analysis on deep neural network models for detection of cyberbullying on Social Media. Ingeniería Solidaria. 2022;18(1):1-33.
  • 13. Balakrishnan V, Ng S. Personality and emotion based cyberbullying detection on YouTube using ensemble classifiers. Behaviour & Information Technology. 2022:1-12.
  • 14. Fang Y, Yang S, Zhao B, Huang C. Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism. Information [Internet]. 2021; 12(4).
  • 15. Hamlett M, Powell G, Silva YN, Hall D. A Labeled Dataset for Investigating Cyberbullying Content Patterns in Instagram. Proceedings of the International AAAI Conference on Web and Social Media. 2022;16(1):1251-8.
  • 16. Ho SM, Li W. “I know you are, but what am I?” Profiling cyberbullying based on charged language. Computational and Mathematical Organization Theory. 2022.
  • 17. Kompally P, Sethuraman SC, Walczak S, Johnson S, Cruz MV. MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content. Applied Sciences [Internet]. 2021; 11(18).
  • 18. Muneer A, Fati SM. A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet [Internet]. 2020; 12(11).
  • 19. Paruchuri VL, Rajesh P. CyberNet: a hybrid deep CNN with N-gram feature selection for cyberbullying detection in online social networks. Evolutionary Intelligence. 2022.
  • 20. Song T-M, Song J. Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data. Telematics and Informatics. 2021;58:101524.
  • 21. Tahmasbi N, Rastegari E. A Socio-Contextual Approach in Automated Detection of Public Cyberbullying on Twitter. ACM Transactions on Social Computing. 2018;1:1-22.
  • 22. Toapanta M, Recalde Monar JA, Mafla E. Prototype to Perform Audit in Social Networks to Determine Cyberbullying2020.
  • 23. Urbaniak R, Ptaszyński M, Tempska P, Leliwa G, Brochocki M, Wroczyński M. Personal attacks decrease user activity in social networking platforms. Computers in Human Behavior. 2022;126:106972.
  • 24. Wan Ali WNH, Mohd M, Fauzi F, Shirai K, Noor M. Implemention of hyperparameter optimisation and over-sampling in detecting cyberbullying using machine learning approach. Malaysian Journal of Computer Science. 2021:78-100.
  • 25. Yi P, Zubiaga A. Session-based Cyberbullying Detection in Social Media: A Survey. arXiv preprint arXiv:220710639. 2022.
  • 26. Zambrano P, Torres J, Yánez Á, Macas A, Tello-Oquendo L. Understanding cyberbullying as an information security attack—life cycle modeling. Annals of Telecommunications. 2021;76(3):235-53.
  • 27. Oksanen A, Oksa R, Savela N, Kaakinen M, Ellonen N. Cyberbullying victimization at work: Social media identity bubble approach. Computers in Human Behavior. 2020;109:106363.
  • 28. Kowalski RM, Toth A, Morgan M. Bullying and cyberbullying in adulthood and the workplace. The Journal of Social Psychology. 2018;158(1):64-81.
  • 29. Oksa R, Saari T, Kaakinen M, Oksanen A. The Motivations for and Well-Being Implications of Social Media Use at Work among Millennials and Members of Former Generations. International Journal of Environmental Research and Public Health [Internet]. 2021; 18(2).
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal Psikoloji
Bölüm Araştırma Makaleleri
Yazarlar

Sümeyye Kavici Porsuk 0000-0003-3579-8545

Burak Şirin 0000-0002-8485-5756

Erken Görünüm Tarihi 30 Nisan 2023
Yayımlanma Tarihi 30 Nisan 2023
Gönderilme Tarihi 29 Ekim 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 4 Sayı: 1

Kaynak Göster

Vancouver Kavici Porsuk S, Şirin B. Yapay Zeka Siber Zorbalığı Önceden Tahmin Edebilir mi?. MEYAD Akademi. 2023;4(1):1-21.