Abstract
The current study attempts to investigate the influence of virtual learning communities (VLCs) on behavior modification, through the bullying paradigm, using natural language processing (NLP) techniques. The key question is whether individual learners that bully in their physical learning community (PLC) can be able to exhibit a behavior modification, if integrated in a VLC. Results indicate that the attempted "integration" could be a promising framework to behavior modification via a virtual community. Furthermore, machine learning is employed for the automatic detection of aggressive behavior that can facilitate the timely teacher's intervention, without him having to manually scan through the textual dataset. To the authors' knowledge, this is the first time such a linguistic and behavioral analysis for bullying detection is applied to VLCs. Another innovative challenge is the language targeted in the analysis, namely Greek.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agrawal, S., & Awekar, A. (2018, March). Deep learning for detecting cyberbullying across multiple social media platforms. In European Conference on Information Retrieval (pp. 141–153). Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_11.
Antoniadou, N., & Kokkinos, C. M. (2015). A review of research on cyber-bullying in Greece. International Journal of Adolescence and Youth, 20(2), 185–201. https://doi.org/10.1080/02673843.2013.778207.
Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4), 555–596. https://doi.org/10.1162/coli.07-034-R2.
Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017, April). Deep learning for hate speech detection in tweets. In Proceedings of the 26th international conference on World Wide Web companion (pp. 759–760). https://doi.org/10.1145/3041021.3054223.
Barana A., Brancaccio A., Esposito M., Fioravera M., Marchisio M., Pardini C. & Rabellino S. (2017). Problem solving competence developed through a virtual learning environment in a European context. In Proceedings of the 13th international scientific conference “eLearning and Software for Education”, 1, 455–463. https://doi.org/10.12753/2066-026X-17-067.
Battistich, V., & Hom, A. (1997). The relationship between students' sense of their school as a community and their involvement in problem behaviors. American Journal of Public Health, 87(12), 1997–2001. https://doi.org/10.2105/AJPH.87.12.1997.
Blanchard, A. L., & Markus, M. L. (2004). The experienced sense of a virtual community: Characteristics and processes. ACM Sigmis Database, 35(1), 64–79. https://doi.org/10.1145/968464.968470.
Bereiter, C., & Scardamalia, M. (2018). Fixing Humpty-Dumpty: Putting higher-order skills and knowledge together again. In L. Kerslake & R. Wegerif (Eds.), Theory of teaching thinking: International perspectives (pp. 72–87). London, UK: Routledge.
Bosse, T., & Stam, S. (2011). A normative agent system to prevent cyberbullying. Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE/WIC/ACM International Conference, 2, 425–430. https://doi.org/10.1109/WI-IAT.2011.24.
Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016, September). Deep Learning with H2O. Retrieved from https://h2o.ai/resources.
Cao, W., Wang, X., Ming, Z., & Gao, J. (2018). A review on neural networks with random weights. Neurocomputing, 275, 278–287. https://doi.org/10.1016/j.neucom.2017.08.040.
Chamba-Eras, L., Arruarte, A., & Elorriaga, J. A. (2016, November). Bayesian networks to predict reputation in Virtual Learning Communities. In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1–6). IEEE. https://doi.org/10.1109/LA-CCI.2016.7885721.
Chen, B., & Luppicini, R. (2017). The new era of bullying: A phenomenological study of university students' past experience with cyberbullying. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 7(2), 72–90. https://doi.org/10.4018/IJCBPL.2017040106.
Dadvar, M., & De Jong, F. (2012). Cyberbullying detection: A step toward a safer internet yard. In Proceedings of the 21st international conference On World Wide Web (pp. 121–126). ACM. https://doi.org/10.1145/2187980.2187995.
Dawson, S. (2006). A study of the relationship between student communication interaction and sense of community. The Internet and Higher Education, 9(3), 153–162. https://doi.org/10.1016/j.iheduc.2006.06.007.
Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7(3–4), 197–387. https://doi.org/10.1561/2000000039.
Di Capua, M., Di Nardo, E., & Petrosino, A. (2016, December). Unsupervised cyber bullying detection in social networks. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 432–437). IEEE. https://doi.org/10.1109/ICPR.2016.7899672
Dinakar, K., Jones, B., Havasi, C., Lieberman, H., & Picard, R. (2012). Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(3), 18. https://doi.org/10.1145/2362394.2362400.
Doumen, S., Verschueren, K., Buyse, E., Germeijs, V., Luyckx, K., & Soenens, B. (2008). Reciprocal relations between teacher–child conflict and aggressive behavior in kindergarten: A three-wave longitudinal study. Journal of Clinical Child & Adolescent Psychology, 37(3), 588–599. https://doi.org/10.1080/15374410802148079.
Engeström, Y. (1999). Activity Theory and individual and social transformation. In Engeström Y. et al. (Εds). Perspectives on activity theory: Learning in doing: Social, cognitive & computational perspective (pp. 19–39). New York: Cambridge University Press.
Englander, E. K., & Muldowney, A. M. (2007). Just Turn the Darn Thing Off: Understanding Cyberbullying. In: Proceedings of persistently safe schools: The 2007 national conference on safe schools.
Erdur-Baker, Ö. (2010). Cyberbullying and its correlation to traditional bullying, gender and frequent and risky usage of internet-mediated communication tools. New Media & Society, 12(1), 109–125. https://doi.org/10.1177/1461444809341260.
Fekkes, M., Pijpers, F. I., & Verloove-Vanhorick, S. P. (2004). Bullying: Who does what, when and where? Involvement of children, teachers and parents in bullying behavior. Health Education Research, 20(1), 81–91. https://doi.org/10.1093/her/cyg100.
Fioravera, M., Marchisio, M., Di Caro, L., & Rabellino, S. (2018). Alignment of content, prerequisites and educational objectives: towards automated mapping of digital learning resources. In The 14th International Scientific Conference eLearning and Software for Education (Vol. 4, pp. 335–342). https://doi.org/10.12753/2066-026X-18-261.
Flor, M., Yoon, S. Y., Hao, J., Liu, L., von Davier, A. A. (2016). Automated classification of collaborative problem solving interactions in simulated science tasks. In Proceedings of the 11th workshop on innovative use of NLP for building educational applications (pp. 31–41). San Diego, California. Association for Computational Linguistics. June 16.
García-García, C., Chulvi, V., & Royo, M. (2017). Knowledge generation for enhancing design creativity through co-creative Virtual Learning Communities. Thinking Skills and Creativity, 24, 12–19. https://doi.org/10.1016/j.tsc.2017.02.009.
Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18–33. https://doi.org/10.1177/074171369704800103.
Hai-Jew, S. (2019). Running a ‘Deep Learning’ artificial neural network in RapidMiner Studio. C2C Digital Magazine, 1(10), 17.
Haidar, B., Chamoun, M., & Serhrouchni, A. (2017). A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Advances in Science, Technology and Engineering Systems Journal, 2(6), 275–284.
Hiltz, S. R. (1985). Online communities: A case study of the office of the future. Bristol: Intellect Books.
Hinduja, S., & Patchin, J. W. (2017). Cultivating youth resilience to prevent bullying and cyberbullying victimization. Child Abuse & Neglect, 73, 51–62. https://doi.org/10.1016/j.chiabu.2017.09.010.
Hod, Y., Bielaczyc, K., & Ben-Zvi, D. (2018). Revisiting learning communities: Innovations in theory and practice. Instructional Science, 46(4), 489–506. https://doi.org/10.1007/s11251-018-9467-z.
Ioannidis, J. P., Tarone, R., & McLaughlin, J. K. (2011). The false-positive to false-negative ratio in epidemiologic studies. Epidemiology, 22(4), 450–456. https://doi.org/10.1097/EDE.0b013e31821b506e.
Jeong, H., Cress, U., Moskaliuk, J., et al. (2017). Joint interactions in large online knowledge communities: The A3C framework. IJCSCL, 12(2), 133–151. https://doi.org/10.1007/s11412-017-9256-8.
John, G. H., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the eleventh conference on uncertainty in artificial intelligence (pp. 338–345). Morgan Kaufmann Publishers Inc.
Klomek, A. B., Sourander, A., & Gould, M. S. (2011). Bullying and suicide. Psychiatric Times, 28(2), 1–6.
Koh, J., & Kim, Y. G. (2003). Sense of virtual community: A conceptual framework and empirical validation. International Journal of Electronic Commerce, 8(2), 75–94. https://doi.org/10.1080/10864415.2003.11044295.
Leontyev, A. (2009). Activity and Consciousness. Marxists Internet Archive. Retrieved from https://www.marxists.org/archive/leontev/works/activity-consciousness.pdf.
Menesini, E., Nocentini, A., Palladino, B. E., Frisén, A., Berne, S., Ortega-Ruiz, R., et al. (2012). Cyberbullying definition among adolescents: A comparison across six European countries. Cyberpsychology, Behavior, and Social Networking, 15(9), 455–463. https://doi.org/10.1089/cyber.2012.0040.
McInnerney, J. M., & Roberts, T. S. (2004). Online learning: Social interaction and the creation of a sense of community. Educational Technology & Society, 7(3), 73–81.
McMillan, D. W., & Chavis, D. M. (1986). Sense of community: A definition and theory. Journal of Community Psychology, 14(1), 6–23. https://doi.org/10.1002/1520-6629(198601)14:1%3c6:AID-JCOP2290140103%3e3.0.CO;2-I.
Nahar, V., Li, X., & Pang, C. (2013). An effective approach for cyberbullying detection. Communications in Information Science and Management Engineering, 3(5), 238. https://doi.org/10.15680/IJIRCCE.2016.0404305.
Nandhini, B. S., & Sheeba, J. I. (2015). Online social network bullying detection using intelligence techniques. Procedia Computer Science, 45, 485–492. https://doi.org/10.1016/j.procs.2015.03.085.
Ng, J. C. (2017). Interactivity in virtual learning groups: Theories, strategies, and the state of literature. International Journal of Information and Education Technology, 7(1), 46–52.
Nikiforos, S., Tzanavaris, S., & Kermanidis, K. L. (2020). Virtual learning communities (VLCs) rethinking: Collaboration between learning communities. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10132-4.
Ntais, G. (2006). Development of a Stemmer for the Greek Language. (Master Thesis, Stockholm University, Royal Institute of Technology, Department of Computer and Systems Sciences, pp. 1–40).
Ortega, R., Elipe, P., Mora-Merchán, J. A., Genta, M. L., Brighi, A., Guarini, A., et al. (2012). The emotional impact of bullying and cyberbullying on victims: A European cross-national study. Aggressive Behavior, 38(5), 342–356. https://doi.org/10.1002/ab.21440.
Özel, S. A., Saraç, E., Akdemir, S., & Aksu, H. (2017, October). Detection of cyberbullying on social media messages in Turkish. In Computer science and engineering (UBMK), 2017 international conference on (pp. 366–370). IEEE. https://doi.org/10.1109/ubmk.2017.8093411.
Preece, J., & Maloney-Krichmar, D. (2003). Online communities. In J. Jacko & A. Sears (Eds.), Handbook of human–computer interaction (pp. 596–620). Mahwah: Lawrence Erlbaum.
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 on (Vol. 2, pp. 241–244). IEEE. https://doi.org/10.1109/ICMLA.2011.152.
Rheingold, Η. (1993). The virtual community: Homesteading on the electronic frontier. New York: MIT.
Rovai, A. P. (2002a). Building sense of community at a distance. The International Review of Research in Open and Distributed Learning, 3(1), 1–16.
Rovai, A. P. (2002b). Sense of community, perceived cognitive learning, and persistence in asynchronous learning networks. The Internet and Higher Education, 5(4), 319–332.
Rovai, A. P., & Jordan, H. (2004). Blended learning and sense of community: A comparative analysis with traditional and fully online graduate courses. The International Review of Research in Open and Distributed Learning, 5, 2.
Sanchez, H., & Kumar, S. (2011). Twitter bullying detection. Series NSDI, 12, 15–15.
Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of Learning Sciences, 3(3), 265–283. https://doi.org/10.1207/s15327809jls0303_3.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Sokolov, A. (1972). Inner speech and thought. New York: Plenum Press.
Suleiman, D., & Al-Naymat, G. (2017). SMS spam detection using H2O framework. Procedia Computer Science, 113, 154–161. https://doi.org/10.1016/j.procs.2017.08.335.
Song, B., & Li, X. (2019). The research of intelligent virtual learning community. International Journal of Machine Learning and Computing, 9, 621.
Stahl, G., & Hakkarainen, K. (2019). Theories of CSCL. In U. Cress, C. Rosé, A. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning. New York: Springer.
Tommasel, A., Rodriguez, J. M., & Godoy, D. (2018, August). Textual aggression detection through deep learning. In Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018) (pp. 177–187). Santa Fe, USA.
Thomas, D. R., Becker, W. C., & Armstrong, M. (1968). Production and elimination of disruptive classroom behavior by systematically varying teacher's behavior. Journal of Applied Behavior Analysis, 1(1), 35–45. https://doi.org/10.1901/jaba.1968.1-35.
Yin, D., Xue, Z., Hong, L., Davison, B. D., Kontostathis, A., & Edwards, L. (2009). Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB, 2, 1–7.
Vanhove, T., Leroux, P., Wauters, T., & De Turck, F. (2013, May). Towards the design of a platform for abuse detection in osns using multimedial data analysis. In Integrated Network Management (IM 2013), 2013 IFIP/IEEE International Symposium on (pp. 1195–1198). IEEE.
Vreeman, R. C., & Carroll, A. E. (2007). A systematic review of school-based interventions to prevent bullying. Archives of Pediatrics & Adolescent Medicine, 161(1), 78–88. https://doi.org/10.1001/archpedi.161.1.78.
Vygotsky, L. S. (2008). Thought & language, (A. Rodi, Transl.). Athens: Gnosi.
Xu, J. M., Jun, K. S., Zhu, X., & Bellmore, A. (2012, June). 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 (pp. 656–666). Association for Computational Linguistics.
Zhao, R., Zhou, A., & Mao, K. (2016, January). Automatic detection of cyberbullying on social networks based on bullying features. In Proceedings of the 17th international conference on distributed computing and networking (pp. 1–6). https://doi.org/10.1145/2833312.2849567.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nikiforos, S., Tzanavaris, S. & Kermanidis, KL. Virtual learning communities (VLCs) rethinking: influence on behavior modification—bullying detection through machine learning and natural language processing. J. Comput. Educ. 7, 531–551 (2020). https://doi.org/10.1007/s40692-020-00166-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40692-020-00166-5