Melody Evans
Dr. Andy Fentem
English 1101
8 October 2021
Dealing with social media Bullying
Agrawal, S. and Awekar, A. (2018). Deep learning for detecting cyberbullying across multiple social media platforms. In European Conference on Information Retrieval, Grenoble, France. Cham, Switzerland: Springer, pp. 141–153.10.1007/978-3-319-76941-7_11
Based on this 2018 scholarly article, “Deep learning for detecting cyberbullying across multiple social media platforms” Agrawal, S. and Awekar argue that harassment by cyberbullies is a significant phenomenon on the social media. The author’s support their argument by performing extensive experiments using three real-world datasets: Form spring (∼∼12k posts), Twitter (∼∼16k posts), and Wikipedia (∼∼100k posts). This article contributes to the topic because it systematically analyzes cyberbullying detection on various topics across multiple SMPs using deep learning-based models and transfer learning.
Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A. and De Bourdeaudhuij, (2014). Cyberbullying on social network sites. An experimental study into bystanders’ behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior 31, 259–271.10.1016/j.chb.2013.10.036
Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A., and De Bourdeaudhuij 2014 scholarly article, “Cyberbullying on social network sites and an experimental study into bystanders’ behavioral intentions to help the victim or reinforce the bully” implies that cyberbullying on social network sites poses a significant threat to the mental and physical health of victimized adolescents. The author’s support their claim by setting up an experimental scenario study in order to examine the influence of contextual factors (severity of the incident, identity and behavior of other bystanders) on bystanders’ behavioral intentions to help the victim or reinforce the bully in cases of harassment on Facebook. This article contributes to the topic because an interaction effect was found between other bystanders’ identity and behavior on behavioral intentions to join in the bullying where both helped and reinforcing behavioral intentions differed according to gender.
Cowie, H. (2013). Cyberbullying and its impact on young people’s emotional health and well-being. The Psychiatrist 37(5), 167–170.
Based on this 2013 scholarly article, “Cyberbullying and its impact on young people’s emotional health and well-being” Cowie, H., argues that the upsurge of cyberbullying is a frequent cause of emotional disturbance in children and young people. The author supports her claim through research that indicates the importance of tackling bullying early before it escalates into something much more serious. This article contributes to the topic because it examines the effectiveness of common responses to cyberbullying.
Kumar, R., Ojha, A.K., Malmasi, S. and Zampieri, M. (2018). Benchmarking aggression identification in social media. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018). Santa Fe, New Mexico, USA: Association for Computational Linguistics, pp. 1–11.
Kumar, R., Ojha, A.K., Malmasi, S., and Zampieri 2018 scholarly article, “Benchmarking aggression identification in social media” implies that it is important that preventative measures be taken to cope with abusive behavior aggression online. The author’s support their claim through a task to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. This article contributes to the topic because the positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest.
Salmivalli, C. and Pöyhönen, V. (2012). Cyberbullying in Finland. In Cyberbullying in the Global Playground: Research from International Perspectives, pp. 5772.10.1002/9781119954484.ch4
Based on this 2012 scholarly article, “Cyberbullying in Finland” Salmivalli, C., and Poyhonen, V., argue that empirical studies on the phenomenon are scarce, and are almost exclusively based on web-based surveys with possibly highly selected samples. The author’s supported their claim by presenting results from a large-scale study conducted in the context of the Daphne project, “an investigation into forms of peer-peer bullying at school in preadolescents and adolescent groups: New instruments and preventing strategies (2006-2009)”. This article contributes to the topic because the study concerned the prevalence, sex, and age differences in cyberbullying and cybervictimization, the most typical devices utilized (the internet versus mobile phones), and the associations between traditional and cyberbullying/victimization.
Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., Hu, H., Luo, F., Macbeth, J. and Dillon, E. (2016). Cyberbullying detection with a pronunciation based convolutional neural network. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA: IEEE, pp. 740–745.
Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., Hu., Luo, F., 2016 scholarly article, “Cyberbullying detection with a pronunciation based convolutional neutral network” implies that accurately detecting cyberbullying helps prevent a deep and long-lasting effect on victims but the noise and errors in social media posts and messages make it very challenging. The author’s support their claim because they proposed a novel pronunciation based convolutional neural network (PCNN) to address this challenge. This article contributes to the topic because they evaluate the performance of their models using two cyberbullying datasets collected from Twitter and Formspring.me.
