The socially relevant topic that I chose is “Dealing with Social Media Bullying”. Another term used for social media bullying would be cyberbullying. Cyberbullying is when someone repeatedly makes fun of another person online or repeatedly picks on another person through e-mail or text message or when someone posts something online about another person that they don’t like. While all bullying is characterized by intentional, often repetitive, hurtful behavior towards another person or group, there are distinguishing elements when it happens online or via smartphone. One million children were harassed, threatened, or subjected to other forms of cyberbullying on Facebook during the past year.

The first source I found is Agrawal, S. and Awekar, A. (2018). Deep learning for detecting cyberbullying across multiple 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 social media. The authors support their argument by performing extensive experiments using three real-world datasets: Formspring (∼“>∼∼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 transfers learning.

The second source I found is 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’ behavioral 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 authors 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.

The third source I found is 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.

The fourth source I found is 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 authors 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.