ANALYSIS OF CYBER WARFARE ON SOCIAL MEDIA
DOI:
https://doi.org/10.59367/pp4jxx83Keywords:
Aggression Detection, Aggression Behavior Analysis, Online Social PlatformAbstract
Antisocial behavior becomes more common because many online platforms encourage people to interact with one another. Lately, there has been a significant increase in aggressive behavior on social media, leading to various negative effects, including mental health issues and controversies. To address this, I conducted the very first analysis of aggressive user behavior on Twitter, a microblogging platform that doesn't have strict rules against aggressive behavior.
This analysis process involves three main steps: first, I collect data from Twitter; then, I identify instances of aggression in user interactions; finally, I create profiles of users based on their online behavior. In this study, I took a close look at how users exhibit aggressive behavior by examining their aggressive posts and the events they engage in. Interestingly, our findings show that users tend to be more engaged with aggressive content on the platform. This research sheds light on the relationship between user behavior and the prevalence of aggressive posts on Twitter.
References
Jacob Amedie. The impact of social media on society. 2015.
McKenzie Himelein-Wachowiak, Salvatore Giorgi, Amanda Devoto, Muhammad Rahman, Lyle Ungar, H. A. Schwartz, David H. Epstein, Lorenzo Leggio, and Brenda L Curtis. Bots and misinformation spread on social media: Implications for covid-19. Journal of Medical Internet Research, 23, 2021.
Maibam Debina and Navanath Saharia. Delab@iiitsm at icon-2021 shared task: Identification of aggression and biasness using decision tree. In ICON, 2021.
Sreekanth Madisetty and Maunendra Sankar Desarkar. Aggression detection in social media using deep neural networks. In Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018), pages 120–127, 2018.
Herodotos Herodotou, Despoina Chatzakou, and Nicolas Kourtellis. A streaming machine learning framework for online aggression detection on twitter. 2020 IEEE International Conference on Big Data (Big Data), pages 5056–5067, 2020.
Arup Baruah, K Amar Das, Ferdous Ahmed Barbhuiya, and Kuntal Dey. Aggression identification in english, hindi and bangla text using bert, roberta and svm. In Workshop on Trolling, Aggression and Cyberbullying, 2020. IEEE Transl. J. Magn. Japan, vol. 2, pp.
Md Saifullah Razali, Alfian Abdul Halin, Lei Ye, Shyamala Doraisamy, and Noris Mohd Norowi. Sarcasm detection using deep learning with contextual features. IEEE Access, 9:68609–68618, 2021.
Eytan Bakshy, Jake M Hofman, Winter A Mason, and Duncan J Watts. Everyone’s an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 65–74, 2011.
Lowlesh Nandkishor Yadav, “Predictive Acknowledgement using TRE System to reduce cost and Bandwidth” IJRECE VOL. 7 ISSUE 1 (JANUARY- MARCH 2019) pg no 275-278.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Futuristic Innovation in Arts, Humanities and Management (IJFIAHM)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
