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Cyberbullying detection on Tweets
Author(s)
Date Issued
May 19, 2021
Type
Conference Paper
Abstract
Nowadays the young generation spends a median of 3 hours a day on social media sites such as Twitter, Facebook, Instagram, Snapchat or TikTok. As a consequence, these platforms have gradually become part of their daily life as well as an influence on their attitude and behaviour. Although the social media platforms were originally designed to share information/news or connect families and friends, they also spread fake news, conspiracy theories, hate speech and cyberbullying. Cyberbullying is bullying with the use of digital technologies which is repeated, aimed at scaring, angering or shaming those who are targeted. Feeling vulnerable, powerless, humiliated, isolated, depressed or suicidal are examples of the negative effects from cyberbullying. Unfortunately, study has shown that one-third of the students have experienced cyberbullying in their lifetime. To mitigate this problem, we propose 1) a novel dataset of 67K tweets collected from Twitter, 2) automatic annotation methods for a large-scale dataset and 3) a detection system that to identify these toxic behaviours. The experimental results demonstrate that our method outperforms the baseline by 4%.
Citation
Ecti Con 2021 2021 18th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Smart Electrical System and Technology Proceedings, 295-298, 2021