Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media

• URL: http://arxiv.org/abs/2107.08902v1
• Date: Fri, 23 Apr 2021 04:20:19 GMT
• Title: Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media
• Authors: Bhumika Bhatia, Anuj Verma, Anjum, Rahul Katarya
• Abstract summary: In our work, we explore binary classification by using a combination of datasets from various social media platforms. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus.
• Score: 4.932130498861987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, cyberbullying amongst children and other vulnerable groups are only growing over online classes, and increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure minors' safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing.

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