Rating-Based Cyberbullying Detection with Text, Emojis on Social Media

Title

Rating-Based Cyberbullying Detection with Text, Emojis on Social Media

Creator

Arjun Krishna K.; Jadhav D.; Vijayalakshmi S.; Palathara T.S.

Description

In the dynamic landscape of online interactions, cyberbullying has become pervasive, profoundly impacting user's digital well-being. Public figures, especially celebrities and influencers, face heightened vulnerability to online harassment, exacerbated by the post-pandemic surge in social media usage. To address this challenge, our research adopts a holistic approach to detect cyberbullying in text, considering both textual content and the nuanced expressions conveyed through emojis on social media platforms. We employed a diverse set of machine learning and deep learning models, including Support Vector Classifier, Logistic Regression, Random Forest, XGBoost, LSTM, Bi-LSTM, GRU, and Bi-GRU, to accurately classify non cyberbullying or cyberbullying text. Beyond classification, our study introduces an offensive rating system, assigning severity ratings on a 1-5 scale to identify cyberbullying instances. A critical aspect is the establishment of a threshold value which depends on user security and safety ethics of different social media platforms; texts surpassing this trigger an automatic recommendation to block the user, ensuring a proactive response to minimize harm. This recent contribution not only comprehensively addresses cyberbullying but also empowers society. 2024 IEEE.

Source

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Cyberbullying Detection; Offensive Rating; Swear Words Dataset; TF-IDF with Random Forest

Coverage

Arjun Krishna K., Christ University, Department of Data Science, India; Jadhav D., Christ University, Department of Data Science, India; Vijayalakshmi S., Christ University, Department of Data Science, India; Palathara T.S., Christ University, Department of Data Science, India

Rights

Restricted Access

Relation

ISBN: 979-835037809-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICEECT61758.2024.10739238

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211132470&doi=10.1109%2fICEECT61758.2024.10739238&partnerID=40&md5=3e603a4d911a86d94e7d5c3acd1d302f