Detecting cyberbullying in tweets using Machine Learning

Source code and models for the paper "Cyberbullying Detection with Fairness Constraints". IEEE Internet Computing, 2020

Cyber Bullying Detection Application (CBDA)

Repository hosting dataset for the Shared Task on Aggression Identification during First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) as COLING - 2018. Please visit the workshop website - https://sites.google.com/view/trac1/home - for more details

InsightGuard is AI-powered, free to use and open source, cybersecurity API with a lot of functionalities. Python + Next.js

Discord Cyberbullying Bot

This app predicts the nature of the tweet into 6 Categories. i.e. Age, Ethnicity, Gender, Religion, Other Cyberbullying, Not Cyberbullying.

Public Website for Kindly

Repository for the experiments described in "Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity" submitted as pre-print to arXiv.

Identifying trolling, aggression, cyber-bullying and hate speech etc. Three classes: Overtly Aggressive (OAG), Covertly Aggressive (CAG), and Non-aggressive (NAG)

Cyber bullying detection in Twitter using machine learning

Cyberbullying Detection App tailored for the Arabic language. The app is designed to identify instances of cyberbullying in Arabic text using various machine learning and deep learning algorithms.

This application can detect the hate and toxic tweets of users amongst an entire corpora of tweets and thus can be an effective methodology towards reducing cyber bullying.

A Model Training for Cyberbullying Detection by Using Youtube Comments

Cyberbullying detection using BLSTM and GloVe

Code and data for the paper "Politeness Stereotypes and Attack Vectors: Gender Stereotypes in Japanese and Korean Language Models" (Arxiv 2023)

Natural Language Processor ecosystem for proactive and reactive cyberbullying response | Created during Hack4Lem 2021 hackathon

Research paper about Cyber Bulluying Detection on Twitter from Stanford University students