The Dataset for Multi Label Hate Speech and Abusive Language Detection in Indonesian Twitter

Hate-speech and offensive language detection model using various Machine Learning and NLP techniques and labeled Twitter data

A repository

Capstone project to automate Twitter hate speech detection with classification modeling.

Twitter Analysis with Topic Modeling, Sentiment Analysis and Hate Speech Detection

dynamic graph neural network implementaion for hate-speech detection on twitter

Several experiments were performed to detect hate speech by first categorizing which words fall under the category of hate/offensive words and then use deep learning and other machine learning techniques to learn abstract feature representations from input data and see how well the model performs on Twitter dataset.

Live cyberbullying/hate speech detection on twitter with integrated portal.

Hate-Speech-Detection-in-Social-Media This project detect hate speech and classify twitter texts using NLP techniques-SpaCy, TF-IDF,Word2vec and Machine Learning techniques in Python.

A hate speech detection bot for Discord. Uses an artificial neural network trained on Twitter tweets to detect hate speech. Python.

Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter

Hate speech detection on the micro-blogging website, twitter. Application of novel modification made to traditional tfidf algorithm for word frequency and weight generation relative to its document frequency. Further, application of classifications models including random forest, naive bayes, decision tree, logistic regression and gradient boosting to classify tweets under the "hate-speech" and the "non-hate speech" tags.

A ML-based hate speech detection tool on Twitter (in French)

Here we are detecting whether a comment posted in Twitter, is a hate speech of not by training different models i.e., Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes.

Given the steadily growing body of social media content, hate speech towards women is increasing. Such kind of contents have the potential to cause harm and suffering on an individual basis, and they may lead to social tension and disorder beyond cyber space. To support the automatic detection of cyber hate online, specifically on Twitter, we build a supervised learning model which is developed to classify cyber hate towards woman on Twitter. Turkish tweets, with a hashtag specific to choice of clothing for women, have been collected and five machine learning based classification algorithms were applied including Support Vector Machines (using polynomial and RBF Kernel), J48, Naive Bayes, Random Forest and Random Tree. Preliminary results showed that hateful contents can be detected with high precision however more sophisticated approaches are necessary to improve recall. Keywords—Hate speech recognition, machine learning, classification, tf-idf

Automated recognition and detection of Hate Speech and Offensive language on different Online Social Networks, mainly Twitter, presents a challenge to the community of Artificial Intelligence and Machine Learning. Unfortunately, sometimes these ideas communicated via the internet are intended to promote or incite hatred or humiliation of an individual, community, or even organizations. The HASOC shared task is to attempt to automatically detect abusive language on Twitter in English and Indo-Aryan Languages like Hindi. To participate in this task and provide our input, we (team Data Pirates) presented several machine learning models for Hindi Subtasks. The datasets provided allowed the development and testing of supervised machine learning techniques. The top 2 performing models for sub-task A were Naïve Bayes and Logistic Regression with the same Macro F1 score of 0.7394. The top 2 performing models for sub-task B were Logistic Regression and CatBoost, with Macro F1 scores of 0.4828 and 0.4709, respectively. This overview intends to provide detailed understandings and to analyze the outcomes.

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Hate-speech and offensive language detection model using various Machine Learning and NLP techniques and labeled Twitter data

Detection of hate speech by leveraging the term frequency-inverse document frequency (TFIDF) values to multiple machine learning models.

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Harassment and hate speech detection app built for twitter feed

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Classification of hate-speech detection of twitter data using machine learning algorithm

Transformers models for Hate Speech and Offensive Language Detection on Arabic Twitter

This project implements a hate speech detection model using a decision tree classifier and Twitter data.

What we hope to create on one portion of our proposal is an online tool using Microsoft Flow and Python. The packages I will use for building this api is tweepy. Tweepy is described as an easy-to-use python library for accessing twitter api (https://www.tweepy.org/ ). This tool will pull 100 tweets from a twitter user, place it in a csv file, and then filter and score the tweets from threatening, mild, and violent hate. After doing so, using a sentiment analysis tool, the tool will detect what score the tweet would receive in the case of the criteria made by Eve. After scoring the user, it will then do the same for their followers (limited to the first 10 so that the system does not crash). The tool will then pop up with a name of all the files that scored higher risk and place the information in a csv file. In order to create my scoring method, I will create three text files. These files will be separated by subject: non-threatening, negative, and violent text words. The csv tweet files will then be read in and filtered through these categories. They will be placed in separate arrays and then be written to a new file with only the names of the twitter user. I will be placing the text files in my github for public use and may develop a stronger tool in place of this method such as a deep learning tool so that the words may continue to become up-to-date and not rely on a static file. I will then finish with two files that have the user information placed and their score. I will work on adding context/geographical component to my sentiment analysis in order to make the data more reliable and further the study of speech detection research.

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A Project that identifies hate speech from a data

Twitter is the biggest platform where anybody and everybody can have their views heard. Some of these voices spread hate and negativity. Twitter is wary of its platform being used as a medium to spread hate. You are a data scientist at Twitter, and you will help Twitter in identifying the tweets with hate speech and removing them from the platform. You will use NLP techniques, perform specific cleanup for tweets data, and make a robust model.