is the word count vector for comment i with label yi ∈ · {−1, 1} indicating whether it’s toxic or not.

message or any comment appearing in social platform that can be toxic or non- toxic) and detects the type of toxicity it contains.

April 30, 2024 - In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.

This repository contains code to instantiate and deploy a toxic comment classifier. This model is able to detect 6 types of toxicity in a text fragment.

Identify and classify toxic online comments · Jigsaw/Conversation AI

December 21, 2025 - These limitations hinder their performance in real-world scenarios, where toxicity often emerges across multi-turn interactions. This paper proposes a context-aware interactive framework using GPT models to simulate human-like interactions, transforming single comments into multi-level threads. This approach improves toxicity detection by leveraging contextual dialogue.

The Toxic Comment Detector leverages Hugging Face’s unitary/toxic-bert model to identify harmful, offensive, or abusive language in user-generated content.

comprises the toxicity of this comment. Typical for this class, there is no · need to take into account the full comment if at least one profane word has · been found. For this reason, simple blacklists of profane words can be used · for detection.

January 25, 2020 - comprises the toxicity of this comment. Typical for this class, there is no · need to take into account the full comment if at least one profane word has · been found. For this reason, simple blacklists of profane words can be used · for detection.

To this end, we describe the concept of toxicity and characterize its subclasses. Further, we present various deep learning approaches, including datasets and architectures, tailored to sentiment analysis in online discussions. One way to make these approaches more comprehensible and trustworthy is fine-grained instead of binary comment classification.

We propose a machine learning approach for detecting the toxicity of a comment by analyzing both the text and the emojis within the comment. Our approach utilizes word embeddings derived from GloVe and emoji2vec to train ...

October 27, 2021 - In this context, AI could be beneficial. While humans would spend hours going over thousands of comments, machine learning algorithms could detect potentially toxic comments in just a few seconds.

December 27, 2024 - The prevalence of toxic comments on social networking sites poses a significant threat to the freedom of speech and the psychological well-being of online users. To address this challenge, researchers have turned to machine learning algorithms as ...

July 30, 2020 - A basic and simple yet powerful Python library to detect toxicity/profanity of a review or list of reveiws.

December 16, 2019 - The network takes tokenized text in the form of word ids and predicts the probabilities for two classes: “Toxic” and “Non-toxic” · x_train is a list of comments, we will first clean it, the filters parameter in Tokenizer will do this for us.

In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits.

Toxicity detection in comments is one of such methodologies to find out the different types of conversations that can be classified as toxic in nature.

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, ...

ToxiDetect is an AI-powered model designed to identify and classify toxic comments using advanced deep learning techniques. Leveraging TensorFlow and Bidirectional LSTMs, this model detects various types of toxicity in comments, including insults, ...

October 13, 2025 - The study involves extensive text preprocessing, advanced word embedding techniques (like Word2Vec and FastText), and training the DL models on large-scale datasets such as the Jigsaw Toxic Comment Classification Challenge. Experimental results demonstrate that the proposed BiLSTM- and BERT-based models achieve superior performance in classifying multiple types of toxicity (e.g., toxic, severe-toxic, threat, insult) compared to conventional machine learning approaches.