The problem with this is that people will frequently write things they shouldn’t, and to maintain a positive community this toxic content and the users posting it need to be removed quickly. But they don’t have the resources to hire full-time moderators to review every comment. This problem led the Conversation AI team1, owned by Alphabet, to develop a large open dataset of labeled Wikipedia Talk Page comments, which will be the dataset used for the project.

Identify and classify toxic online comments

April 4, 2025 - Classify comments based on whether they are toxic or not. ... This dataset contains sample social media comments labeled as toxic or non-toxic.

any piece of text and detecting different types of toxicity like obscenity, threats, insults and identity-based hatred. The labelled Wikipedia Com- ment Dataset prepared by Jigsaw is used for the purpose.

The toxicity and toxicity subtype labels are binary values (0 or 1) indicating whether the majority of annotators assigned that attribute to the comment text. This config is a replica of the data released for the Jigsaw Toxic Comment Classification Challenge on Kaggle, with the test dataset joined with the test_labels released after the competition, and test data not used for scoring dropped.

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

This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.

February 17, 2023 - It is also important to consider the larger social implications of toxic comments. They can perpetuate harmful stereotypes, discrimination, and prejudice, and contribute to a divisive and toxic online culture. This can have a ripple effect on offline communities, leading to further social and political polarization. The dataset comes from Kaggle Toxic Comments Classification, a very popular dataset for NLP projects.

The competition could be found here: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge · As a group of students with great interests in Natural Language Processing, as well as making online discussion more productive and respectful, we determined to work on this project and aim to build a model that is capable of detecting different types of toxicity like threats, obsenity, insults, and identity-based hate. The dataset we are using consists of comments from Wikipedia’s talk page edits.

In this project, we aimed to classify toxic comments using Machine Learning models, which can be applied in censoring inappropriate comments on social media platforms. The dataset used for training and testing is obtained from Jigsaw Unintended Bias in Toxicity Classification competition on Kaggle: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data

The dataset used in this project is the Toxic Comment Classification Challenge from Kaggle. The dataset contains approximately 159,000 comments from Wikipedia talk pages that have been labeled by human annotators as toxic or non-toxic.

March 5, 2025 - Toxic Comment Classifier is a competition that has been organized by Jigsaw/Conversation AI and hosted on Kaggle. The data set for building the classification model was acquired from the competition site and it included the training set as well as the test set.

September 22, 2023 - Platforms struggle to identify and flag potentially harmful or offensive online comments, leading many communities to restrict or shut down user comments altogether. Kaggle issued a challenge to build a multi-label classification model that’s able to detect different types of toxicity like threats, obscenity and insults, and thus help make online discussion more productive and respectful. The data for the problem is a dataset of 159,571 comments from Wikipedia’s talk page edits.

November 23, 2025 - Multilingual Toxic Comment Dataset for NLP & Social Media Moderation

Kaggle [30], is a multi-label dataset and contains labels such · as toxic, severe_toxic, obscene, threat, insult, and · identity_hate. The non-toxic comments belong to one class, while from the other comments only those comments are · selected that have toxic labels. It means that the comments · that label severe_toxic, obscene, threat, insult, and ... TABLE 1. Example ...

November 11, 2021 - Data from Toxic Comment Classification Challenge · Data CardCode (308)Discussion (0)Suggestions (0) info · 7.06 · CC0: Public Domain · Not specified · NLP · get_app · fullscreen · chevron_right · DetailCompactColumn · 7 of 7 columnskeyboard_arrow_down ·

December 16, 2019 - This could easily be ported to your website’s comment section and you can change it to have the colour of the comment being entered change as per the toxicity of the text. We use kaggle toxic Comment classification challenge dataset to build ...

Comment Text – User comment. Toxicity Label – Tagged as "Toxic" or "Non-Toxic". The data was divided into 80% training and 20% validation sets with stratified sampling to maintain class balance. The dataset is mixture of Kaggle’s jigsaw dataset and a custom dataset created through surveys and research by our team on real

Table 1: Datasets currently included in the toxic comment collection (sorted by year of publication). For · this tabular presentation, we combined labels, e.g., target represents several different labels of targets. ... Source Lang. ... Nuha Albadi, Maram Kurdi, and Shivakant Mishra. 2018. Are they our brothers? analysis and detection...

October 15, 2024 - We will use the toxic comments dataset, which comprises a large number of Wikipedia comments labelled as toxic by human raters. There are six types of toxicities in this data: toxic, severe-toxic, obscene, threat, insult and identity-hate.