Abusive Language

Dataset of hate speech annotated on Internet forum posts in English at sentence-level. The source forum in Stormfront, a large online community of white nacionalists. A total of 10,568 sentence have been been extracted from Stormfront and classified as conveying hate speech or not. Source: https://arxiv.org/pdf/1809.04444.pdf

HSOL is a dataset for hate speech detection. The authors begun with a hate speech lexicon containing words and phrases identified by internet users as hate speech, compiled by Hatebase.org. Using the Twitter API they searched for tweets containing terms from the lexicon, resulting in a sample of tweets from 33,458 Twitter users. They extracted the time-line for each user, resulting in a set of 85.4 million tweets. From this corpus they took a random sample of 25k tweets containing terms from the lexicon and had them manually coded by CrowdFlower (CF) workers. Workers were asked to label each tweet as one of three categories: hate speech, offensive but not hate speech, or neither offensive nor hate speech. Source: Automated Hate Speech Detection and the Problem of Offensive Language

Torque is an English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships. Source: TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

TalkDown is a labelled dataset for condescension detection in context. The dataset is derived from Reddit, a set of online communities that is diverse in content and tone. The dataset is built from COMMENT and REPLY pairs in which the REPLY targets a specific quoted span (QUOTED) in the COMMENT as being condescending. The dataset contains 3,255 positive (condescend) samples and 3,255 negative ones. Source: https://arxiv.org/pdf/1909.11272.pdf

A corpus of Offensive Language and Hate Speech Detection for Danish This DKhate dataset contains 3600 comments from the web annotated for offensive language, following the Zampieri et al. / OLID scheme. Submissions and benchmarks for the OffensEval 2020 Danish track are also included.

A dataset of games played in the card game "Cards Against Humanity" (CAH), by human players, derived from the online CAH labs. Each round includes the cards presented to users - a "black" prompt with a blank or question and 10 "white" punchlines as possible responses, and which punchline was picked by a player each round, along with text and metadata. An example prompt is “TSA guidelines now prohibit ___ on airplanes”. Candidate punchlines are “Goblins”, “BATMAN!!!”, “Poor people”, and “The right amount of cocaine”. Importantly, many cards are offensive or politically incorrect. Used to explore human humor preferences. Available upon request from CAH labs: mail@cardsagainsthumanity.com Train/test splits and data processing available in the paper/code: "Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game": https://github.com/ddofer/CAH

CoRAL is a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context.

The PointDenoisingBenchmark dataset features 28 different shapes, split into 18 training shapes and 10 test shapes. * PointDenoisingBenchmark for outliers removal: contains noisy point clouds with different levels of gaussian noise and the corresponding clean ground truths. * PointDenoisingBenchmark for denoising: contains noisy point clouds with different levels of noise and density of outliers and the corresponding clean ground truths. Source: PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

The ComMA Dataset v0.2 is a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the "type" of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here (and made available as part of the ComMA@ICON shared task), consists of a total 15,000 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian English - collected from various social media platforms such as YouTube, Facebook, Twitter and Telegram. As is usual on social media websites, a large number of these comments are multilingual, mostly code-mixed with English.