The FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema in its details is described in https://arxiv.org/pdf/1906.02045.pdf. Usernames in the metadata are pseudo-anonymised and removed from the comments. The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testi...
Social media was a heavily used platform by people in different countries to express their opinions ...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
Recent directions for offensive language detection are hierarchical modeling, identifying the type a...
The LiLaH-HAG dataset (HAG is short for hate-age-gender) consists of metadata on Facebook comments t...
In light of unprecedented increases in the popularity of the internet and social media, comment mode...
This project proposes a NooJ algorithm with the task to find and categorize various slurs, insults a...
The dataset comprises comments collected from the official Facebook page of the NIPHK Institute for ...
In recent debates on offensive language in participatory online spaces, the term ‘hate speech’ has b...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
The HateBR dataset was collected from the comment section of Brazilian politicians’ accounts on Inst...
Abuse and hate are penetrating social media and many comment sections of news media companies. These...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
The dataset comprises comments collected from the official Facebook page of the National Institute o...
This article describes initial work into the automatic classification of user-generated content in n...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
Social media was a heavily used platform by people in different countries to express their opinions ...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
Recent directions for offensive language detection are hierarchical modeling, identifying the type a...
The LiLaH-HAG dataset (HAG is short for hate-age-gender) consists of metadata on Facebook comments t...
In light of unprecedented increases in the popularity of the internet and social media, comment mode...
This project proposes a NooJ algorithm with the task to find and categorize various slurs, insults a...
The dataset comprises comments collected from the official Facebook page of the NIPHK Institute for ...
In recent debates on offensive language in participatory online spaces, the term ‘hate speech’ has b...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
The HateBR dataset was collected from the comment section of Brazilian politicians’ accounts on Inst...
Abuse and hate are penetrating social media and many comment sections of news media companies. These...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
The dataset comprises comments collected from the official Facebook page of the National Institute o...
This article describes initial work into the automatic classification of user-generated content in n...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
Social media was a heavily used platform by people in different countries to express their opinions ...
A comprehensive corpus of user comments on online news articles on the topic of language from major ...
Recent directions for offensive language detection are hierarchical modeling, identifying the type a...