Directions in abusive language training data

Directions in abusive language training data, a systematic review

This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers.

Directions in Abusive Language Training Data: Garbage In ... - arXiv

This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data.

Directions in abusive language training data, a systematic review

Creating a training dataset for online abuse detection is typically motivated by the desire to address a social problem. These motivations can ...

Directions in abusive language training data, a systematic review

Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained ...

Directions in abusive language training data, a systematic review

This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data.

Directions in abusive language training data, a systematic review

This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers and reports on ...

Directions in abusive language training data, a systematic review

This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also ...

Directions in Abusive Language Training Data - ResearchGate

Request PDF | Directions in Abusive Language Training Data: Garbage In, Garbage Out | Data-driven analysis and detection of abusive online content covers ...

Directions in Abusive Language Training Data - Semantic Scholar

134 References ; Understanding Abuse: A Typology of Abusive Language Detection Subtasks · Zeerak TalatThomas DavidsonDana WarmsleyIngmar Weber.

dblp: Directions in Abusive Language Training Data

Bertie Vidgen, Leon Derczynski: Directions in Abusive Language Training Data: Garbage In, Garbage Out. CoRR abs/2004.01670 (2020).

Hate Speech Dataset Catalogue | hatespeechdata

“DALC: the Dutch Abusive Language Corpus”. Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), ACL. English. Not All Counterhate Tweets ...

Bias and comparison framework for abusive language datasets

They showed that a classifier trained on a standard abusive language dataset discriminates documents in AAE. [1] identified annotator bias by ...

Abusive language detection in youtube comments leveraging ...

(2018) used the abusive term in the context of sexism and racism. Waseem et al. (2017) categorized the term abuse generally dependent on ...

Abusive Language | Papers With Code

However, there is a lack of data sets that take into account the degree of explicitness. Paper · Add Code · Directions in Abusive Language Training Data: ...

Hate Speech Dataset Catalogue

our paper: Directions in Abusive Language Training Data: Garbage In, Garbage Out. And if you would like to find other resources for researching online hate ...

Detecting Abusive Language on Online Platforms: A Critical Analysis

[Waseem et al., 2017]Zeerak Waseem et al. Understanding Abuse: A Typology of Abusive Language Detection ...

Dataset annotation in abusive language detection - SSOAR

Abusive message collection using keywords. The sparsity of online abuse data has led researchers to develop specific sampling techniques to increase their ...

Towards generalisable hate speech detection - arXiv

(2019)'s study of model generalisability, abusive language-specific pre- trained ... Directions in Abusive Language Training Data: Garbage In,.

Deep learning-based approaches for abusive content detection and ...

The word abuse is a combination of two words: ab (Latin for "away") and use (to employ), which together mean misuse or incorrect use. As a result, abusive ...

Catalogue of abusive language training data - FoLD

Subject keywords: NLP, abusive language, online language ; Data types: Written ; Funders: N/A ; Associated AIFL centres: Forensic Linguistic ...