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Dataset . 2021
License: CC BY
Data sources: Datacite
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License: CC BY
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ZENODO
Dataset . 2021
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CAD: the Contextual Abuse Dataset

Authors: Vidgen, Bertie; Nguyen, Dong; Margetts, Helen; Rossini, Patricia; Tromble, Rebekah;

CAD: the Contextual Abuse Dataset

Abstract

Introducing CAD: the Contextual Abuse Dataset Bertie Vidgen, Dong Nguyen, Helen Margetts, Patricia Rossini, Rebekah Tromble, NAACL 2021 Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic. Progress in automatically detecting and classifying abusive content is often held back by the lack of high quality and detailed datasets. We introduce a new dataset of primarily English Reddit entries which addresses several limitations of prior work. It (1) contains six conceptually distinct primary categories as well as secondary categories, (2) has labels annotated in the context of the conversation thread, (3) contains rationales and (4) uses an expert-driven group-adjudication process for high quality annotations. This repository contains the annotated dataset, annotation guidelines and the trained models and their output. Code: https://github.com/dongpng/cad_naacl2021 Paper: https://www.aclweb.org/anthology/2021.naacl-main.182/

Keywords

online abuse, labelled dataset, machine learning, social media, online hate

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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