Directions in abusive language training data, a systematic review: Garbage in, garbage out

Bertie Vidgen, Leon Derczynski

Publikation: Artikel i tidsskrift og konference artikel i tidsskriftTidsskriftartikelForskningpeer review

Abstrakt

Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.
OriginalsprogEngelsk
Artikelnummere0243300
TidsskriftPLOS ONE
Vol/bind15
Udgave nummer12
ISSN1932-6203
DOI
StatusUdgivet - 28 dec. 2020

Fingeraftryk

Dyk ned i forskningsemnerne om 'Directions in abusive language training data, a systematic review: Garbage in, garbage out'. Sammen danner de et unikt fingeraftryk.

Citationsformater