ReviewAI and ethics2022
Bias and comparison framework for abusive language datasets.
Review in AI and ethics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Preventing profiling for ethical fake news detection.Information processing & management · 2023Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recently, numerous datasets have been produced as research activities in the field of automatic detection of abusive language or hate speech have increased. A problem with this diversity is that they often differ, among other things, in context, platform, sampling process, collection strategy, and labeling schema. There have been surveys on these datasets, but they compare the datasets only superficially. Therefore, we developed a bias and comparison framework for abusive language datasets for their in-depth analysis and to provide a comparison of five English and six Arabic datasets. We make this framework available to researchers and data scientists who work with such datasets to be aware of the properties of the datasets and consider them in their work.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.