Kavli Affiliate: Xian Chen
| First 5 Authors: Julian Bäumler, Louis Blöcher, Lars-Joel Frey, Xian Chen, Markus Bayer
| Summary:
The dissemination of online hate speech can have serious negative
consequences for individuals, online communities, and entire societies. This
and the large volume of hateful online content prompted both practitioners’,
i.e., in content moderation or law enforcement, and researchers’ interest in
machine learning models to automatically classify instances of hate speech.
Whereas most scientific works address hate speech classification as a binary
task, practice often requires a differentiation into sub-types, e.g., according
to target, severity, or legality, which may overlap for individual content.
Hence, researchers created datasets and machine learning models that approach
hate speech classification in textual data as a multi-label problem. This work
presents the first systematic and comprehensive survey of scientific literature
on this emerging research landscape in English (N=46). We contribute with a
concise overview of 28 datasets suited for training multi-label classification
models that reveals significant heterogeneity regarding label-set, size,
meta-concept, annotation process, and inter-annotator agreement. Our analysis
of 24 publications proposing suitable classification models further establishes
inconsistency in evaluation and a preference for architectures based on
Bidirectional Encoder Representation from Transformers (BERT) and Recurrent
Neural Networks (RNNs). We identify imbalanced training data, reliance on
crowdsourcing platforms, small and sparse datasets, and missing methodological
alignment as critical open issues and formulate ten recommendations for
research.
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