Papers with hate speech classifiers
Hate Speech Classifiers Learn Normative Social Stereotypes (2023.tacl-1)
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| Challenge: | Social stereotypes negatively impact individuals’ judgments about different groups and may have a critical role in understanding language directed toward marginalized groups. |
| Approach: | They first investigate the impact of novice annotators’ stereotypes on their hate-speech-annotation behavior. Then, they examine the effect of normative stereotypes in language on the aggregated annotated judgments. |
| Outcome: | The framework provides insights into sources of bias in hate-speech moderation, informing ongoing debates regarding machine learning fairness. |
Lifelong Learning of Hate Speech Classification on Social Media (2021.naacl-main)
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| Challenge: | Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. |
| Approach: | They propose to use Variational Representation Learning and a load-balancing self-organizing inductive neural network to learn hate speech classification on social media. |
| Outcome: | The proposed model improves on the lifelong learning techniques on social media. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
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| Challenge: | Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class. |
| Approach: | They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets. |
| Outcome: | The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task. |