Hate speech detection and racial bias mitigation in social media based on BERT model

PloS One
Marzieh MozafariNoël Crespi

Abstract

Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the traine...Continue Reading

References

Aug 21, 2019·PloS One·Sean MacAvaneyOphir Frieder

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Citations

Jan 28, 2021·BMC Medical Informatics and Decision Making·Mohammed Ali Al-GaradiAbeed Sarker
Jul 10, 2021·PeerJ. Computer Science·Wenjie Yin, Arkaitz Zubiaga

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

GloVe
Google Colaboratory tool
Word2Vec
WordPiece
CrowdFlower
bert
pytorch
BERT LARGE
- bert library
ELMo

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