An Effective, Robust and Fairness-aware Hate Speech Detection Framework
- URL: http://arxiv.org/abs/2409.17191v1
- Date: Wed, 25 Sep 2024 07:01:51 GMT
- Title: An Effective, Robust and Fairness-aware Hate Speech Detection Framework
- Authors: Guanyi Mou, Kyumin Lee
- Abstract summary: Existing hate speech detection methods have limitations in several aspects.
We design a data-augmented, fairness addressed, and uncertainty estimated novel framework.
Our model outperforms eight state-of-the-art methods under both no attack scenario and various attack scenarios.
- Score: 2.9927562390637394
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the widespread online social networks, hate speeches are spreading
faster and causing more damage than ever before. Existing hate speech detection
methods have limitations in several aspects, such as handling data
insufficiency, estimating model uncertainty, improving robustness against
malicious attacks, and handling unintended bias (i.e., fairness). There is an
urgent need for accurate, robust, and fair hate speech classification in online
social networks. To bridge the gap, we design a data-augmented, fairness
addressed, and uncertainty estimated novel framework. As parts of the
framework, we propose Bidirectional Quaternion-Quasi-LSTM layers to balance
effectiveness and efficiency. To build a generalized model, we combine five
datasets collected from three platforms. Experiment results show that our model
outperforms eight state-of-the-art methods under both no attack scenario and
various attack scenarios, indicating the effectiveness and robustness of our
model. We share our code along with combined dataset for better future research
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