Summary of An Effective, Robust and Fairness-aware Hate Speech Detection Framework, by Guanyi Mou et al.
An Effective, Robust and Fairness-aware Hate Speech Detection Framework
by Guanyi Mou, Kyumin Lee
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel framework proposes a data-augmented, fairness-addressed, and uncertainty-estimated approach to hate speech detection in online social networks. The framework incorporates Bidirectional Quaternion-Quasi-LSTM layers to balance effectiveness and efficiency. Five datasets from three platforms are combined to build a generalized model that outperforms eight state-of-the-art methods under various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hate speech is spreading rapidly online, causing harm and damage. Existing detection methods have limitations, including data insufficiency, uncertainty estimation, and unfair bias. A new framework is needed for accurate, robust, and fair hate speech classification. This framework combines five datasets from three platforms to build a generalized model that outperforms eight state-of-the-art methods. |
Keywords
» Artificial intelligence » Classification » Lstm
