Summary of Cross-platform Hate Speech Detection with Weakly Supervised Causal Disentanglement, by Paras Sheth et al.
Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement
by Paras Sheth, Tharindu Kumarage, Raha Moraffah, Aman Chadha, Huan Liu
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes a novel framework for detecting hate speech on social media, called HATE WATCH, which can work across different platforms without requiring explicit target labeling. The existing deep learning approaches have limitations in adapting to the evolving nature of hate speech and platform-specific peculiarities. To address this challenge, the authors utilize causality-inspired disentanglement and combine it with confidence-based reweighting and contrastive regularization techniques. This approach enables the separation of universal hate indicators from platform-specific features, leading to superior performance in detecting hate speech. The study demonstrates the effectiveness of HATE WATCH on two platforms with target labels and two without, providing a promising solution for scalable content moderation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to develop a way to detect hate speech on social media that works across different platforms. Hate speech is a big problem because it can spread quickly online, but we also want to promote global connectivity. The current approaches have some limitations, so the authors came up with a new method called HATE WATCH. This method uses special techniques to separate out the things that are specific to each platform from the things that are universal, like hate speech itself. It works better than other methods and could help us create safer online communities. |
Keywords
» Artificial intelligence » Deep learning » Regularization
