Summary of Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detection, by Ahmed Haj Ahmed et al.
Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detection
by Ahmed Haj Ahmed, Rui-Jie Yew, Xerxes Minocher, Suresh Venkatasubramanian
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 The proposed research investigates the challenges of detecting hate speech in Levantine Arabic, an underrepresented dialect. Current datasets used in hate speech detection are limited and biased towards dominant dialects, posing cultural, ethical, and linguistic hurdles. The study highlights the scarcity of diverse datasets and critiques existing resources for their dialectal bias. To address these limitations, the authors argue for a more nuanced and inclusive approach to natural language processing (NLP) that takes into account the complex sociopolitical and linguistic landscape of Levantine Arabic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Social media platforms are crucial to global communication, but they also spread hate speech. Detecting this in underrepresented languages like Levantine Arabic is tricky because it’s different from other languages. This study looks at why current methods for detecting hate speech don’t work well with Levantine Arabic. It finds that there aren’t many good datasets and the ones we do have are biased towards more popular dialects. The authors suggest using natural language processing (NLP) tools that understand the specific culture and context of Levantine Arabic to better detect hate speech. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
