arXiv:2607.00143v1 Announce Type: new Abstract: Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing. Societies grappling with this issue, particularly when hate speech targets specific groups based on religion, race, ethnicity, culture, nationality, or migration status, face the challenge of balancing freedom of expression with the need for effective content moderation on widely used online platforms. In response to this challenge, we introduce a comprehensive hate speech dataset coverin
The rise in online hate speech globally necessitates better moderation tools, particularly in non-English languages where existing solutions may be lacking.
Improved capabilities for detecting hate speech in critical languages like Turkish and Arabic will assist in content moderation, potentially reducing real-world violence and platform liability.
The availability of comprehensive datasets and models for hate speech detection in Turkish and Arabic could lead to more effective content moderation policies and tools in these regions.
- · Social media platforms
- · NLP researchers
- · Content moderation companies
- · Minority groups
- · Hate speech promulgators
More accurate hate speech detection in Turkish and Arabic languages due to a new comprehensive dataset.
Reduced spread of harmful content on platforms widely used in Arabic and Turkish speaking regions, potentially leading to safer online environments.
Enhanced trust in online platforms in these regions, impacting user engagement and potentially influencing geopolitical discourse.
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Read at arXiv cs.CL