arXiv:2506.21613v3 Announce Type: replace Abstract: Mental health industry faces growing concerns regarding hate speech directed at children's on social media, as exposure to such content can contribute to adverse psychological outcomes during critical stages of development. Current hate speech datasets and detection systems provide limited support for child-focused applications because they are primarily designed for adults and lack dedicated representations of age-specific characteristics associated with hate speech directed at children's. To address this gap, we introduce ChildGuard, a larg
The proliferation of social media and the growing awareness of its negative impacts on youth mental health necessitate specialized tools to combat online harm.
A strategic reader should care about specialized datasets like ChildGuard as they represent a critical step in developing more effective AI systems to protect vulnerable populations online and address evolving forms of digital harm.
The availability of ChildGuard changes the landscape of hate speech detection by providing a dedicated resource for training AI models specifically for child-targeted content, addressing a significant gap in existing systems.
- · Child protection organizations
- · Mental health professionals
- · Social media platforms prioritizing safety
- · AI ethics researchers
- · Perpetrators of child-targeted hate speech
- · Platforms with weak content moderation
Improved detection and removal of child-targeted hate speech on social media platforms.
Potential for new regulations and industry standards emphasizing age-appropriate content moderation and child safety.
Increased public and parental trust in digital platforms, fostering safer online environments for younger users.
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