StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms
On the map: LLM safety and security → Safety alignment, refusal and guardrails
Abstract (arXiv)
The mental health of social media users has started more and more to be put at risk by harmful, hateful, and offensive content. In this paper, we propose textscStopHC, a harmful content detection and mitigation architecture for social media platforms. Our aim with textscStopHC is to create more secure online environments. Our solution contains two modules, one that employs deep neural network architecture for harmful content detection, and one that uses a network immunization algorithm to block toxic nodes and stop the spread of harmful content. The efficacy of our solution is demonstrated by experiments conducted on two real-world datasets.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 14 of its 36 references that are in the map. First posted on arXiv 9 Nov 2024.
Builds on
14 of 36 references.
References · 14
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- 2023MisRoBAERTa: Transformers versus Misinformationcited by 4
2 more
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.