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 \textsc{StopHC}, a harmful content detection and mitigation architecture for social media platforms. Our aim with \textsc{StopHC} 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.
@article{arxiv.2411.06138,
title = {StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms},
author = {Ciprian-Octavian Truică and Ana-Teodora Constantinescu and Elena-Simona Apostol},
journal= {arXiv preprint arXiv:2411.06138},
year = {2024}
}