StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms
This addresses mental health risks for social media users by providing a detection and mitigation system, but it appears incremental as it combines existing methods like deep learning and network algorithms.
The paper tackles the problem of harmful content on social media by proposing StopHC, an architecture that detects such content using deep neural networks and mitigates its spread via network immunization, with experiments on two real-world datasets showing efficacy.
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.