StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms

TL;DR

StopHC is a comprehensive architecture combining deep learning and network immunization to detect and prevent the spread of harmful content on social media, aiming to improve online safety.

Contribution

It introduces a novel architecture integrating harmful content detection with network immunization to mitigate toxicity in social media environments.

Findings

Effective detection of harmful content demonstrated on real-world datasets

Network immunization successfully blocks toxic nodes and curtails content spread

Improves safety and security of social media platforms

Abstract

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.