STopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms
Ciprian‐Octavian Truică, Ana-Teodora Constantinescu, Elena‐Simona Apostol
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 STOPHC, a harmful content detection and mitigation architecture for social media platforms. Our aim with 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.
Topics & Concepts
Computer scienceArchitectureSocial mediaContent (measure theory)Computer securityWorld Wide WebMathematicsVisual artsArtMathematical analysisHate Speech and Cyberbullying DetectionAdvanced Malware Detection TechniquesInternet Traffic Analysis and Secure E-voting