提出一套检测并阻断社交平台有害内容的系统架构
StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms
- 用深度神经网络识别有害内容
- 通过网络免疫算法阻断毒性节点传播
- 在两个真实数据集上验证有效性
社交媒体用户的心理健康正日益受到有害、仇恨和冒犯性内容的威胁。本文提出 extsc{StopHC}，一种面向社交平台的有害内容检测与缓解架构。该系统包含两个模块：一个采用深度神经网络架构用于有害内容检测；另一个使用网络免疫算法，识别并阻断有毒节点，阻止有害内容扩散。实验在两个真实世界数据集上进行，验证了该方案的有效性。
原文摘要 · Abstract (English)
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.
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