Malicious behavior in social media has many faces, which for instance appear in the form of bots, sock puppets, creation and dissemination of fake news, Sybil attacks, and actors hiding behind multiple identities. In this paper, we propose the NetDER architecture, which takes its name from its two main modules: Net work D iffusion and ontological reasoning based on E xistential R ules), to address these issues. This initial proposal is meant to serve as a roadmap for research and development of tools to attack malicious behavior in social media, guiding the implementation of software in this domain, instead of a specific solution. Our working hypothesis is that these problems – and many others – can be effectively tackled by (i) combining multiple data sources that are constantly being updated, (ii) maintaining a knowledge base using logic-based formalisms capable of value invention to support generating hypotheses based on available data, and (iii) maintaining a related knowledge base with information regarding how actors are connected, and how information flows across their network. We show how these three basic tenets give rise to a general model that has the further capability of addressing multiple problems at once.

社交媒体中的恶意行为有很多面孔，例如以僵尸程序，木偶，伪造新闻的创建和传播，Sybil攻击以及隐藏多种身份的演员的形式出现。在本文中，我们提出了NetDER架构，它的名字来源于它的两个主要模块：网络工作d iffusion和本体推理基于Ë xistential [Rules）来解决这些问题。最初的建议旨在作为研究和开发工具来攻击社交媒体中恶意行为的路线图，从而指导该领域中软件的实施，而不是特定的解决方案。我们的工作假设是，可以通过（i）合并不断更新的多个数据源，（ii）使用能够对发明进行估价的基于逻辑的形式主义来维护知识库，从而有效地解决这些问题以及许多其他问题。支持基于可用数据生成假设，以及（iii）维护有关知识库，其中包含有关参与者如何连接以及信息如何在其网络中流动的信息。我们将展示这三个基本原则如何形成一个通用模型，该模型具有同时解决多个问题的进一步功能。