Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213-3890, USA.
Department of Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213-3890, USA.
Sci Rep. 2021 Mar 25;11(1):6908. doi: 10.1038/s41598-021-85608-5.
This work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user-user and user-media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user-user and user-media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user-user and user-media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.
这项工作研究了在不依赖真实标签的情况下，对在线社交媒体网络中的错误信息进行分类的能力。这项工作没有将分类问题视为人类或机器学习算法的任务，而是利用用户-用户和用户-媒体（即媒体点赞）之间的交互来推断正在传播的信息类型（虚假与真实），而无需了解信息本身的实际细节。为了研究用户-用户和用户-媒体交互随时间的起源和演变，我们创建了一个实验平台，模拟真实世界社交媒体网络的功能。我们开发了一个图形模型，考虑网络拓扑结构的演变，以模拟虚假和真实媒体在网络中传播时不确定性（熵）的传播。创建真实的社交媒体网络使我们能够测试与用户、他们与其他用户的交互以及与媒体内容相关的各种假设。发现用户-用户和用户-媒体交互的熵近似于虚假和真实媒体点赞，这使我们能够以无监督学习的方式对虚假媒体进行分类。
