Misinformation detection in Online Social Networks has recently become a critical topic due to its important role in restraining misinformation. Recent studies have showed that machine learning methods can be used to detect misinformation/fake news/rumors by detecting user’s behaviour. However, we can not implement this strategy for all users on a social network due to the limitation of budget. Therefore, it is critical to optimize the monitor/sensor placement to effectively detect misinformation. In this paper, we investigate Minimum Budget for Misinformation Detection problem which aims to find the smallest set of nodes to place monitors in a social network so that detection function is at least a given threshold. Beside showing the inapproximability of the problem under the well-known Independent Cascade diffusion model, we then propose three approximation algorithms including: Greedy, Sampling-based Misinformation Detection and Importance Sampling-based Misinformation Detection. Greedy is a deterministic approximation algorithm which utilizes the properties of monotone and submodular of the detection function. The rest is two randomized algorithms with provable guarantees based on developing two novel techniques (1) estimating detection function by using the concepts of influence sample and importance influence sample with proof of correctness, and (2) an algorithmic framework to select the solution with theoretical analysis. Experiments on real social networks show the effectiveness and scalability of our algorithms.

由于其在抑制错误信息中的重要作用，在线社交网络中的错误信息检测最近已成为一个关键主题。最近的研究表明，机器学习方法可以通过检测用户的行为来检测错误信息/虚假新闻/谣言。但是，由于预算有限，我们无法为社交网络上的所有用户实施此策略。因此，优化监视器/传感器的位置以有效检测错误信息至关重要。在本文中，我们研究了错误信息检测的最低预算该问题旨在找到最小的节点集以将监视器放置在社交网络中，以使检测功能至少是给定的阈值。除了在众所周知的独立级联扩散模型下显示问题的不可逼近性之外，我们还提出了三种近似算法，包括：贪婪，基于采样的错误信息检测和基于重要性采样的错误信息检测。贪婪是一种确定性近似算法，它利用了检测函数的单调和子模的性质。剩下的是在开发两种新技术的基础上，两个具有可证明保证的随机算法（1）使用影响样本和重要性影响样本的概念通过正确性证明估计检测函数，（2）通过理论分析选择解决方案的算法框架。在真实社交网络上的实验证明了我们算法的有效性和可扩展性。