QuickStop：一种最快错误信息检测的马尔可夫最优停止方法
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
摘要：
本文结合数据驱动和模型驱动的方法进行实时错误信息检测。我们的算法，名为QuickStop，是一个最佳的停止算法的基础上获得的标记数据的概率信息传播模型。该算法由离线机器学习算法学习概率信息传播模型和在线最优停止算法检测错误信息。在线检测算法具有较低的计算复杂度和存储复杂度。我们对真实世界数据集的数值评估表明，QuickStop在准确性和检测时间（检测所需的观察次数）方面优于现有的错误信息检测算法。我们对合成数据的评估进一步表明，QuickStop对（离线）学习错误具有鲁棒性。
英文摘要：
This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named QuickStop, is an optimal stopping algorithm based on a probabilistic information spreading model obtained from labeled data. The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation. The online detection algorithm has both low computational and memory complexities. Our numerical evaluations with a real-world dataset show that QuickStop outperforms existing misinformation detection algorithms in terms of both accuracy and detection time (number of observations needed for detection). Our evaluations with synthetic data further show that QuickStop is robust to (offline) learning errors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
