QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
This work addresses the problem of quickly detecting misinformation in online platforms, which is incremental as it builds on existing detection algorithms with improved performance.
The paper tackles real-time misinformation detection by combining data-driven and model-driven methods, resulting in QuickStop, an algorithm that outperforms existing methods in accuracy and detection time, as shown by evaluations with a real-world dataset.
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.