Скачать или смотреть QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection

ABSTRACT: This work considers the real-time misinformation detection problem on information spreading networks, where our goal is to detect misinformation quickly and accurately in a scalable way. We formulate this problem as a Markov optimal stopping problem that encodes both the cost from detection error and the cost from letting misinformation spread. Our approach combines model-driven and data-driven methods, where the proposed 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 spreading model and an online algorithm that uses the optimal stopping rule 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) and our evaluations with synthetic data show that QuickStop is robust to (offline) learning errors.

SPEAKER: Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. Her research lies in the broad area of applied probability and stochastic systems, with applications in cloud computing, data centers, and privacy-preserving data analytics. From 2016 to 2018, she was a joint postdoctoral research associate in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign and in the School of ECEE at Arizona State University. She received her PhD degree in Electrical Engineering from Arizona State University in 2016 and her Bachelor's degree from the Department of Electronic Engineering at Tsinghua University in 2009. Her dissertation received the Dean’s Dissertation Award in the Ira A. Fulton Schools of Engineering at Arizona State University in 2016. She received the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS 2016.