@inproceedings{d8ebdb015f3d4df5a31c734e61147a71,
title = "QuickStop: A Markov optimal stopping approach for quickest misinformation detection",
abstract = "This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named Quick- Stop, 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.",
keywords = "Fake news, Misinformation detection, Quickest detection, Social networks",
author = "Honghao Wei and Xiaohan Kang and Weina Wang and Lei Ying",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019 ; Conference date: 24-06-2019 Through 28-06-2019",
year = "2019",
month = jun,
day = "20",
doi = "10.1145/3309697.3331513",
language = "English (US)",
series = "SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "79--80",
booktitle = "SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems",
}