Modern social media platforms play an important role in facilitating rapid
dissemination of information through their massive user networks. Fake news,
misinformation, and unverifiable facts on social media platforms propagate
disharmony and affect society. In this paper, we consider the problem of online
auditing of information flow/propagation with the goal of classifying news
items as fake or genuine. Specifically, driven by experiential studies on
real-world social media platforms, we propose a probabilistic Markovian
information spread model over networks modeled by graphs. We then formulate our
inference task as a certain sequential detection problem with the goal of
minimizing the combination of the error probability and the time it takes to
achieve correct decision. For this model, we find the optimal detection
algorithm minimizing the aforementioned risk and prove several statistical
guarantees. We then test our algorithm over real-world datasets. To that end,
we first construct an offline algorithm for learning the probabilistic
information spreading model, and then apply our optimal detection algorithm.
Experimental study show that our algorithm outperforms state-of-the-art
misinformation detection algorithms in terms of accuracy and detection time.

社交媒体平台在信息传播中起着重要作用，本研究旨在通过识别和分类新闻，解决社交媒体平台上虚假消息和不可验证事实的传播问题，提出了基于图模型的概率马尔可夫信息传播模型，并通过实验研究证明了其在准确性和检测时间方面优于现有的虚假信息检测算法。