Online Auditing of Information Flow
Mor Oren-Loberman Vered Azar Wasim Huleihel
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.