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 misinformation detection which classify news items as fake or real. Specifically, driven by experiential studies on real-world social media platforms, we propose a probabilistic Markovian information spread model over networks modeled by graphs.
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13 Views- Read more about Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs
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This paper focuses on the problem of distributed composite
hypothesis testing in a network of sparsely interconnected
agents, in which only a small section of the field modeling
parametric alternatives is observable at each agent. A recursive
generalized likelihood ratio test (GLRT) type algorithm
in a distributed setup of the consensus-plus-innovations form
is proposed, in which the agents update their parameter estimates
and decision statistics by simultaneously processing
the latest sensed information (innovations) and information
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