Nowadays social media play an important role in our societies. In fact, they remain one of the most commonly used means to easily obtain information online. However, social media can also be a catalyst for the proliferation of fake news around the world, especially in times of crisis where massive misinformation can have serious consequences. In particular, fake news about the COVID'19, as the virus spreads, leads to an infodemic of misinformation, hence the need for news verification mechanisms in social media. In this paper, we approach the problem of fake news related to COVID'19 as a global pandemic with a significant impact on various sectors. We also provide an aggregation system to detect and analyze fake news related to the COVID'19 pandemic in the Moroccan context based on data sets scrapped from Facebook.

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Typically, conformance testing consists of placing a set of parallel testers at each port of an implementation to ensure its conformance to the specifification. However, a number of common fault detections occur if no coordination is made between these parallel testers and the implementation under test (IUT). Therefore, the test process must support mechanisms of coordination between these distributed components, particularly for implementation with stochastic behaviour. To this end, as well as to analyse the stochastic behaviour of the implementation under test, we propose in this paper an algorithm to generate for each tester a probabilistic local test sequence (PLTS) aiming to avoid both synchronization and observation issues. Finally, we suggest a new architecture based on Markov decision processes with an adaptive controller to control and optimize the whole testing process.

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