  Misinformation propagation in online social networks has become an
increasingly challenging problem. Although many studies exist to solve the
problem computationally, a permanent and robust solution is yet to be
discovered. In this study, we propose and demonstrate the effectiveness of a
blockchain-machine learning hybrid approach for addressing the issue of
misinformation in a crowdsourced environment. First, we motivate the use of
blockchain for this problem by finding the crucial parts contributing to the
dissemination of misinformation and how blockchain can be useful, respectively.
Second, we propose a method that combines the wisdom of the crowd with a
behavioral classifier to classify the news stories in terms of their
truthfulness while reducing the effects of the actions performed by malicious
users. We conduct experiments and simulations under different scenarios and
attacks to assess the performance of this approach. Finally, we provide a case
study involving a comparison with an existing approach using Twitter Birdwatch
data. Our results suggest that this solution holds promise and warrants further
investigation.
