Modeling and Mitigating Online Misinformation: a Suggested Blockchain Approach

Abstract

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.