MODELING THE SPREAD OF FAKE NEWS ON SOCIAL NETWORKING SITES USING THE SYSTEM DYNAMICS APPROACH

The problem of false news online has continued to worsen, especially after witnessing significant events around the world unfold, such as the 2018 Cambridge Analytica scandal, COVID-19 pandemic, to the 2021 January 6th Insurrection at the US Capitol. False information online has distorted online users’ perception of the real world. As daily life is more intertwined with the digital world, false news becomes a more urgent concern because of the way it can shape public opinion. This study presents a rumor propagation model, which was based on epidemiological models, to address the spread of false news on social networking sites. The existing model was expanded on the STELLA software to consider the cognitive process of users when encountering false news, the platform in which the false news spreads, and the relationship of false news with online users. Simulations showed that Confirmation Bias, Sharing of Posts, and Algorithmic Ranking were the three critical variables of the model. It was found that possible interventions include a mix of reducing the bias of users at a wide-scale level and restructuring the SNS algorithm.