Modeling Rumor Spread and Influencer Impact on Social Networks

Social networks act as an indispensable component in the lives of individuals. However, misinformation and fake news are critical challenges in the digital world as people get persuaded towards false information. Though several fake news detection algorithms emerged, epidemic modeling is crucial in understanding the dissemination of fake news, which helps the policyholders to adopt control mechanisms to prevent the curb of infection within the networks. We propose a mathematical model of rumor spread by considering the human nature of selection and social influence within social networks by analyzing the stiffness of different global communities. The positivity of the model was mathematically proved, which proves the validity of the model within the real world. Our real-world data analysis showcases the possibility of a significant increase in fake news and misinformation within online digital networks during the COVID-19 pandemic. A comparative study using real-world data by extracting tweets shows that the proposed model outperforms the existing model. The significance of influencers in digital networks in disseminating rumors is discussed using the proposed model. The results can be used to analyze the impact of misinformation in different communities, which can help the policyholders implement necessary intervention mechanisms at the right time.