Predictive Modeling of Opinion and Connectivity Dynamics in Social Networks

Social networks have been extensively studied in recent years with the aim of understanding how the connectivity of different societies and their subgroups influences the spread of inno-vations and opinions through human networks. Using data collected from real-world social networks, researchers are able to gain a better understanding of the dynamics of such networks and subsequently model the changes that occur in these networks over time. In our work, we use data from the Social Evolution dataset of the MIT Human Dynamics Lab to develop a data-driven model capable of predicting the trends and long term changes observed in a real-world social network. We demonstrate the effectiveness of the model by predicting changes in both opinion spread and connectivity that reflect the changes observed in our dataset. After validating the model, we use it to understand how different types of social networks behave over time by varying the conditions governing the change of opinions and connectivity. We conclude with a study of opinion propagation under different conditions in which we use the structure and opinion distribution of various networks to identify sets of agents capable of propagating their opinion throughout an entire network. Our results demonstrate the effectiveness of the proposed modeling approach in predicting the future state of social networks and provide further insight into the dynamics of interactions between agents in real-world social networks.