How to Mitigate Disagreement and Polarization in Opinion Formation Processes on Social Networks

In recent years, we have been observing an important increase of the phenomena of disagreement and polarization in our society. Among the factors that have been recognized as causes of these social processes, there are the algorithms adopted by the social media platforms to select news and messages to present to their users that are designed to increase their social engagement. Since these phenomena may have very dangerous and destructive effects in terms of social cohesion, it is of great interest to design methods that can be adopted by social media platforms in order to mitigate disagreement and polarization in the process of opinion formation. In this work, we propose mitigation methods based on seeding. Seeding is largely used in viral marketing and opinion diffusion campaigns, and it consists of injecting information into the network by some influential nodes called seeds. We propose using information campaigns starting from seeds that were opportunely chosen to mitigate disagreement and polarization in the network. We consider two different scenarios: in the first one we assume that the whole graph of the social network is known and we present an efficient greedy-based heuristics to select a given number of seeds in order to minimize disagreement and polarization; in the second case, we assume that the social graph is unknown and we present an online learning algorithm that can be used to learn the graph while the opinion diffusion dynamics is run. Finally, in order to evaluate and demonstrate the functionality of our framework, we present some experimental results on the performance of our algorithms on a comprehensive collection of synthetic and real-world networks.