In today’s interconnected world, online social networks play a pivotal role in facilitating global communication. These platforms often host discussions on contentious topics such as climate change, vaccines, and war, leading to the formation of two distinct groups: deniers and believers. Understanding the characteristics of these groups is crucial for predicting information flow and managing the diffusion of information. Moreover, such understanding can enhance machine learning algorithms designed to automatically detect these groups, thereby contributing to the development of strategies to curb the spread of disinformation, including fake news and rumors. In this study, we employ social network analysis measures to extract the characteristics of these groups, conducting experiments on three large-scale datasets of over 22 million tweets. Our findings indicate that, based on network science
measures, the denier (anti) group exhibits greater coherence than the believer (pro)
group.