Exploring systemic effects of the friendship paradox on social network participation

The friendship paradox states that, on average, people have fewer friends than their friends do. This can lead to social perception biases, such as the belief that one’s friends are more socially engaged than oneself. In a recent paper, we investigated the consequences of this type of social comparison for content sharing behavior in online social networks (Medhat and Iyer, The Friendship paradox and social network participation, international workshop on complex networks and their applications, pp 301–315, 2023). We simulated a scenario where people compare the feedback that their own content receives to the feedback that their friends’ content receives and adjust their sharing based on the comparison. If sharing and feedback rates are initially uniform over individuals, then feedback disparities initially depend solely upon the local structural friending paradox. These structurally induced disparities may then induce sharing disparities that further amplify (or potentially, reduce) feedback disparities, triggering cycles of updates of sharing rates and feedback disparities. In our previous work, we observed that monotonic responses to social comparisons, where larger disparities result in greater behavioral changes, led to an asymptotic decline in overall network sharing. In this paper, we extend our earlier work by studying fundamental properties of our friendship-paradox-induced sharing trajectories (FIT) method. We analyze how sharing rates trend when it is dictated by both observed feedback disparities and activity thresholds for collective behavior. We also evaluate the iterative impact of FIT on network nodes, as a measure of node centrality in a network, finding it to have the ability to identify central nodes at a community level and to do so at a lower computational complexity than community detection, a property not matched by commonly used node centrality methods such as betweeness, closeness and degree centrality.