Analysis of Echo Chamber Formation by Friend Recommendation

Abstract

In this paper, we analyze how friend recommendation algorithms on social networks promote echo chambers. We analyze both link bias and content bias using a real social graph from X (Twitter). We extract a follow graph from X, repeatedly add new edges selected by a recommendation algorithm, and observe how the degree of bias in the graph changes. Our findings include: (1) the follow graph of X is sufficiently homophilic for recommendation algorithms to produce link bias, (2) iterated recommendations do not accelerate increase of content bias, (3) even when an algorithm recommends no user from the target user’s community, it sometimes produces link bias by recommending users from a few other communities, (4) but no similar phenomenon is observed for content bias.