Analysis of Echo Chamber Formation by Friend Recommendation
by Masafumi Iwanaga, Keishi Tajima
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.
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