Abstract
Social networks shape perceptions by exposing people to the actions and opinions of their peers. However, the perceived popularity of a trait or an opinion may be very different from its actual popularity. We attribute this perception bias to friendship paradox and identify conditions under which it appears. We validate the findings empirically using Twitter data. Within posts made by users in our sample, we identify topics that appear more often within users’ social feeds than they do globally among all posts. We also present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic’s global prevalence from biased individual perceptions. We characterize the polling estimate and validate it through synthetic polling experiments on Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort perceptions and presents approaches to mitigate this bias.
Highlights
Social networks shape perceptions by exposing people to the actions and opinions of their peers
Consequences of friendship paradox can skew how we compare ourselves to friends: people tend to be less happy than their friends are[9], and researchers tend to have less impact than their co-authors do[10], on average
We carry out the analysis to show that while two variants of the friendship paradox occur in any directed network[16], the remaining two exist only if an individual’s in-degree and out-degree are correlated
Summary
Social networks shape perceptions by exposing people to the actions and opinions of their peers. The asymmetric nature of links in directed networks leads to four variants of the friendship paradox[14]: your friends (or followers) have more friends (or followers) than you do, on average This effect can be quite large, with upwards of 90% of social media users observing that they have a lower indegree and out-degree than both their friends and followers[15]. We identify a new paradox in directed networks, as a result of which a trait will appear to be significantly more popular locally among an individual’s friends, than it is globally among all people. We show that this effect is stronger in networks where higher out-degree nodes with the trait are connected to nodes with a lower indegree
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