Characterizing Efficient Referrals in Social Networks

TL;DR

This paper analyzes network referrals in social networks, classifies their efficiency, and demonstrates that using user experience and message content can predict successful referrals, aiming to reduce the filter bubble effect.

Contribution

It introduces a method to predict effective referrals based on user experience and message features, helping to mitigate filter bubbles in social networks.

Findings

Achieved an AUC of 0.87 in predicting effective referrals.

Identified features that correlate with referral success.

First step towards algorithmic construction of efficient referrals.

Abstract

Users of social networks often focus on specific areas of that network, leading to the well-known "filter bubble" effect. Connecting people to a new area of the network in a way that will cause them to become active in that area could help alleviate this effect and improve social welfare. Here we present preliminary analysis of network referrals, that is, attempts by users to connect peers to other areas of the network. We classify these referrals by their efficiency, i.e., the likelihood that a referral will result in a user becoming active in the new area of the network. We show that by using features describing past experience of the referring author and the content of their messages we are able to predict whether referral will be effective, reaching an AUC of 0.87 for those users most experienced in writing efficient referrals. Our results represent a first step towards algorithmically constructing efficient referrals with the goal of mitigating the "filter bubble" effect pervasive in on line social networks.