Characterizing Efficient Referrals in Social Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
Pages23-24
Number of pages2
ISBN (Electronic)9781450356404
DOIs
StatePublished - 23 Apr 2018
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: 23 Apr 201827 Apr 2018

Publication series

NameThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
Country/TerritoryFrance
CityLyon
Period23/04/1827/04/18

Keywords

  • filter bubble
  • social networks
  • web mining

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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