@inproceedings{871fce5416664511b726e9d38bf50d0b,
title = "Characterizing Efficient Referrals in Social Networks",
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.",
keywords = "filter bubble, social networks, web mining",
author = "Reut Apel and Elad Yom-Tov and Moshe Tennenholtz",
note = "Publisher Copyright: {\textcopyright} 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.; 27th International World Wide Web, WWW 2018 ; Conference date: 23-04-2018 Through 27-04-2018",
year = "2018",
month = apr,
day = "23",
doi = "https://doi.org/10.1145/3184558.3186910",
language = "الإنجليزيّة",
series = "The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018",
pages = "23--24",
booktitle = "The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018",
}