Predicting Positive and Negative Links in Online Social Networks

TL;DR

This paper demonstrates that the signs of links in online social networks with positive and negative relationships can be accurately predicted using generalized models, providing insights into social theories and enabling attitude estimation.

Contribution

It introduces models that accurately predict signed links across diverse social networks and offers insights into the social principles behind link formation.

Findings

High accuracy in predicting link signs across multiple datasets

Models generalize well across different social platforms

Provides social theory insights and practical applications

Abstract

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.